Pricing and Coordination Strategy in a Green Supply Chain with a Risk-Averse Retailer
This paper addresses the pricing and coordination strategy in a green supply chain in which a manufacturer produces a green product and sells it to a risk-averse retailer. The product’s demand is a random variable influenced by the green level and the retail price. The problem is modeled in three di...
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Veröffentlicht in: | Mathematical problems in engineering 2019, Vol.2019 (2019), p.1-12 |
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description | This paper addresses the pricing and coordination strategy in a green supply chain in which a manufacturer produces a green product and sells it to a risk-averse retailer. The product’s demand is a random variable influenced by the green level and the retail price. The problem is modeled in three different structures, a centralized and two decentralized models, in which the upstream manufacturer and the downstream retailer act as the channel leader, respectively. This paper presents the optimal decisions for all supply chain members, analyzes the effects of green degree and risk-averse coefficient on the supply chain members’ decision-making and their profits, and performs the numerical analysis. The results show that the green degree and the whole supply chain’s expected profits are highest in the centralized scenario, followed by the retailer-led scenario, and lowest under the manufacturer-led scenario; the green degree and the manufacturer’s expected profit increase with the risk-averse coefficient, no matter who dominates the channel; however, the risk-averse coefficient’s effects on the retailer’s expected utility and the retail price depends on who dominates the channel and on the greening investment parameter. |
doi_str_mv | 10.1155/2019/7482080 |
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The product’s demand is a random variable influenced by the green level and the retail price. The problem is modeled in three different structures, a centralized and two decentralized models, in which the upstream manufacturer and the downstream retailer act as the channel leader, respectively. This paper presents the optimal decisions for all supply chain members, analyzes the effects of green degree and risk-averse coefficient on the supply chain members’ decision-making and their profits, and performs the numerical analysis. The results show that the green degree and the whole supply chain’s expected profits are highest in the centralized scenario, followed by the retailer-led scenario, and lowest under the manufacturer-led scenario; the green degree and the manufacturer’s expected profit increase with the risk-averse coefficient, no matter who dominates the channel; however, the risk-averse coefficient’s effects on the retailer’s expected utility and the retail price depends on who dominates the channel and on the greening investment parameter.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2019/7482080</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Coefficients ; Coordination ; Decision analysis ; Decision making ; Engineering ; Numerical analysis ; Pricing ; Random variables ; Retail stores ; Risk ; Supply chains</subject><ispartof>Mathematical problems in engineering, 2019, Vol.2019 (2019), p.1-12</ispartof><rights>Copyright © 2019 Liyan Wang et al.</rights><rights>Copyright © 2019 Liyan Wang et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 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The product’s demand is a random variable influenced by the green level and the retail price. The problem is modeled in three different structures, a centralized and two decentralized models, in which the upstream manufacturer and the downstream retailer act as the channel leader, respectively. This paper presents the optimal decisions for all supply chain members, analyzes the effects of green degree and risk-averse coefficient on the supply chain members’ decision-making and their profits, and performs the numerical analysis. The results show that the green degree and the whole supply chain’s expected profits are highest in the centralized scenario, followed by the retailer-led scenario, and lowest under the manufacturer-led scenario; the green degree and the manufacturer’s expected profit increase with the risk-averse coefficient, no matter who dominates the channel; however, the risk-averse coefficient’s effects on the retailer’s expected utility and the retail price depends on who dominates the channel and on the greening investment parameter.</description><subject>Coefficients</subject><subject>Coordination</subject><subject>Decision analysis</subject><subject>Decision making</subject><subject>Engineering</subject><subject>Numerical analysis</subject><subject>Pricing</subject><subject>Random variables</subject><subject>Retail stores</subject><subject>Risk</subject><subject>Supply chains</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>BENPR</sourceid><recordid>eNqF0MFKw0AQBuBFFKzVm2cJeNTYndlsNjlK0SoUlGrBW9gkk3ZrTeJuaunbuyUFj55mmPmYgZ-xS-B3AFKOkEM6UlGCPOFHbAAyFqGESB37nmMUAoqPU3bm3IpzBAnJgM1frSlMvQh0XQbjprGlqXVnmjp466zuaLELTB3oYGKJ_GzTtutdMF5qP9yabuk3M-M-w_sfso6CGXXarMmes5NKrx1dHOqQzR8f3sdP4fRl8jy-n4aFiHkXAvFKqJTnIHROslJcYCVBYpyiLqNcFaQijGWSJFRijFjmVRGliYcxqlKLIbvu77a2-d6Q67JVs7G1f5mhEICYKlBe3faqsI1zlqqsteZL210GPNsHl-2Dyw7BeX7T86WpS701_-mrXpM3VOk_DanyKYtfsL11hQ</recordid><startdate>2019</startdate><enddate>2019</enddate><creator>Sha, Yipeng</creator><creator>Ma, Shanshan</creator><creator>Ye, Minghai</creator><creator>Wang, Liyan</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0002-4151-7503</orcidid><orcidid>https://orcid.org/0000-0003-2135-7116</orcidid><orcidid>https://orcid.org/0000-0002-9829-5673</orcidid></search><sort><creationdate>2019</creationdate><title>Pricing and Coordination Strategy in a Green Supply Chain with a Risk-Averse Retailer</title><author>Sha, Yipeng ; Ma, Shanshan ; Ye, Minghai ; Wang, Liyan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c360t-1e0f3790b13abe5f7032f5152692ad4b7ce74265888ed2622dbfc498f70627da3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Coefficients</topic><topic>Coordination</topic><topic>Decision analysis</topic><topic>Decision making</topic><topic>Engineering</topic><topic>Numerical analysis</topic><topic>Pricing</topic><topic>Random variables</topic><topic>Retail stores</topic><topic>Risk</topic><topic>Supply chains</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sha, Yipeng</creatorcontrib><creatorcontrib>Ma, Shanshan</creatorcontrib><creatorcontrib>Ye, Minghai</creatorcontrib><creatorcontrib>Wang, Liyan</creatorcontrib><collection>الدوريات العلمية والإحصائية - e-Marefa Academic and Statistical Periodicals</collection><collection>معرفة - المحتوى العربي الأكاديمي المتكامل - e-Marefa Academic Complete</collection><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sha, Yipeng</au><au>Ma, Shanshan</au><au>Ye, Minghai</au><au>Wang, Liyan</au><au>Guo, Zhaoxia</au><au>Zhaoxia Guo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Pricing and Coordination Strategy in a Green Supply Chain with a Risk-Averse Retailer</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2019</date><risdate>2019</risdate><volume>2019</volume><issue>2019</issue><spage>1</spage><epage>12</epage><pages>1-12</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>This paper addresses the pricing and coordination strategy in a green supply chain in which a manufacturer produces a green product and sells it to a risk-averse retailer. The product’s demand is a random variable influenced by the green level and the retail price. The problem is modeled in three different structures, a centralized and two decentralized models, in which the upstream manufacturer and the downstream retailer act as the channel leader, respectively. This paper presents the optimal decisions for all supply chain members, analyzes the effects of green degree and risk-averse coefficient on the supply chain members’ decision-making and their profits, and performs the numerical analysis. The results show that the green degree and the whole supply chain’s expected profits are highest in the centralized scenario, followed by the retailer-led scenario, and lowest under the manufacturer-led scenario; the green degree and the manufacturer’s expected profit increase with the risk-averse coefficient, no matter who dominates the channel; however, the risk-averse coefficient’s effects on the retailer’s expected utility and the retail price depends on who dominates the channel and on the greening investment parameter.</abstract><cop>Cairo, Egypt</cop><pub>Hindawi Publishing Corporation</pub><doi>10.1155/2019/7482080</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-4151-7503</orcidid><orcidid>https://orcid.org/0000-0003-2135-7116</orcidid><orcidid>https://orcid.org/0000-0002-9829-5673</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Coefficients Coordination Decision analysis Decision making Engineering Numerical analysis Pricing Random variables Retail stores Risk Supply chains |
title | Pricing and Coordination Strategy in a Green Supply Chain with a Risk-Averse Retailer |
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