Green supply chain design with emission sensitive demand: second order cone programming formulation and case study
We propose a novel modeling framework for supply chain network design that models a prevailing trend in consumer choice in which demand is impacted by carbon footprint. To date, the literature lacks models that realistically account for and accurately calculate per unit emissions, i.e., carbon footp...
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| Published in: | Optimization letters Vol. 15; no. 1; pp. 231 - 247 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.02.2021
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| ISSN: | 1862-4472, 1862-4480 |
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| Abstract | We propose a novel modeling framework for supply chain network design that models a prevailing trend in consumer choice in which demand is impacted by carbon footprint. To date, the literature lacks models that realistically account for and accurately calculate per unit emissions, i.e., carbon footprint. We develop a profit maximizing model that accounts for emissions at the different stages of the supply chain, locates facilities and selects their technology, and decides on the flow between echelons. To calculate the carbon footprint, fixed emissions are averaged over throughput, which results in a nonlinear optimization problem with fractional terms. To solve it, we provide a mixed integer second order cone programming reformulation. We perform extensive testing of the framework on a realistic case study and carry out detailed analysis. The proposed framework succeeds in capturing the trade-off between lost demand due to a high carbon footprint and investing in environmentally-friendly technology. The framework serves as a tool to induce organizations to invest in green technology and to allow regulating authorities to assess the impact of eco-labeling. |
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| AbstractList | We propose a novel modeling framework for supply chain network design that models a prevailing trend in consumer choice in which demand is impacted by carbon footprint. To date, the literature lacks models that realistically account for and accurately calculate per unit emissions, i.e., carbon footprint. We develop a profit maximizing model that accounts for emissions at the different stages of the supply chain, locates facilities and selects their technology, and decides on the flow between echelons. To calculate the carbon footprint, fixed emissions are averaged over throughput, which results in a nonlinear optimization problem with fractional terms. To solve it, we provide a mixed integer second order cone programming reformulation. We perform extensive testing of the framework on a realistic case study and carry out detailed analysis. The proposed framework succeeds in capturing the trade-off between lost demand due to a high carbon footprint and investing in environmentally-friendly technology. The framework serves as a tool to induce organizations to invest in green technology and to allow regulating authorities to assess the impact of eco-labeling. |
| Author | Gzara, Fatma Waltho, Cynthia Elhedhli, Samir |
| Author_xml | – sequence: 1 givenname: Samir surname: Elhedhli fullname: Elhedhli, Samir email: elhedhli@uwaterloo.ca organization: Department of Management Sciences, University of Waterloo – sequence: 2 givenname: Fatma surname: Gzara fullname: Gzara, Fatma organization: Department of Management Sciences, University of Waterloo – sequence: 3 givenname: Cynthia surname: Waltho fullname: Waltho, Cynthia organization: Department of Management Sciences, University of Waterloo |
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| Cites_doi | 10.1016/j.orl.2016.03.015 10.1016/j.ejor.2006.02.016 10.1016/j.ijpe.2018.12.003 10.1287/mnsc.42.8.1199 10.1108/JAMR-07-2012-0027 10.1016/j.dss.2012.05.053 10.1109/TASE.2012.2203304 10.1016/j.ijpe.2015.11.002 10.1080/00207543.2014.961203 10.1111/j.1937-5956.2003.tb00210.x 10.1080/0740817X.2013.860508 10.1007/s10898-019-00817-7 10.1080/02331934.2016.1276179 10.1016/j.ijpe.2011.01.020 |
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| Keywords | Green supply chain design Eco-labels Carbon footprint Emission sensitive demand Second order cone programming |
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| References | CR2 Benjaafar, Yanzhi, Daskin (CR3) 2013; 10 Seuring (CR18) 2013; 54 Lu, Gzara, Elhedhli (CR10) 2014; 46 CR6 CR5 Mehmanchi, Gomez, Prokopyev (CR13) 2019; 75 Yalabik, Fairchild (CR22) 2011; 131 CR7 Stancu-Minasian (CR19) 2017; 66 Altmann (CR1) 2015; 53 CR15 Waltho, Elhedhli, Gzara (CR21) 2019; 208 CR14 CR12 Luthra, Garg, Haleem (CR11) 2014; 11 CR20 Borreo, Gillen, Prokopyev (CR4) 2016; 44 Klassen, McLaughlin (CR9) 1996; 42 Kassinis, Soteriou (CR8) 2003; 12 Romeijn, Shu, Teo (CR17) 2007; 178 Nouira, Hammami, Frein, Temponi (CR16) 2016; 173 1631_CR14 B Yalabik (1631_CR22) 2011; 131 1631_CR15 1631_CR12 1631_CR7 1631_CR6 S Luthra (1631_CR11) 2014; 11 JS Borreo (1631_CR4) 2016; 44 1631_CR5 RD Klassen (1631_CR9) 1996; 42 1631_CR20 1631_CR2 M Altmann (1631_CR1) 2015; 53 HE Romeijn (1631_CR17) 2007; 178 S Seuring (1631_CR18) 2013; 54 S Benjaafar (1631_CR3) 2013; 10 E Mehmanchi (1631_CR13) 2019; 75 IM Stancu-Minasian (1631_CR19) 2017; 66 G Kassinis (1631_CR8) 2003; 12 C Waltho (1631_CR21) 2019; 208 D Lu (1631_CR10) 2014; 46 I Nouira (1631_CR16) 2016; 173 |
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| Title | Green supply chain design with emission sensitive demand: second order cone programming formulation and case study |
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