Advantages and Disadvantages of Cosmetic Surgery Essay

Advantages and Disadvantages of Cosmetic Surgery Essay Introduction Nowadays, many men or women are considering to make themselves looks better so decided to have cosmetic surgery. A lot of cosmetic surgery provider advertising their cosmetic skill and benefit of cosmetic surgery to attract more customer. Cosmetic surgery contain positive and negative morality. Question 1 Teleology theory can be defined as moral consequence position, in which moral behaviour means achieving an ethical consequence without concern over the process by which the end result is achieved. Right conduct with teleology means that which in turn promotes the best consequences or perhaps identifiable good (Craig, 1996). Utilitarianism can say by the phase â€Å"the greatest good for the greatest number† (Mill, 1983). From the view of Chris, Chris decided to have nose surgery is moral because acting accordance with happiness. Nonetheless, Chris are appearing upon a low delight. Even though the surgery will bring Chris several happiness but it is something that become endless pursuit. If there is a propensity for Chris into the future out feeling and looking better, than this surgeon did their own job and there is nothing immoral happening. Where the layaway is acceptable and surgeons do not have permit to practice, in pain and annoyance, there is immoral about the particula r action. After the nose surgery operation, Chris perceive that there was some pain and discomfort after the surgery operation within a couple of week. Besides that, Chris having the second operation to correct a minor. After the operation, Chris satisfy with the result and bring Chris happiness. Based on the Chris’s decision to take action on cosmetic surgery, Chris’s mother was fully disagree Chris to having the nose surgery. Because Chris didn’t know what are the risk will occur during the operation. Chris’s mother keep on recall back the story of famous footballer’s wife which who are the one kept going for cosmetic surgery and passed away because of complication after the operation. From the view of Chris’s mother is based on deontology theory. Deontology define since the rightness as well as wrongness of action by evaluation of particular formal properties of act for instance duty, justice as well as respect a great individual’s autonomy (Stahl, 1996). All of the parents will worry about their child by doing some activities to damage or hurting themselves because of this, parents will have the responsible to avoid the activities happen. Even Chris’s mother also tried to persuade Chris do not have cosmetic surgery i s because Chris’s mother worrying about Chris will have any danger during the operation. In the other way, Kantian ethics believe that consequence could never make right or wrong because an action may bring the happiness but still might be wrong. Because of the prominent nose was bring Chris unhappiness and dissatisfaction so Chris decide to have nose surgery for the beauty and seeking happiness. Question 2 Archie B Carroll said that the definition of Corporate Social Responsibility (CSR) has four type of classification which include economic responsibility, legal responsibility, ethical responsibility and discretionary responsibility (Carroll, 1979). These four type of expectations reflect a view of social responsibility that related to some in the definitions made available earlier although that categorizes the social responsibilities connected businesses within a more inclusive manner. In order to make the Naturea clinic be more profitable, the Naturea clinic have to maintain a strong competitive position in surgery industry. Therefore, the Naturea clinic have to maintain a high level of operational efficiency by providing the best service quality to fulfill customer need and make sure the customer satisfied with the result after the surgery. Naturea clinic employees had been training to help them proceed within a cautions along with measured method, but the employees are being chivv ied with the management team to become more energetic and market-facing to make the clinic more profitable. In addition, the Naturea clinic should perform in a manner consistent with maximize earning per share. Furthermore, Naturea clinic must obey to the law cause of law is the society’s codification of right and wrong. Clinic have to perform in a manner in keeping with the expectations of government along with the law and it is also important to comply with numerous national and supranational laws and regulation. Other than that, Naturea clinic have to meet the minimum legal requirements by providing goods or services. For example, a qualified surgeons provide face or body plastic surgery to satisfy customer need. The rules and regulation for Naturea clinic by not doing illegal activities such as providing fake information about the cosmetics surgery, cheating customer by saying do not have any risk occur during the surgery operation. Ethical responsibility encompass those standard, norms, or expectation that reflect a concern for just what consumers, workers, shareholders and the community consider as fair, just or commensurate with the esteem or protection of stakeholders moral legal rights. The Naturea medical team members complain amongst themselves the company has been hijacked by the team of the managers and the medics feel very pressure to hit the targets pertaining to the number and characteristics of operations are expected to perform. The manager of the Naturea clinic should treat their employees fairly, provide training to surgeons to ensure become a qualified surgeon to satisfy customers and do not give them high pressure when performing their work. Ethical responsibility for Naturea clinic is important to prevent ethical norms from being compromised as a way to achieve corporate objectives. Besides that, ethical responsibility could help clinic to recognize which corporate integrity along with ethical behavior go beyond mere compliance with regulations. Lastly, the ethical responsibility also important to Naturea clinic by recognize and also respect fresh or innovating moral ethical norms used by society. Discretionary responsibility almost like philanthropy initiatives try to be good corporate citizen by doing some community activities, charitable work, donation to good causes and providing day-care centers to create a positive image for company (Blowfield and Murray, 2008). The Naturea clinic should do some charity activities such tend to arrange for teams of volunteer physicians carrying medical supplies to complete the mission. Increasingly, these teams also work to improve medical infrastructures and to train personnel. The manager and employees of Naturea clinic participate in voluntary and charitable activities within their local communities. Cause-related marketing (CRM) occurs when a company forms a strategic relationship with a particular social cause that are mutually advantages to all parties (Kolter and Lee, 2005). CRM are advantage to Naturea clinic such as, increasing the clinic population, increasing the brand awareness and services quality, developing good public rela tions and increasing the customer loyalty. And there are advantages for the social cause by increasing in donations through better exposure. Question 3 For some, plastic surgery is an approach to recover an ordinary appearance after a mishap. For other, plastic surgery is an approach to revise saw corrective blemishes. Despite the purpose behind the plastic surgery, there are serious disadvantages that must be considered before having surgery (Holley, 2013). The first disadvantages of having surgery is medicals risks. Plastic surgery, in the same way as any surgery, convey a danger of medical complications, including death. The absolute most regular dangers connected with plastic surgery are serve bleeding, infection and nerve damage that may lead to dumbness, tissue death, fluid collecting around the site (seroma) and wounding in the range. Blood clusters can profound vein thrombosis are likewise complications of plastic surgery. The second disadvantages of having surgery is about the appearance. Unsightly scarring is also disadvantage of plastic surgery. With these scar, which are called hypertrophic scars, are thick and red. Wounding and swelling for a period of time after the surgery are frequently hard to cover up, particularly when the work was carried out in the facial area. Besides that, Ptosis or drooping and sagging in the surgical area, might be another disadvantage. Furthermore, some of the patients find that the result of the procedure are not what they anticipated. The next disadvantage is cost of the plastic surgery. Numerous plastic surgery procedures are costly. As indicated by American Society for Aesthetic plastic surgery, microdermabrasion was the least expensive procedure which costs an average of $139 per procedure and the most expensive with a costs of $7,976 is a lower body lift. Besides that, there are some fees also will be calculated when considering the costs of plastic surgery which is surgeon fees, hospital fees and anesthesia fees, along with lost wages and medication costs. Cosmetic surgery tourism can be defined as the motion of the patients from one location to another to carry out â€Å"aesthetic† medical procedure, which is the growing area of medical tourism (Reisman, 2010). Some of the activities like sunbathing, drinking alcohol, jet skiing and swimming should be avoided after surgery said by the American Society of Plastic Surgery (ASPS) (ASPS, 2008). The disadvantage of cosmetic surgery tourism is that impossible for the patient to meet up the doctors to operate on the patient before travelling, it is required to have a talk with doctor or surgeon to know detail of the surgery and the effect of travelling for operation. Many patient lack of knowledge about the problem of cosmetic surgery oversea. An accident or bad cosmetic surgery can occur. For example, Elliott (2004) reported that a patient had liposuction on her legs and the surgery so radical that the patient leg badly dimpled and require more fat to fill them out. All cosmetic surgery possible present the dangers to patient or tourist health. Some of patient face extra language and culture barrier that obstruct communication with physicians and nurses (Casanova, 2007). When post-operative individual decide in tourism activities will raise the chances of undesirable results and complications. Question 4 From my opinion, I felt that the advertising of cosmetic surgery by clinics such as Naturea should be banned. As of now, cosmetic surgery supplier are free to promote their services without any restriction on content. The advertisement heedlessly trivialize invasive surgical procedures that consist of inherent heath risk. Every people have their own definition of beauty. Genuine beauty lie in your individuality, if one of the boy or girl consider as ugly but they have a good personality which mean there is still have people find them because they are attractive. But the cosmetic surgery trend has misdirect the people into imaging that looks are generally vital. They may duplicate their idols’ looks so they can like themselves and be acknowledge in general. This would be bad for the society. We should respect our parents because our looks are a present from our parents, if we have cosmetic surgery and changed our looks are considered as cheating. Besides that, some of the adver tisement show that cosmetic surgery do not contain any risk and guarantee that the surgeons is a qualified surgeons and have their own license. All of the advertisement is tried to cheat customer and actually cosmetic surgery involves risk such medical risk. Some of the surgeons might not have a license or unqualified surgeons, if you having cosmetic surgery from the surgeons that do not have license and not a qualified surgeons and the end result is not what you expected, you would not be able to do anything on it. Currently, people have no choice, but still exposed the aggressive marketing plot of some cosmetic clinics, whether in the public, social media such as internet, magazines or advertising on TV. Because of these, the advertising will affect everyone that already considering to have the surgery. Conclusion As a conclusion, cosmetic surgery can satisfy what customer need and also will damage to the customer after the surgery. If cosmetic surgery fulfill the happiness of the customer then the action of the surgeon is moral. And there are four responsibility in corporate social responsibility which include economic, legal, ethical and discretionary responsibility. There is also a disadvantage of having cosmetic surgery and cosmetic surgery tourism. (1946 word) References ASPS, (2008), Warnings of ASPS, [Online] Available from:http://www.plastisurgery.org> [Accessed: 7 April 2015] Archie B. Carroll. (1979). The Academy of Management Review, Vol. 4, No.4 (Oct., 1979), pp. 497-505 Blowfield, M., Murray, A. (2008). Corporate Responsibility: a critical introduction, Blackwell Publishing, Oxford, UK Craig, E. (1998).Encyclopedia of Philosophy. London and New York: Routledge. Casanova, E. (2007) The Whole Package: Exploring Cosmetic Surgery Tourism, [Online] Available from:http://www.allacademicresearch.com> [Accessed: 6 April 2015] Elliott, J. (2004) Do not Rush into Cosmetic Surgery, [Online] Available from:http://www.bbc.co.uk> [Accessed: 6 April 2015] Holley, Casey. ’Disadvantages of Plastic Surgery | LIVESTRONG.COM’. LIVESTRONG.COM. N.p., 2013 [Online] Available from: http://www.livestrong.com/article/23750-disadvantages-plastic-surgery/> [Accessed: 5 April 2015] Kotler, P. Lee, N. (2005). Corporate Social Responsibility: doing the most good for your company and your cause Wiley Publications, New Jersey, USA Mill, John Stuart. The Collected Works of John Stuart Mill. Gen. Ed. John M. Robson. 33vols. Toronto: University of Toronto Press, 1963-91. Reisman, D. (2010) Health Tourism: Social Welfare through International Trade. Cheltenham: Edward Elgar. Stahl DA. Ethics in subacute care—part1. Nurs Manage. 1996;27(9):29-30. Thomas MCElhenney â€Å"The Ethical Issues behind Cosmetic Plastic Surgery. [Online] Available from :http://parenethical.com/phil140sp11/2011/03/07/thomas-mcelhenney-the-ethical-issues-behind-cosmetic-plastic-surgery-and-how-js-mill-and-immanuel-kant-would-view-it/> [Accessed: 8 April 2015]

Medias Influence Essay -- The Role and Influence of Mass Media

You do it without even knowing. Everyday you are influenced by the media, when you wake up, one of the first things you will do, and I speak from experience because I do it my self, you will look at your mobile phone, turn the TV on, turn the sleeper timer on your radio or check your emails. It doesn't seem like much but already straight away in your lives you're being influenced. After that it doesn't stop. you go to the kitchen and get some food, and I can almost be 100% sure when I say that there will be a product in that kitchen of yours that has been or is endorsed by a well known celebrity. Which again just shows another way in which the media influence us? I carried out a bit of background research before I did this essay, and this statement is backed up by what I found. I found that 87% of people I asked have brought something because of celebrity endorsement. 100% of people owned some products that had been endorsed, be it makeup, cooking equipment, shoes/clothes whatever. Of all these people they said that t...

Abraham Lincoln And Jefferson Davis Essay -- Compare Contrast American

Abraham Lincoln and Jefferson Davis Works Cited Missing   Ã‚  Ã‚  Ã‚  Ã‚  In this report I compare two great historical figures: Abraham Lincoln, the 16th president, steered the Union to victory in the American Civil War and abolished slavery, and the first and only president of the Confederate States of America, Jefferson Davis. Abraham Lincoln was the President of the Union, and Jefferson Davis struggled to lead the Confederacy to independence in the U.S. Civil War. Lincoln was treasured by the African Americans and was considered an earthly incarnation of the Savior of mankind (DeGregorio 20-25). On the other hand, Davis was both admired and hated. Lincoln had a different view of how the U.S. should be in abolishing slavery. Davis was a politician, president of the Confederate States of America, and also a successful planter. He had beliefs for the South to continue in the old ways with slavery and plantations. Both Lincoln and Davis had strong feelings for the protection of their land (Arnold 55-57).   Ã‚  Ã‚  Ã‚  Ã‚  Both Abraham and Jefferson Davis shared several differences and similarities. Lincoln was known to have an easy going and joking type attitude. In contrast, Davis had a temper such that when challenged, he simply could not back down (DeGregorio 89). Davis had been a fire-eater before Abraham Lincoln's election, but the prospect of Civil War made him gloomy and depressed. Fifty- three years old in 1861, he suffered from a variety of ailments such as fever, neuralgia, and inflamed eye, poor digestion, insomnia, and stress. Lincoln also suffered from illnesses during the war. He had severe cases of headaches and stress.   Ã‚  Ã‚  Ã‚  Ã‚  Both presidents had a lot of pressure of them due to the fact of defending their region. Lincoln had difficulties growing up because of the deaths early in his childhood, poverty, and little education. Davis; however, studied at a Roman Catholic school in Kentucky and at Transylvania University, and entered West Point in 1824. Davis seemed to have had an outreaching environment to his success. The major difference, personality wise, was Davis's weakness in his inability to get along with other people where Lincoln was a well liked and easygoing man.   Ã‚  Ã‚  Ã‚  Ã‚  Both men shared a common bond in their education towards war. Davis served at frontier military posts and in the Black Hawk War before resigning in 1835. L... ...sident, we prospered in many ways. His actions brought about great challenges that he, and the following fathers of our country would go through. The combining of both races and more freedoms would stir troubles for many years to come. Although his actions stopped slavery, it didn't stop the harsh treatment of the black race which carried out for decades. Lincoln's Presidency was dominated by the war. The country was going through major changes socially and economically. Though Davis led the South through dramatic changes, no change has ever had more importance than the abolishment of slavery. Both the North and the South gained knowledge from this horrid experience of war. Lincoln's achievements—saving the Union and freeing the slaves—and his martyrdom just at the war's end assured his continuing fame. Both men have made their historical marks due to their great deeds, and ways they both sacrificed their devotion to shape our country. I agree with most historians that Lincoln had qualities that made him a great American statesman. His keen intellect, humor and boldness, and compassion all contributed to his presidency in unifying the nation and extending freedom.

The Lottery: Examples Of Literary Terms, And A Journal Entry Essay

A. 1) The tone, mood or atmosphere in the story begins with that of happiness and euphoria, by setting us up with a wonderful day that most everyone would enjoy. (Quote: â€Å"The morning of June 27th was clear and sunny, with the fresh warmth of a full-summer day†¦Ã¢â‚¬ [pg147] ) However, later on in the story, it takes a different tone, and by the very end the tone is that of panic, disdain and fear. (Quote: â€Å"‘It isn’t fair, it isn’t right,’ Mrs. Hutchinson screamed, and then they were upon her.† [pg 155] ) 2) The conflict in this story is person versus environment (society) And to a lesser extent, person versus person. (Quotes: â€Å"I tell you it wasn’t fair, you didn’t give him time enough to chose. Everybody saw that.† [pg154] â€Å"‘Be a good sport, Tessie,’ Mrs. Delecroix called, and Mrs’ Graves said, â€Å"‘All of us took the same chance.’†[pg153] ) 3) The point of view is semi-omniscient. 4) One example of foreshadowing in this story is when Tessie is late to the draw and everyone is picking on her, this shows you that she is more of a main character and that she is going to come up later in the story. (Quote: â€Å"She came hurriedly into the square†¦Ã¢â‚¬ËœClean forgot what day it was’† [pg150] ) Another example is when she gets picked the for the first draw, this again shows us that she is going to come up later in the story again with an important part. And it also re-assures us that the previous foreshadowing was true. (Quote: â€Å"People began to look around to see the Hutchinsons. Bill Hutchinson...

Report on Telstra Marketing Strategies Essay

AbstractMarketing strategies are the main drives for promoting a company’s products to increase its sales and to build its brand. With regards to competition, a company needs to strengthen their marketing strategies to stay competitive. With the intense competition in the telecommunication industry, marketing is an important element in Telstra to build its presence in the community. This report seeks to analyse Telstra’s marketing strategies and identify critiques help to point out the areas of development for Telstra. IntroductionFormed in 1901, Telstra is Australia’s oldest and largest telecommunication service provider, with a market share of 74.5%. Telstra was partially privatized by the Commonwealth Government in two public offerings in 1997 and 1999. Due to government deregulations September 2002, Telstra has begun to open itself to more competition and the company’s business has turned to be more transparent. Its current principle activities include a wide array of telecommunication and information products and services. As Telstra services a wide demographic of customers, their products are segmented according to different target markets. Market SegmentationUnder the management of its new CEO, Sol Trujillo, Telstra is aimed towards understanding and satisfying its customer needs (Shanahan 2006). New market research is to be conducted to satisfy different customer segments who had demonstrated different needs from the broader telecommunications industry. He mentions that, ‘when the market research had been completed, Telstra would have interviewed over 90,000 consumer customers and created a small business ‘panel’ of 16,000 businesses’. This thorough market research is being conducted as it was shown that there is a reducing market in the mobile segment (Corner, 2006). Marketing Strategy AnalysisAs Telstra is involved in the telecommunications and information services industry, the marketing strategies will be divided and analysed according to their respective industries. Telecommunication IndustryProductTelstra provides various products and services in the telecommunications industry. Among them are landlines, mobile phones and the internet. LandlinesTelstra owns and manages most public switched telephone network (PSTN) services. Most of Telstra’s revenues are generated from fixed-line services. However, its revenue from its fixed-line services has been declining due to the reduced number of local calls made. Among the fixed line products and services offered by Telstra are the business line plans, fixed and rental phones. Mobile PhonesTelstra has two core services in mobile phone communication – post-paid and pre-paid mobile phone plans. The company provides products such as mobile phones. As these mobile phone sales are a relatively small part of the total revenue these phones are to be viewed as a means to sell more plans. As Telstra emphasises on providing the latest technology in the industry, it has recently introduced its Next G network, which claims to be the largest, fastest mobile broadband network. Their mobile phone plans work as a base for beneficial segmentation. The casual plan allows for more flexibility for change, while the phone plans offers the added benefit of including a handset. InternetTelstra has a wide variety of Internet services, which is offered to a wide demographic of customers. Among the products offered are: dial-up, cable internet, ADSL, wireless, and satellite. Telstra brands its Internet service as Bigpond. With Bigpond Broadband, the internet is provided at a blisteringly fast speed. For more traditional Internet services, Telstra also offers dial-up and ISDN services. With these services, Telstra has segmented each service through pre-paid and business group plans. Its ISDN service is targeted to people with limited broadband availability. CritiquesTelstra seeks to increase its customer experience and satisfaction through reward programs and product bundling. Telstra has designed telecommunication plans that offer more options, value and control to meet the customer’s needs. One such example is that it offers the latest fixed phones that take some of the best features from mobile phones, such as address book and caller ID. Through product bundling, Telstra is able to package a combination of these services into one large offering and single bill service, which makes its mobile products more attractive for consumers. Although Telstra boasts on having a stable and reliable internet connection, its internet speed has been observed to perform poorly. According to ACCC chairman Graeme Samuel, â€Å"Telstra’s competitors have been offering ADSL2+ since 2005, but until now Telstra’s fastest DSL offering was a mere 1.5 megabits per second (mbps)†. It’s reluctance to switch to ADSL2+ has been based on the fear that the Australian Competition and Consumer Commission would â€Å"declare† it, thereby giving competitors access to ADSL2+ at controlled prices (Barker, 2006). Telstra does not use much of traditional advertising for fixed-line products and services such as billboards, banners and digital banners, but rather it spends most of its advertising on new products and services such as BigPond Broadband and the Next G network. This is made possible as Telstra monopolizes the landlines in Australia, and people would still use its fixed-line products and services. Telstra should continue doing this and to continue to spend more in marketing its new products and services to meet the demand of the emerging market. Given this, Telstra is gaining the demands for both its fixed-line and new products and services. PriceTelstra is focused on product line pricing, where products within the same product range are priced differently, depending on its features and benefits. This price discrimination is to assist Telstra in maximising their turnover and profit. LandlinesBusiness line plans are priced at $99.90 per month (including monthly Line Rental), where the customer can get unlimited local and STD calls and valued rates on calls to mobiles. For fixed home phones, the price ranges from $60 to $500. The average pricing for rental phones includes the provisioning fee of $20 plus $3.00 per month for rental. Mobile PhonesTelstra’s mobile services are priced according to mobile phone plans, with the model of the phone being the main determinant of their price, where the greater the features of the phone, the dearer the price. As Telstra is by large a service oriented company, they emphasise on communicating more on the benefits of their services, such as covering most parts of Australia and its supplementary services and network stability. InternetThe Bigpond Internet is priced according to the type of infrastructure used. For example, it’s prepaid dial-up lines have a fee range of $2 to $19.95 for a validity of up to 45 days, while their wireless service price ranges from $29.95 to $199.95 on a 12 month contract. CritiquesA comparison of the pricing component of Telstra’s marketing strategy with offerings from competitors such as Virgin and Optus reveals that Telstra does not always have the lowest rates. For example, Optus offers a cable connection of 20GB for $79.95, where as Telstra offers it for $99.95. Comparing the plans Optus has a better price in almost every internet product than Telstra. In addition to that, Optus provides free initial time for one or two months on a variety of their internet connections which lacks in Telstra. Telstra also has a slow internet speed compared to Optus. From Telstra’s mobile plans, its 24 month long phone plans provide the lowest call rates out of Telstra’s offerings while the two shorter member- and casual plans have more expensive call rates. Due to its wide network coverage, Telstra emphasizes on this feature, rather than pushing their price as their primary selling point. On the other hand, the company is trying to attract price conscious consumers with its money back promotion for pre-paid mobile plans. In mid-July 2005, Telstra launched a major advertising campaign, where they promote the benefit of gaining credit for doing nothing but talk. Customers can gain up to an extra $50 in call credit each month, 5 cent extra for each incoming minute and 1 cent extra for every outgoing minute. This promotion campaigns are created to outdo the competition from Optus and Vodafone. They engaged in price war in order to offer the best options. Here, customers are  benefiting from lower prices, but unfortunately it is not likewise for these companies. Here, the government’s implementation of the ceiling and floor price for telecommunications regulation is significant to avoid further price war. PlaceTelstra’s telecommunications products and services are widely available throughout Australia. Telstra has 115 owned Telstra branded stores and 153 licensed shops, with two of its largest licensors being the mobile phone chain Crazy John’s and Australia Post. In addition, the company also has a compelling web presence with a complete online shop for the purchasing of mobile phone plans, pre-paid refills, handsets and supplementary services such as ring tones and games. Telstra boasts of a strong internet presence, with its network covering almost 98% of the population, providing internet service to even the remotest area in Australia. CritiquesTelstra shops and dealers are strategically located across Australia. It attends to the need of the people in their respective area. Apart from its own dealers and shops, it also has Crazy John as its exclusive mobile phone dealers which are very reputable and successful dealers. Telstra definitely has the edge over its competitors in terms of numbers and locations. PromotionTelstra promotes its products through media, out-of home billboards and also through their in-store value-added product offerings. For example, the promotion of the Bigpond is done in variety of ways, such as using humor and celebrities through their advertisements and electronic billboards in train stations, advertisements on trams, and also on their website. Telstra also promotes their products and services through providing value-added components to attract customers and promote customer loyalty. For example, Telstra uses loyalty programs such as ‘Telstra Rewards’ for landlines. This loyalty program entitles eligible customers to receive a 5% or 10% discount off eligible calls and charges. It is also observed that product bundling is commonly used in Telstra as a means of attracting customers to sign up for their products and services at a cheaper price. For example, product bundling is targeted to families, where families are given the option of combining their Telstra bills at a cheaper price. Telstra also provides discounted Bluetooth headsets with selected fixed phones in its product offering. Telstra promotes heavily on its mobile phone offering through service improvements, such as covering 96 percent of Australia with its GSM network and 98 percent with its CDMA network. CritiquesTelstra is one of the country’s largest investor in out-of-home advertising, spending more than $A9 million during the first six months of 2005. Despite being one of the largest spenders on advertising, Telstra has to be wary of Optus, its close competitor in fixed-line service. Although revenue from its fixed-line services have been declining due to the increased dependence of mobile phones and the internet, little has been done to promote Telstra’s fixed line services. Again this is because more emphasize is being put into promoting its new products and services, and also the fact that fixed-line is declining in demand. Telstra billboards were located in the heart of the cities and traffic for people’s easy access. PeoplePeople are greatest asset for Telstra. In selling and marketing, Telstra aims to attract the best people by keeping them contented and encouraging them to grow and improve. Telstra develops their staff through a sophisticated development framework, including online development courses, frontline manager and leadership programs, and a comprehensive range of options that develop technical and interpersonal skills. Telstra strives to build a culture that respects and harnesses the individual differences that all people bring to an organization. Through the Telstra Business Principles and the Code of Conduct standards of appropriate business and ethical behaviors are valued and expected from all employees. CritiquesThrough mystery shopping and observation, the sales staffs are good at communicating. They are knowledgeable about the products and services featured. Telstra has a diversity of staff consisting from various non-English speaking backgrounds. This portrays Telstra’s success in maintaining its image as a company that values diverse workforce in order to  meet the customers’ needs. ProcessThe process of applying for Telstra’s telecommunication products and services are relatively easy, where customers can go through Telstra shops and dealers and also through the Internet. The process involves interacting with Telstra’s sales team and providing relevant documents when applying for their products. However, the mystery shopping revealed that the processes of the new users’ application for the latest technology next G network took a longer time to be completed which is due to unfamiliarity with the new product. Physical EvidenceProducts such as telephones, mobile phones and Internet hardware such as modems and routers are provided by Telstra. These products are often offered as value-added components to their service for customers when signing up with Telstra. Being a large establishment in this industry, Telstra has a strong 24 hour customer support, which provides assurance to consumers that Telstra are reliable. 2.Information servicesBeing a telecommunications provider, Telstra complements this service with a directory service, through Yellow Pages, White Pages and the Trading Post. Due to the increasing dependence on the internet, Telstra has designed Sensis as a search engine to bring together listed businesses in the directory under one umbrella. Product/ServiceTelstra’s information services operate under the brand name Sensis. Sensis is designed as a search engine for Australians, bringing together businesses listed in the Yellow Pages, White Pages and Trading Post under one search engine. It acts as a platform that brings together businesses and customers. Sensis has a large network partner, which includes some of Australia’s more popular websites, such as CitySearch and Whereis. CritiquesSensis should be commended on trying to build a reliable circle of trust through their partner networks. With these alliances, Sensis is able to build up its brand through popular websites and establish a high standard  of reliability. PriceSensis has its own unique pricing strategy, with BidSmart, which acts as a method that determines the priority of each business listed in a particular search. A company/business with a higher bid for listing in sensis.com.au will appear first compared to other businesses that have a lower bid with the same search phrase. At the end of the month, the price paid by each business is determined according to the number of ‘hits’ the websites get from search traffics. CritiquesThis pricing strategy is suitable for Sensis. Being a pioneer in providing directory services through the Internet, Sensis is able to create value for its advertisers, by allowing them to pay according to the number of ‘hits’ obtained. This is a good establishment for Sensis as it is able to attract more businesses to advertise with them without burdening the businesses with a fixed price as they continue to build the Sensis brand to attract more searches. PlaceSensis is available on the internet and is linked with their network partners. Recent collaborations with carshowroom.com.au are aimed to increase its recognition and customer traffic through the website. CritiquesTransferring Yellow Pages and White Pages to the internet under Sensis is a good move as people are now depending on the internet for faster information. In addition to that, people are not burdened with the hassle of the printed version of these two directories which are bulky and time-consuming when searching for information. PromotionSensis promotes itself by collaborating with websites such as carshowroom.com.au to increase its presence in the marketplace. Sensis’ public relations have also helped gain recognition through press releases and conferences. In addition, Sensis has published a book, Small Business, Big Opportunity and a new website for small businesses to aid Australian businesses to optimize their marketing and advertising skills. CritiquesAlthough Sensis does not focus on marketing through billboards and media advertisements, their logo can be found on Telstra websites and also on their partner network websites that aims to provide a level of interaction between their partners and Sensis. PeopleSensis is recognized as one of Australia’s leading employers, who strongly encourage an environment of learning, innovation and enjoyable. Behind these employees is a strong support system – its board of directors and executive team leaders who overlook the direction of the business. CritiquesThrough a phone interview enquiring Sensis’ service, the customer service staff was knowledgeable regarding the processes required to sign up for their services and was also friendly and helpful in providing advice on how to advertise on Sensis. ProcessThe process of signing up to advertise on Sensis is relatively straightforward with a step-by-step process. Businesses/advertisers would need to register with Sensis before submitting their advertisement to the Sensis search engine. These clients are given the option of choosing among 3 advertising packages to aid in their advertisement presentation. Other websites can form partnerships with Sensis through inserting a Sensis link onto their website. This can be done by downloading a program which integrates the link onto the website. CritiquesThis strategy is good as it provides a level of flexibility and control for clients. With the three types of packages, Sensis is able to attract a wider demographic of businesses to advertise with them as they will be able to address different business needs. Physical EvidenceSensis is available on the Internet, with approximately 100,000 searches per day. According to statistics, Sensis serves almost 13 million users every month and about 150,000 online advertising customers advertise with Sensis. CritiquesAlthough Telstra aims to make Sensis one of Australia’s premier  search engine, many people are unaware of the existence of Sensis as it has only been advertised through the Telstra website and their partner network search engines. Consumers are not aware of the significance of the Sensis logo on these websites, hence decreasing internet hits. Market research shows that Sensis is the 5th visited local search engine in Australia, with 0.39% market share. Although launched in 2004, Sensis has yet to make a big impact in the Australian internet scene. Due to the transition of White Pages onto Sensis, search engine results have been quite inaccurate, affecting its reliability and popularity (Jones, 2006). ConclusionAs the report progresses with the information about Telstra’s main products and services under two main industries; 1) Telecommunication industry and 2) Information services, Telstra’s marketing strategies are being critiqued using the marketing tool, the 7 P’s framework. In the telecommunication industry, Telstra’s marketing is more on media, billboards and value-added components. Also, it engages more in promoting its new products and services to capture the emerging market. Also, Sensis has helped Telstra to increase customer usage of the directory, thus the Telstra website which leads to a comprehensive information. In essence, the marketing strategies in its telecommunication industry is currently in good practice but its information service needs further promotion to create more reliability and data accurateness. References Barker G. 2006, Telco finally gets on ADSL2+ bandwagon, The Age, viewed 11 December 2006, Corner S. 2006, Telstra’s grand plan to know and serve its customers, ITWIRE, viewed 13 January 2007, Jones M. 2006, Sensis revs engine but Google still rules, Australian Financial Review, viewed 2 January 2007, Government Introduces Laws to Boost Telstra Competition, The Age.com.au, 26 September 2002, viewed on 20 December 2006,Optus, viewed 9 January 2007 Shanahan J. 2006, Consumer-centricity: An idea whose Time Has Come, Australian Marketing Institute, viewed on 5 December 2006, Sensis, viewed 10 January 2007, Telstra, viewed 9 January 2007

Research on Warehouse Design

European Journal of Operational Research 203 (2010) 539–549 Contents lists available at ScienceDirect European Journal of Operational Research journal homepage: www. elsevier. com/locate/ejor Invited Review Research on warehouse design and performance evaluation: A comprehensive review Jinxiang Gu a, Marc Goetschalckx b,*, Leon F. McGinnis b a b Nestle USA, 800 North Brand Blvd. , Glendale, CA 91203, United States Georgia Institute of Technology, 765 Ferst Dr. , Atlanta, GA 30332-0205, United States a r t i c l e i n f o a b s t r a c tThis paper presents a detailed survey of the research on warehouse design, performance evaluation, practical case studies, and computational support tools. This and an earlier survey on warehouse operation provide a comprehensive review of existing academic research results in the framework of a systematic classi? cation. Each research area within this framework is discussed, including the identi? cation of the limits of previous research and of potential future research directions. O 2009 Elsevier B. V. All rights reserved.Article history: Received 5 December 2005 Accepted 21 July 2009 Available online 6 August 2009 Keywords: Facilities design and planning Warehouse design Warehouse performance evaluation model Case studies Computational tools 1. Introduction This survey and a companion paper (Gu et al. , 2007) present a comprehensive review of the state-of-art of warehouse research. Whereas the latter focuses on warehouse operation problems related to the four major warehouse functions, i. e. , receiving, storage, order picking, and shipping, this paper concentrates on warehouse design, performance evaluation, case studies, and computational support tools.The objectives are to provide an all-inclusive overview of the available methodologies and tools for improving warehouse design practices and to identify potential future research directions. Warehouse design involves ? ve major decisions as illustrated in Fig. 1: deter mining the overall warehouse structure; sizing and dimensioning the warehouse and its departments; determining the detailed layout within each department; selecting warehouse equipment; and selecting operational strategies. The overall structure (or conceptual design) determines the material ? ow pattern within the warehouse, the speci? ation of functional departments, and the ? ow relationships between departments. The sizing and dimensioning decisions determine the size and dimension of the warehouse as well as the space allocation among various warehouse departments. Department layout is the detailed con? guration within a warehouse department, for example, aisle con? guration in the retrieval area, pallet block-stacking pattern in the reserve storage area, and con? guration of an Automated Storage/Retrieval System (AS/RS). The equipment selection deci* Corresponding author. Tel. : +1 404 894 2317; fax: +1 404 894 2301. E-mail address: marc. [email  protected] gatech. edu (M. G oetschalckx). 0377-2217/$ – see front matter O 2009 Elsevier B. V. All rights reserved. doi:10. 1016/j. ejor. 2009. 07. 031 sions determine an appropriate automation level for the warehouse, and identify equipment types for storage, transportation, order picking, and sorting. The selection of the operation strategy determines how the warehouse will be operated, for example, with regards to storage and order picking. Operation strategies refer to those decisions about operations that have global effects on other design decisions, and therefore need to be considered in the design phase.Examples of such operation strategies include the choice between randomized storage or dedicated storage, whether or not to do zone picking, and the choice between sort-while-pick or sortafter-pick. Detailed operational policies, such as how to batch and route the order picking tour, are not considered design problems and therefore are discussed in Gu et al. (2007). It should be emphasized that w arehouse design decisions are strongly coupled and it is dif? cult to de? ne a sharp boundary between them. Therefore, our proposed classi? ation should not be regarded as unique, nor does it imply that any of the decisions should be made independently. Furthermore, one should not ignore operational performance measures in the design phase since operational ef? ciency is strongly affected by the design decisions, but it can be very expensive or impossible to change the design decisions once the warehouse is actually built. Performance evaluation is important for both warehouse design and operation. Assessing the performance of a warehouse in terms of cost, throughput, space utilization, and service provides feedback about how a speci? design or operational policy performs compared with the requirements, and how it can be improved. Furthermore, a good performance evaluation model can help the designer to quickly evaluate many design alternatives and narrow down the design space durin g the early design stage. Performance operational cost for each alternative is estimated using simple analytic equations. Gray et al. (1992) address a similar problem, and propose a multi-stage hierarchical approach that uses simple calculations to evaluate the tradeoffs and prune the design space to a few superior alternatives.Simulation is then used to provide detailed performance evaluation of the resulting alternatives. Yoon and Sharp (1996) propose a structured approach for exploring the design space of order picking systems, which includes stages such as design information collection, design alternative development, and performance evaluation. In summary, published research ndco4h lar02. 8659(war,. 0320Td[(pro2k evaluation methods include benchmarking, analytical models, and simulation models.This review will mainly focus on the former two since simulation results depend greatly on the implementation details and are less amenable to generalization. However, this should not obs cure the fact that simulation is still the most widely used technique for warehouse performance evaluation in the academic literature as well as in practice. Some case studies and computational systems are also discussed in this paper. Research in these two directions is very limited. However, it is our belief that more case studies and computational tools for warehouse design and operation will help to bridge the signi? ant gap between academic research and practical application, and therefore, represent a key need for the future. The study presented in this paper and its companion paper on operations, Gu et al. (2007), complements previous surveys on warehouse research, for example, Cormier (2005), Cormier and Gunn (1992), van den Berg (1999) and Rowenhorst et al. (2000). Over 250 papers are included within our classi? cation scheme. To our knowledge, it is the most comprehensive review of existing research results on warehousing.However, we make no claim that it includes all the literature on warehousing. The scope of this survey has been mainly focused on results published in available English-language research journals. The topic of warehouse location, which is part of the larger area of distribution system design, is not addressed in this current review. A recent survey on warehouse location is provided by Daskin et al. (2005). The next four sections will discuss the literature on warehouse design, performance evaluation, case studies, and computational systems, respectively. The ? al section gives conclusions and future research directions. 2. Warehouse design 2. 1. Overall structure The overall structure (or conceptual design) of a warehouse determines the functional departments, e. g. , how many storage departments, employing what technologies, and how orders will be assembled. At this stage of design, the issues are to meet storage and throughput requirements, and to minimize costs, which may be the discounted value of investment and future operating costs. We can identify only three published papers addressing overall structural design.Park and Webster (1989) assume the functions are given, and select equipment types, storage rules, and order picking policies to minimize total costs. The initial investment cost and annual J. Gu et al. / European Journal of Operational Research 203 (2010) 539–549 541 Levy (1974), Cormier and Gunn (1996) and Goh et al. (2001) consider warehouse sizing problems in the case where the warehouse is responsible for controlling the inventory. Therefore, the costs in their models include not only warehouse construction cost, but also inventory holding and replenishment cost.Levy (1974) presents analytic models to determine the optimal storage size for a single product with either deterministic or stochastic demand. Assuming additional space can be leased to supplement the warehouse, Cormier and Gunn (1996) propose closed-form solution that yields the optimal warehouse size, the optimal amount of space to lease in each period, and the optimal replenishment quantity for a single product case with deterministic demand. The multi-product case is modeled as a nonlinear optimization problem assuming that the timing of replenishments is not managed.Cormier and Gunn (1999) developed a nonlinear programming formulation for the optimal warehouse expansion over consecutive time periods. Goh et al. (2001) ? nd the optimal storage size for both single-product and multi-product cases with deterministic demand. They consider a more realistic piecewise linear model for the warehouse construction cost instead of the traditional linear cost model. Furthermore, they consider the possibility of joint inventory replenishment for the multi-product case, and propose a heuristic to ? nd the warehouse size.The effects of inventory control policies (e. g. , the reorder point and ordering quantity) on the total required storage capacity are shown by Rosenblatt and Roll (1988) using simulation. Our a bility to answer warehouse sizing questions would be signi? cantly enhanced by two types of research. First, assessing capacity requirements should consider seasonality, storage policy, and order characteristics, because these three factors interact to impact the achievable storage ef? ciency, i. e. that fraction of warehouse capacity that can actually be used effectively.Second, sizing models all employ cost models, and validation studies of these models would be a signi? cant contribution. 2. 2. 2. Warehouse dimensioning The warehouse dimensioning problem translates capacity into ? oor space in order to assess construction and operating costs, and was ? rst modeled by Francis (1967), who used a continuous approximation of the storage area without considering aisle structure. Bassan et al. (1980) extends Francis (1967) by considering aisle con? gurations. Rosenblatt and Roll (1984) integrate the optimization model in Bassan et al. 1980) with a simulation model which evaluates the s torage shortage cost, a function of storage capacity and number of zones. They assume single-command tours in order to evaluate the effect of warehouse dimension on the operational cost, and therefore their approach is not applicable to warehouses that perform multi-command operations (e. g. , interleaving put-away and retrieval, or retrieving multiple items per trip). The work discussed so far has approached the sizing and dimensioning problem assuming the warehouse has a single storage department.In reality, a warehouse might have multiple departments, e. g. , a forward-reserve con? guration, or different storage departments for different classes of Stock Keeping Units (SKUs). These different departments must be arranged in a single warehouse and compete with each other for space. Therefore, there are tradeoffs in determining the total warehouse size, allocating the warehouse space among departments, and determining the dimension of the warehouse and its departments. Research stud ying these tradeoffs in the warehouse area is scarce.Pliskin and Dori (1982) propose a method to compare alternative space allocations among different warehouse departments based on multi-attribute value functions, which explicitly capture the tradeoffs among different criteria. Azadivar (1989) proposes an approach to optimally allocate space between two departments: one is ef? cient in terms of storage but inef? cient in terms of operation, while the other is the opposite. The objective is to achieve the best system performance by appropriately allocating space between these two departments to balance the storage capacity and operational ef? iency tradeoffs. Heragu et al. (2005) consider a warehouse with ? ve functional areas, i. e. , receiving, shipping, cross-docking, reserve, and forward. They propose an optimization model and a heuristic algorithm to determine the assignment of SKUs to the different storage areas as well as the size of each functional area to minimize the total material handling and storage costs. A key issue with all research on the dimensioning problem is that it requires performance models of material handling; these models are often independent of the size or layout of the warehouse.Research is needed to either validate these models, or develop design methods that explicitly consider the impact of sizing and dimensioning on material handling. 2. 3. Department layout In this section we discus layout problems within a warehouse department, primarily a storage department. The storage problems are classi? ed as: (P1) pallet block-stacking pattern, i. e. , storage lane depth, number of lanes for each depth, stack height, pallet placement angle with regards to the aisle, storage clearance between pallets, and length and width of aisles; (P2) storage department layout, i. . , door location, aisle orientation, length and width of aisles, and number of aisles; and (P3) AS/RS con? guration, i. e. , dimension of storage racks, number of cranes. These layout problems affect warehouse performances with respect to: (O1) construction and maintenance cost; (O2) material handling cost; (O3) storage capacity, e. g. , the ability to accommodate incoming shipments; (O4) space utilization; and (O5) equipment utilization. Each problem is treated in the literature by different authors considering a subset of the performance measures, as summarized in Table 1. 2. 3. 1.Pallet block-stacking pattern (P1) In the pallet block-stacking problem, a fundamental decision is the selection of lane depths to balance the tradeoffs between space utilization and ease of storage/retrieval operations, considering the SKUs’ stackability limits, arriving lot sizes, and retrieval patterns. Using deep lane storage could increase space utilization because fewer aisles are needed, but on the other hand could also cause decreased space utilization due to the ‘‘honeycombing† effect that creates unusable space for the storage of other i tems until the whole lane is totally depleted.The magnitude of the honeycombing effect depends on lane depths as well as the withdrawal rates of individual products. Therefore, it might be bene? cial to store different classes of products in different lane depths. A careful determination and coordination of the lane depths for different products is necessary in order to achieve the best storage space utilization. Besides lane con? guration, the pallet block-stacking problem also determines such decisions as aisle widths and orientation, stack height, and storage clearance, which all affect storage space utilization, material handling ef? iency, and storage capacity. 542 J. Gu et al. / European Journal of Operational Research 203 (2010) 539–549 Table 1 A summary of the literature on warehouse layout design. Problem P1 Citation Moder and Thornton (1965) Berry (1968) Marsh (1979) Marsh (1983) Goetschalckx and Ratliff (1991) Larson et al. (1997) Roberts and Reed (1972) Bassan et al. (1980) Rosenblatt and Roll (1984) Pandit and Palekar (1993) P3 Karasawa et al. (1980) Ashayeri et al. 1985) Rosenblatt et al. (1993) Objective O4 O2, O4 O3, O4 O4 O2, O4 O1, O2 O1, O2 O1, O2, O3 O2 O1, O2, O3 O1, O2 O1, O2, O3 O1, O5 O1, O5 O1 Method Analytical formulae Analytical formulae Simulation models Heuristic procedure Heuristic procedure Dynamic Programming Optimal design using analytical formulation Optimal two-dimensional search method Queuing model Nonlinear mixed integer problem Nonlinear mixed integer problem Nonlinear mixed integer problem NotesMainly on lane depth determination For class-based storage Consider the con? guration of storage bays (unit storage blocks) Consider horizontal and vertical aisle orientations, locations of doors, and zoning of the storage area Based on Bassan et al’s work with additional costs due to the use of grouped storage Include not only the ordinary travel time, but also waiting time when all vehicles are busy The model is so lved by generalized Lagrange multiplier method Given rack height, the model can be simpli? d to a convex problem System service is evaluated using simulations, if not satisfactory, new constraints are added and the optimization model is solved again to get a new solution A more elaborated variation of Zollinger’s rules that consider explicitly operational policies For the design of an automated carousel system. The model is solved with a simple search algorithm P2 Zollinger (1996) Malmborg (2001) Lee and Hwang (1988) Rule of thumb heuristic Rule of thumb heuristic Nonlinear integer program A number of papers discuss the pallet block-stacking problem.Moder and Thornton (1965) consider ways of stacking pallets in a warehouse and the in? uence on space utilization and ease of storage and retrieval. They consider such design factors as lane depth, pallet placement angle with regards to the aisle, and spacing between storage lanes. Berry (1968) discusses the tradeoffs between stor age ef? ciency and material handling costs by developing analytic models to evaluate the total warehouse volume and the average travel distance for a given storage space requirement.The factors considered include warehouse shape, number, length and orientation of aisles, lane depth, throughput rate, and number of SKUs contained in the warehouse. It should be noted that the models for total warehouse volume and models for average travel distance are not integrated, and the warehouse layout that maximizes storage ef? ciency is different from the one that minimizes travel distance. Marsh (1979) uses simulation to evaluate the effect on space utilization of alternate lane depths and the rules for assigning incoming shipments to lanes.Marsh (1983) compares the layout design developed by using the simulation models of Marsh (1979) and the analytic models proposed by Berry (1968). Goetschalckx and Ratliff (1991) develop an ef? cient dynamic programming algorithm to maximize space utilizati on by selecting lane depths out of a limited number of allowable depths and assigning incoming shipments to the different lane depths. Larson et al. (1997) propose a three-step heuristic for the layout problem of class-based pallet storage with the purpose to maximize storage space utilization and minimize material handling cost. The ? st phase determines the aisles layout and storage zone dimensions; the second phase assigns SKUs to storage con? gurations; and the third phase assigns ? oor space to the storage con? gurations. The research addressing the pallet block-stacking problem suggests different rules or algorithms, usually with restrictive assumptions, e. g. , the replenishment quantities and retrieval frequencies for each SKU are known. In reality, not only do these change dynamically, but the SKU set itself changes, and pallet block-stacking patterns that are optimized for current conditions may be far from optimum in the near future.Research is needed that will identify a robust solution in the face of dynamic uncertainty in the storage and retrieval requirements. 2. 3. 2. Storage department layout (P2) The storage department layout problem is to determine the aisle structure of a storage department in order to minimize the construction cost and material handling cost. The decisions usually include aisle orientations, number of aisles, length and width of aisles, and door locations.In order to evaluate operational costs, some assumptions are usually made about the storage and order picking policies; random storage and single-command order picking are the most common assumptions. By assuming a layout con? guration, or a small set of alternative con? gurations, models can be formulated to optimize each con? guration. Roberts and Reed (1972) assume storage space is available in units of identical bays. Bassan et al. (1980) consider a rectangular warehouse, and aisles that are either parallel or perpendicular to the longest walls.In addition, they also discuss the optimal door locations in the storage department, and the optimal layout when the storage area is divided into different zones. Roll and Rosenblatt (1983) extend Bassan et al. (1980) to include the additional cost due to the use of grouped storage policy. Pandit and Palekar (1993) minimize the expected response time of storage and/or retrieval requests using a queuing model to calculate the total response time including waiting and processing time for different types of layouts. With these response times, an optimization model is solved to ? nd the optimal storage space con? urations. Roodbergen and Vis (2006) present an optimization approach for selecting the number and length of aisles and the depot location so as to minimize the expected length of a picking tour. They developed models for both S-shaped tours and a largest gap policy, and concluded that the choice of routing policy could, in some cases, have a signi? cant impact on the size and layout of the department . The conclusion from Roodbergen and Vis (2006) is quite significant, since it calls into question the attempt to optimize storage department layout without knowing what the true material handling performance will be.There is a need for additional research that helps to identify the magnitude of the impact of layout (for reasonably shaped departments) on total costs over the life of the warehouse, considering changing storage and retrieval requirements. J. Gu et al. / European Journal of Operational Research 203 (2010) 539–549 543 2. 3. 3. AS/RS con? guration (P3) The AS/RS con? guration problem is to determine the numbers of cranes and aisles, and storage rack dimension in order to minimize construction, maintenance, and operational cost, and/or maximize equipment utilization.The optimal design models or rule-ofthumb procedures summarized in Table 1 typically utilize some empirical expressions of the costs based on simple assumptions for the operational policies, and known s torage and retrieval rates. Karasawa et al. (1980) present a nonlinear mixed integer formulation with decision variables being the number of cranes and the height and length of storage racks and costs including construction and equipment costs while satisfying service and storage capacity requirements. Ashayeri et al. 1985) solve a problem similar to Karasawa et al. (1980). Given the storage capacity requirement and the height of racks, their models can be simpli? ed to include only a single design variable, i. e. , the number of aisles. Furthermore, the objective function is shown to be convex in the number of aisles, which allows a simple one-dimensional search algorithm to optimally solve the problem. Rosenblatt et al. (1993) propose an optimization model that is a slight modi? cation of Ashayeri et al. (1985), which allows a crane to serve multiple aisles.A combined optimization and simulation approach is proposed, where the optimization model generates an initial design, and a simulation evaluates performance, e. g. , service level. If the constraints evaluated by simulation are satis? ed, then the procedure stops. Otherwise, the optimization model is altered by adding new constraints that have been constructed by approximating the simulation results. Zollinger (1996) proposes some rule of thumb heuristics for designing an AS/RS. The design criteria include the total equipment costs, S/ R machine utilization, service time, number of jobs waiting in the queue, and storage space requirements.Closed form equations compute these criteria as functions of the number of aisles and the number of levels in the storage rack. Malmborg (2001) uses simulation to re? ne the estimates of some of the parameters which then are used in the closed form equations. The design of automated carousel storage systems is addressed by Lee and Hwang (1988). They use an optimization approach to determine the optimal number of S/R machines and the optimal dimensions of the carousel sy stem to minimize the initial investment cost and operational costs over a ? ite planning horizon subject to constraints for throughput, storage capacity, and site restrictions. Some other less well-discussed AS/RS design problems include determining the size of the basic material handling unit and the con? guration of I/O points. Roll et al. (1989) propose a procedure to determine the single optimal container size in an AS/RS, which is the basic unit for storage and order picking. Container size has a direct effect on space utilization, and therefore on the equipment cost since the storage capacity requirement needs to be satis? ed. Randhawa et al. 1991) and Randhawa and Shroff (1995) use simulations to investigate different I/O con? gurations on performance such as throughput, mean waiting time, and maximum waiting time. The results indicate that increased system throughput can be achieved using I/O con? gurations different from the common one-dock layout where the dock is located at the end of the aisle. There are two important opportunities for additional research on AS/RS con? guration: (1) results for a much broader range of technology options, e. g. , double deep rack, multi-shuttle cranes, etc. ; and (2) results demonstrating the sensitivity of con? urations to changes in the expected storage and retrieval rates or the effects of a changing product mix. 2. 4. Equipment selection The equipment selection problem addresses the level of automation in a warehouse and what type of storage and material han- dling systems should be employed. These decisions obviously are strategic in nature in that they affect almost all the other decisions as well as the overall warehouse investment and performance. Determining the best level of automation is far from obvious in most cases, and in practice it is usually determined based on the personal experience of designers and managers.Academic research in this category is extremely rare. Cox (1986) provides a methodology t o evaluate different levels of automation based on a cost-productivity analysis technique called the hierarchy of productivity ratios. White et al. (1981) develop analytical models to compare block stacking, single-deep and doubledeep pallet rack, deep lane storage, and unit load AS/RS in order to determine the minimum space design. Matson and White (1981) extend White et al. (1981) to develop a total cost model incorporating both space and material handling costs, and demonstrate the effect of handling requirements on the optimum storage design.Sharp et al. (1994) compare several competing small part storage equipment types assuming different product sizes and dimensions. They considered shelving systems, modular drawers, gravity ? ow racks, carousel systems, and mini-load storage/retrieval systems. The costs they considered include operational costs, ? oor space costs, and equipment costs. In summary, research on equipment selection is quite limited and preliminary, although it is very important in the sense that it will affect the whole warehouse design and the overall lifetime costs.There are two fundamental issues for equipment selection: (1) how to identify the equipment alternatives that are reasonable for a given storage/retrieval requirement; and (2) how to select among the reasonable alternatives. A very signi? cant contribution would be to develop a method for characterizing requirements and characterizing equipment in such a way that these two issues could be addressed in a uni? ed manner. 2. 5. Operation strategy This section discusses the selection of operation strategies in a warehouse.The focus is on operation strategies that, once selected, have important effects on the overall system and are not likely to be changed frequently. Examples of such strategies are the decision between randomized and dedicated storage, or the decision to use zone picking. Two major operation strategies are discussed: the storage strategy and the order picking strat egy. Detailed operation policies and their implementations are discussed in Gu et al. (2007). 2. 5. 1. Storage The basic storage strategies include random storage, dedicated storage, class-based storage, and Duration-of-Stay (DOS) based storage, as explained in Gu et al. 2007). Hausman et al. (1976), Graves et al. (1977) and Schwarz et al. (1978) compare random storage, dedicated storage, and class-based storage in single-command and dual-command AS/RS using both analytical models and simulations. They show that signi? cant reductions in travel time are obtainable from dedicated storage compared with random storage, and also that class-based storage with relatively few classes yields travel time reductions that are close to those obtained by dedicated storage.Goetschalckx and Ratliff (1990) and Thonemann and Brandeau (1998) show theoretically that DOS-based storage policies are the most promising in terms of minimizing traveling costs. Historically, DOS-based policies were dif? cult to implement since they require the tracking and management of each stored unit in the warehouse, but modern WMS’s have this capability. Also the performance of DOS-based policies depends greatly on factors such as the skewness of demands, balance of input and output ? ows, inventory control policies, and the speci? cs of implementation. In a study by Kulturel et al. (1999), class-based 544 J. Gu et al. European Journal of Operational Research 203 (2010) 539–549 storage and DOS-based storage are compared using simulations, and the former is found to consistently outperform the latter. This conclusion may have been reached because the assumptions of the DOS model rarely hold true in practice. All the results on operational strategies are for unit-load AS/RS. Studies on other storage systems are rarely reported. Malmborg and Al-Tassan (1998) develop analytic models to evaluate the performance of dedicated storage and randomized storage in lessthan-unit-load warehouses, but no general conclusions comparable to the unit-load case are given.A strong case can be made that additional research is needed, especially to clarify the conditions under which the storage policy does or does not have a signi? cant impact on capacity or travel time. 2. 5. 2. Order picking In a given day or shift, a warehouse may have many orders to pick. These orders may be similar in a number of respects; for example, some orders are shipped using the same carrier, or transportation mode, or have the same pick due date and time.If there are similarities among subsets of orders that require them to be shipped together, then they also should be picked roughly during the same time period to avoid intermediate storage and staging. Thus, it is common practice to use wave picking, i. e. , to release a fraction of the day’s (shift’s) orders, and to expect their picking to be completed within a corresponding fraction of the day (shift). In addition to wave picking, two ot her commonly used orderpicking strategies are batch picking and zone picking.Batch picking involves the assignment of a group of orders to a picker to be picked simultaneously in one trip. In zone picking, the storage space is divided into picking zones and each zone has one or more assigned pickers who only pick in their assigned zone. Zone picking can be divided into sequential and parallel zone picking. Sequential zone picking is similar to a ? ow line, in which containers that can hold one or more orders are passed sequentially through the zones; the pickers in each zone pick the products within their zone, put them into the container, and then pass the container to the next zone. Bartholdi et al. (2000) propose a Bucket Brigades order picking method that is similar to sequential zone picking, but does not require pickers to be restricted to zones). In parallel zone picking, an order is picked in each zone simultaneously. The picked items are sent to a downstream sorting system to be combined into orders. The organization and planning of the order picking process has to answer the following questions: 1. Will product be transported to the picker (part-to-picker) or will the picker travel to the storage location (picker-to-part)? . Will orders be picked in waves? If so, how many waves of what duration? 3. Will the warehouse be divided into zones? If so, will zones be picked sequentially or concurrently? 4. Will orders be picked in batches or separately? If they are batched, will they be sorted while picking or after picking? Depending on the operating principles selected, the order picking methods will be:        Single order picking. Batching with sort-while-pick. Batching with sort-after-pick. Sequential zoning with single order picking. Sequential zoning with batching.Concurrent zoning without batching. Concurrent zoning with batching. Research on the selection of an order picking strategy is very scarce, which might be a result of the complexity of the problem itself. Lin and Lu (1999) compare single-order picking and batch zone picking for different types of orders, which are classi? ed based on the order quantity and the number of ordered items. Petersen (2000) simulates ? ve different order-picking policies: singleorder picking, batch picking, sequential zone picking, concurrent zone picking, and wave picking.Two control variables in the simulation study are the numbers of daily orders and the demand skewness, while the other factors such as warehouse layout, storage assignment, and zone con? guration (when zone and wave picking are used) are ? xed. The performance measures used to compare the different policies include: the mean daily labor, the mean length of day, and the mean percentage of late orders. For each order picking policy, the simplest rules regarding batching, routing, and wave length are used. It also should be noted that the performance measures are mainly related to order picking ef? iencies and service quality; additional costs caused by downstream sorting with batch, zone, and wave picking are not considered. Furthermore, comparison of these policies are made mainly with regards to the order structures, while other important factors such as storage assignment and detailed implementations of the order picking policies are assumed to be ? xed. Therefore, the results should not be considered generic and more research in this direction is required to provide more guidance for warehouse designers. Order picking strategy selection remains a largely unresolved design problem.Additional research would be valuable, especially if it could begin to characterize order picking alternatives in ways that were easy to apply in design decision making. As an example, could researchers develop performance curves for different order picking strategies? 3. Performance evaluation Performance evaluation provides feedback on the quality of a proposed design and/or operational policy, and more importantl y, on how to improve it. There are different approaches for performance evaluation: benchmarking, analytic models, and simulations. This section will only discuss benchmarking and analytic models. 3. 1.Benchmarking Warehouse benchmarking is the process of systematically assessing the performance of a warehouse, identifying inef? ciencies, and proposing improvements. Data Envelopment Analysis (DEA) is regarded as an appropriate tool for this task because of its capability to capture simultaneously all the relevant inputs (resources) and outputs (performances), to construct the best performance frontier, and to reveals the relative shortcomings of inef? cient warehouses. Schefczyk (1993), Hackman et al. (2001), and Ross and Droge (2002) shows some approaches and case studies of using DEA in warehouse benchmarking.An Internet-based DEA system (iDEAS) for warehouses is developed by the Keck Lab at Georgia Tech, which includes information on more than 200 warehouses (McGinnis, 2003). 3. 2. Analytical models Analytic performance models fall into two main categories: (1) aisle based models which focus on a single storage system and address travel or service time; and (2) integrated models which address either multiple storage systems or criteria in addition to travel/service times. J. Gu et al. / European Journal of Operational Research 203 (2010) 539–549 545 3. 2. 1.Aisle based models Table 2 summarizes research on travel time models for aislebased systems. A signi? cant fraction of research focuses on the expected travel time for the crane in an AS/RS, for either single command (SC) or dual command (DC) cycles. For both, there is research addressing three different storage policies: in randomized storage, any SKU can occupy any location; in dedicated storage, each SKU has a set of designated locations; and in class based storage, a group of storage locations is allocated to a class of SKUs, and randomized storage is allowed within the group of storage locati ons.The issue with DC cycles is matching up storages and retrievals to minimize the dead-head travel of the crane, which may involve sequencing retrievals, and selecting storage locations. The results in this category usually assume in? nite acceleration to simplify the travel time models, although some develop more elaborate models by considering acceleration for the various axes of motion (see, e. g. , Hwang and Lee, 1990; Hwang et al. , 2004b; Chang and Wen, 1997; Chang et al. , 1995).There are a few papers that attack the more mathematically challenging issue of deriving the distribution of travel time (see Foley and Frazelle (1991) and Foley et al. (2002)). The research on carousel travel time models generally parallels corresponding AS/RS research. Given some knowledge of travel time, AS/RS service time models can be developed, considering the times required for load/unload and store/retrieve at the storage slot. Queuing models have been developed assuming various distribution s for travel time, see e. g. Lee (1997), Chow (1986), Hur et al. (2004), Bozer and White (1984), Park et al. (2003a) for AS/RS, Chang et al. (1995) for conventional multi-aisle systems, and for end-of-aisle picking systems, see Bozer and White (1991, 1996), Park et al. (2003a), and Park et al. (1999). Stochastic optimization models have been developed for estimating AS/RS throughput, with constraints on storage queue length and retrieval request waiting time (Azadivar, 1986). The throughput of carousel systems is modeled by Park et al. (2003b) and Meller and Klote (2004).The former consider a system with two carousels and one picker, and derive analytic expressions for the system throughput and picker utilization assuming deterministic and exponential pick time distributions. Meller and Klote (2004) develop throughput models for systems with multiple carousels using an approximate two-server queuing model approach. For conventional multi-aisle storage systems (bin shelving, e. g. ), two kinds of travel time results have been developed: (1) models which estimate the expected travel time; and (2) models of the pdf of travel times.These models require an assumption about the structure of the tour, e. g. , traversal (Hall, 1993), return (Hall, 1993 or Caron et al. , 1998), or largest gap (Roodbergen and Vis, 2006). As long as these models are parameterized on attributes of the storage system design, they can be used to support design by searching over the relevant parameters. As with AS/RS and carousels, there has been research to incorporate travel time models into performance models. Chew and Table 2 Literature of travel time models for different warehouse systems. Randomized storage Unit-load AS/RS Single-command Hausman et al. 1976) Bozer and White (1984) Thonemann and Brandeau (1998) Kim and Seidmann (1990) Hwang and Ko (1988) Lee (1997) Hwang and Lee (1990) Chang et al. (1995) Chang and Wen (1997) Koh et al. (2002) Lee et al. (1999) Graves et al. (1977) Boze r and White (1984) Kim and Seidmann (1990) Hwang and Ko (1988) Lee (1997) Han et al. (1987) Hwang and Lee (1990) Chang et al. (1995) Chang and Wen (1997) Koh et al. (2002) Lee et al. (1999) Meller and Mungwattana (1997) Potrc et al. (2004) Hwang and Song (1993) Bozer and White (1990) Bozer and White (1996) Foley and Frazelle (1991) Park et al. 1999) Han and McGinnis (1986) Han et al. (1988) Su (1998) Hwang and Ha (1991) Hwang et al. (1999) Hall (1993) Jarvis and McDowell (1991) Chew and Tang (1999) Hwang et al. (2004a) Caron et al. (1998) Caron et al. (2000) Jarvis and McDowell (1991) Chew and Tang (1999) Hwang et al. (2004a) Park et al. (2003a) Dedicated storage Hausman et al. (1976) Thonemann and Brandeau (1998) Kim and Seidmann (1990) Class-based storage Hausman et al. (1976) Thonemann and Brandeau (1998) Rosenblatt and Eynan (1989) Eynan and Rosenblatt (1994) Kouvelis and Papanicolaou (1995) Kim and Seidmann (1990) Pan and Wang (1996) Ashayeri et al. 2002) Dual-command Graves et al. (1977) Kim and Seidmann (1990) Graves et al. (1977) Kouvelis and Papanicolaou (1995) Kim and Seidmann (1990) Pan and Wang (1996) Ashayeri et al. (2002) Multi-shuttle Man-on-board AS/RS End-of-aisle AS/RS Carousel and rotary racks Ha and Hwang (1994) Conventional multi-aisle system Jarvis and McDowell (1991) Chew and Tang (1999) Hwang et al. (2004a) 546 J. Gu et al. / European Journal of Operational Research 203 (2010) 539–549 Tang (1999) use their model of the travel time pdf to analyze order batching and storage allocation using a queuing model.Bhaskaran and Malmborg (1989) present a stochastic performance evaluation model for the service process in multi-aisle warehouses with an approximated distribution for the service time that depends on the batch size and the travel distance. de Koster (1994) develops queuing models to evaluate the performance of a warehouse that uses sequential zone picking where each bin is assigned to one or more orders and is transported using a conveyer. If a bin needs to be picked in a speci? c zone, it is transported to the corresponding pick station.After it is picked, it is then put on the conveyor to be sent to the next pick station. The proposed queuing network model evaluates performance measures such as system throughput, picker utilization, and the average number of bins in the system based on factors such as the speed and length of the conveyor, the number of picking stations, and the number of picks per station. Throughput analysis of sorting systems is addressed in Johnson and Meller (2002). They assume that the induction process is the bottleneck of the sorting process, and therefore governs the throughput of the sorting system.This model is later incorporated into a more comprehensive model in Russell and Meller (2003) that integrates order picking and sorting to balance the tradeoffs between picking and packing with different order batch sizes and wave lengths. Russell and Meller (2003) also demonstrate th e use of the proposed model in determining whether or not to automate the sorting process and in designing the sorting system. 3. 2. 2. Integrated models Integrated models combine travel time analysis and the service quality criteria with other performance measures, e. g. storage capacity, construction cost, and operational cost. Malmborg (1996) proposes an integrated performance evaluation model for a warehouse having a forward-reserve con? guration. The proposed model uses information about inventory management, forward-reserve space allocation, and storage layout to evaluate costs associated with: storage capacity and space shortage; inventory carrying, replenishing, and expediting; and order picking and internal replenishment for the forward area. Malmborg (2000) evaluates several performance measures for a twin-shuttle AS/RS.Malmborg and Al-Tassan (2000) present a mathematical model to estimated space requirements and order picking cycle times for less than unit load order pick ing systems that uses randomized storage. The inputs of the model include product parameters, equipment speci? cations, operational policies, and storage area con? gurations. Malmborg (2003) models the dependency of performance measures such as expected total system construction cost and throughput on factors such as the vehicle ? eet size, the number of lifts, and the storage rack con? gurations for warehouse systems that use rail guided vehicles.Table 3 A Summary of the literature on warehouse case studies. Citation Cormier and Kersey (1995) Yoon and Sharp (1995) Zeng et al. (2002) Kallina and Lynn (1976) Brynzer and Johansson (1995) Burkard et al. (1995) van Oudheusden et al. (1988) Dekker et al. (2004) Luxhoj and Skarpness (1986) Johnson and Lofgren (1994) Problems studied Conceptual design Analytic travel time and performance models of storage systems represent a major contribution to warehouse design related research, and a rich set of models is available. Yet despite this wea lth of prior results, there is no uni? d approach to travel time modeling or performance modeling for aisle based systems – every system and every set of assumptions leads to a different model. A signi? cant research contribution would be to present a uni? ed theory of travel time in aisle-based systems. 4. Case studies There are some published industrial case studies, which not only provide applications of the various design and operation methods in practical contexts, but more importantly, also identify possible future research challenges from the industrial point of view. Table 3 lists these case studies, identifying the problems and the types of warehouse they investigated.It is dif? cult to generalize from such a small set of speci? c cases, but one conclusion is that substantial bene? ts can achieved by appropriately designing and operating a warehouse, see for example Zeng et al. (2002), van Oudheusden et al. (1988), and Dekker et al. (2004). On the other hand, one mig ht conclude from these cases that there are few generic simple rules. As just one example, the COI-based storage location assignment rule proposed by Kallina and Lynn (1976) ignores many practical considerations, such as varying weights, item-dependent travel costs, or dependencies between items.Some of these complications have been addressed in the academic research (for example see Table 3 in Section 5. 2 of Gu et al. (2007)), but many others remain unexplored. What these cases illustrate is the gap between the assumption-restricted models in research publications and the complex reality of most warehouses. There is a signi? cant need for more industrial case studies, which will assist the warehouse research community in better understanding the real issues in warehouse design. In turn, research results that have been tested on more realistic data sets will have a more substantial impact on practice.A warehouse design problem classi? cation, such as we have proposed here, might be used to structure such future case studies. 5. Computational systems There are numerous commercial Warehouse Management Systems (WMS) available in the market, which basically help the warehouse manager to keep track of the products, orders, space, equipment, and human resources in a warehouse, and provide rules/algorithms for storage location assignment, order batching, pick routing, etc. Detailed review of these systems is beyond the scope of this paper.Instead, we focus on the academic research addressing computational systems for warehouse design. As previous sections show, research on various warehouse design and Type of warehouse A warehouse for perishable goods that requires Just-In-Time operations An order picking system A distribution center A distribution center Kitting systems that supply materials to assembly lines An AS/RS where a S/R machine can serve any aisle using a switching gangway A man-on-board AS/RS in an integrated steel mill A multi-aisle manual order picking system A distribution center A distribution centerConceptual design Storage location assignment; warehouse dimensioning; storage and order picking policies Storage location assignment using the COI rule Process ? ow; batching; zone picking; Vehicle routing Storage location assignment; batching; routing Storage and routing policies Manpower planning Simulation by decomposition J. Gu et al. / European Journal of Operational Research 203 (2010) 539–549 547 operation problems has been conducted for almost half a century, and as a result, a large number of methodologies, algorithms, and empirical studies have been generated.However, successful implementations of these academic results in current commercial WMS systems or in engineering design software are rare. The prototype systems discussed in this section might shed some light on how academic research results could be utilized to develop more sophisticated computer aided warehouse design and operation systems. Perlmann and Bai ley (1988) present computer-aided design software that allows a warehouse designer to quickly generate a set of conceptual design alternatives including building shape, equipment selection, and operational policy selection, and to select from among them the best one based on the speci? d design requirements. To our knowledge, this is the only research paper addressing computer aided warehouse design. There are several papers on the design of warehouse control systems. Linn and Wysk (1990) develop an expert system for AS/ RS control. A control policy determines decisions such as storage location assignment, which item to retrieve if multi-items for the same product are stored, storage and retrieval sequencing, and storage relocation.Several control rules are available for each decision and the control policy is constructed by selecting one individual rule for each decision in a coherent way based on dynamically changing system state variables such as demand levels and traf? c intensi ty. A similar AS/RS control system is proposed by Wang and Yih (1997) based on neural networks. Ito et al. (2002) propose an intelligent agent based system to model a warehouse, which is composed of three subsystems, i. e. , agent-based communication system, agent-based material handling system, and agent-based inventory planning and control system.The proposed agent-based system is used for the design and implementation of warehouse simulation models. Kim et al. (2002) present an agent based system for the control of a warehouse for cosmetic products. In addition to providing the communication function, the agents also make decisions regarding the operation of the warehouse entities they represented in a dynamic real-time fashion. The absence of research prototypes for computer aided warehouse design is particularly puzzling, given the rapid advancement in computing hardware and software over the past decade.Academic researchers have been at the forefront of computer aided design i n other disciplines, and particularly in developing computational models to support design decision making. Warehousing design, as a research domain, would appear to be ripe for this kind of contribution. 6. Conclusions and discussion We have attempted a thorough examination of the published research related to warehouse design, and classi? ed papers based on the main issues addressed. Fig. 1 shows the numbers of papers in each category; there were 50 papers directly addressing warehouse design decisions.There were an additional 50 papers on various analytic models of travel time or performance for speci? c storage systems or aggregates of storage systems. Benchmarking, case studies and other surveys account for 18 more papers. One clear conclusion is that warehouse design related research has focused on analysis, primarily of storage systems rather than synthesis. While this is somewhat surprising, an even more surprising observation is that only 10% of papers directly addressing w arehouse design decisions have a publication date of 2000 or later.Given the rapid development of computing hardware and solvers for optimization, simulation, and general mathematical problems, one might reasonably expect a more robust design-centric research literature. We conjecture two primary inhibiting factors: 1. The warehouse design decisions identi? ed in Fig. 1 are tightly coupled, and one cannot be analyzed or determined in isolation from the others. Yet, the models available are not uni? ed in any way and are not ‘‘interoperable†. A researcher addressing one decision would require a research infrastructure integrating all the other decisions.The scope and scale of this infrastructure appears too great a challenge for individual researchers. 2. To properly evaluate the impact of changing one of the design decisions requires estimating changes in the operation of the warehouse. Not only are future operating scenarios not speci? ed in detail, even if they w ere, the total warehouse performance assessment models, such as high ? delity simulations, are themselves a considerable development challenge. From this, we conclude that the most important future direction for the warehouse design research community is to ? d ways to overcome these two hurdles. Key to that, we believe, will be the emergence of standard representations of warehouse elements, and perhaps some research community based tools, such as open-source analysis and design models. Other avenues for important contributions include studies describing validated or applied design models, and practical case studies that demonstrate the potential bene? ts of applying academic research results to real problems, or in identifying the hidden challenges that prevent their successful implementation.Finally, both analytic and simulation models are proposed to solve warehouse problems and each has its respective advantages and disadvantages. Analytic models are usually design-oriented in the sense that they can explore many alternatives quickly to ? nd solutions, although they may not capture all the relevant details of the system. On the other hand, simulation models are usually analysis-oriented – they provide an assessment of a given design, but usually have limited capability for exploring the design space. There is an important need to integrate both approaches to achieve more ? exibility in analyzing warehouse problems.This is also pointed out by Ashayeri and Gelders (1985), and its applicability has been demonstrated by Rosenblatt and Roll (1984) and Rosenblatt et al. (1993). There is an enormous gap between the published warehouse research and the practice of warehouse design and operations. Cross fertilization between the groups of practitioners and researchers appears to be very limited. Effectively bridging this gap would improve the state-of-the-art in warehouse design methodology. Until such communication is established, the prospect of meaningfu l expansion and enhancement of warehouse design methodology appears limited.Warehousing is an essential component in any supply chain. 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