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A robust ordering strategy for retailers facing a free shipping option
Free shipping with conditions has become one of the most effective marketing tools available. An increasing number of companies, especially e-businesses, prefer to offer free shipping with some predetermined condition, such as a minimum purchase amount by the customer. However, in practice, the dema...
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Published in: | PloS one 2015-05, Vol.10 (5), p.e0125939-e0125939 |
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description | Free shipping with conditions has become one of the most effective marketing tools available. An increasing number of companies, especially e-businesses, prefer to offer free shipping with some predetermined condition, such as a minimum purchase amount by the customer. However, in practice, the demands of buyers are uncertain; they are often affected by many factors, such as the weather and season. We begin by modeling the centralized ordering problem in which the supplier offers a free shipping service and retailers face stochastic demands. As these random data are considered, only partial information such as the known mean, support, and deviation is needed. The model is then analyzed via a robust optimization method, and the two types of equivalent sets of uncertainty constraints that are obtained provide good mathematical properties with consideration of the robustness of solutions. Subsequently, a numerical example is used to compare the results achieved from a robust optimization method and the linear decision rules. Additionally, the robustness of the optimal solution is discussed, as it is affected by the minimum quantity parameters. The increasing cost-threshold relationship is divided into three periods. In addition, the case study shows that the proposed method achieves better stability as well as computational complexity. |
doi_str_mv | 10.1371/journal.pone.0125939 |
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Additionally, the robustness of the optimal solution is discussed, as it is affected by the minimum quantity parameters. The increasing cost-threshold relationship is divided into three periods. In addition, the case study shows that the proposed method achieves better stability as well as computational complexity.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0125939</identifier><identifier>PMID: 25993533</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Commerce ; Computer applications ; Cost control ; Costs and Cost Analysis ; Electronic commerce ; Experiments ; Fees & charges ; Game theory ; Inventory ; Linear programming ; Operations management ; Optimization ; Researchers ; Retail stores ; Retail trade ; Robustness (mathematics) ; Shipping ; Stochasticity ; Studies ; Suppliers ; Transportation - economics</subject><ispartof>PloS one, 2015-05, Vol.10 (5), p.e0125939-e0125939</ispartof><rights>COPYRIGHT 2015 Public Library of Science</rights><rights>2015 Meng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2015 Meng et al 2015 Meng et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-d11e8d094f7a7c44b118ec72257b3238b44f1cf83849ca61af8326180c265a1c3</citedby><cites>FETCH-LOGICAL-c692t-d11e8d094f7a7c44b118ec72257b3238b44f1cf83849ca61af8326180c265a1c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/1681912549/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/1681912549?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25993533$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Shi, Yongtang</contributor><creatorcontrib>Meng, Qing-chun</creatorcontrib><creatorcontrib>Wan, Xiao-le</creatorcontrib><creatorcontrib>Rong, Xiao-xia</creatorcontrib><title>A robust ordering strategy for retailers facing a free shipping option</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Free shipping with conditions has become one of the most effective marketing tools available. An increasing number of companies, especially e-businesses, prefer to offer free shipping with some predetermined condition, such as a minimum purchase amount by the customer. However, in practice, the demands of buyers are uncertain; they are often affected by many factors, such as the weather and season. We begin by modeling the centralized ordering problem in which the supplier offers a free shipping service and retailers face stochastic demands. As these random data are considered, only partial information such as the known mean, support, and deviation is needed. The model is then analyzed via a robust optimization method, and the two types of equivalent sets of uncertainty constraints that are obtained provide good mathematical properties with consideration of the robustness of solutions. Subsequently, a numerical example is used to compare the results achieved from a robust optimization method and the linear decision rules. 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An increasing number of companies, especially e-businesses, prefer to offer free shipping with some predetermined condition, such as a minimum purchase amount by the customer. However, in practice, the demands of buyers are uncertain; they are often affected by many factors, such as the weather and season. We begin by modeling the centralized ordering problem in which the supplier offers a free shipping service and retailers face stochastic demands. As these random data are considered, only partial information such as the known mean, support, and deviation is needed. The model is then analyzed via a robust optimization method, and the two types of equivalent sets of uncertainty constraints that are obtained provide good mathematical properties with consideration of the robustness of solutions. Subsequently, a numerical example is used to compare the results achieved from a robust optimization method and the linear decision rules. 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subjects | Commerce Computer applications Cost control Costs and Cost Analysis Electronic commerce Experiments Fees & charges Game theory Inventory Linear programming Operations management Optimization Researchers Retail stores Retail trade Robustness (mathematics) Shipping Stochasticity Studies Suppliers Transportation - economics |
title | A robust ordering strategy for retailers facing a free shipping option |
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