Joint capacity planning and distribution network optimization of coal supply chains under uncertainty
A two‐stage stochastic integer programming model is developed to address the joint capacity planning and distribution network optimization of multiechelon coal supply chains (CSCs) under uncertainty. The proposed model not only introduces the uses of compound real options in sequential capacity plan...
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| Vydáno v: | AIChE journal Ročník 64; číslo 4; s. 1246 - 1261 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
American Institute of Chemical Engineers
01.04.2018
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| ISSN: | 0001-1541, 1547-5905 |
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| Abstract | A two‐stage stochastic integer programming model is developed to address the joint capacity planning and distribution network optimization of multiechelon coal supply chains (CSCs) under uncertainty. The proposed model not only introduces the uses of compound real options in sequential capacity planning, but also considers uncertainty induced by both risks and ambiguities. Both strategic decisions (i.e., facility locations and initial investment, service assignment across the entire CSC, and option holding status) and scenario‐based operational decisions (i.e., facility operations and capacity expansions, outsourcing policy, and transportation and inventory strategies) can be simultaneously determined using the model. By exploiting the nested decomposable structure of the model, we develop a new distributed parallel optimization algorithm based on nonconvex generalized Bender decomposition and Lagrangean relaxation to mitigate the computation resource limitation. One of the main CSCs in China is studied to demonstrate the applicability of the proposed model and the performance of the algorithm. © 2017 American Institute of Chemical Engineers AIChE J, 64: 1246–1261, 2018 |
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| AbstractList | A two‐stage stochastic integer programming model is developed to address the joint capacity planning and distribution network optimization of multiechelon coal supply chains (CSCs) under uncertainty. The proposed model not only introduces the uses of compound real options in sequential capacity planning, but also considers uncertainty induced by both risks and ambiguities. Both strategic decisions (i.e., facility locations and initial investment, service assignment across the entire CSC, and option holding status) and scenario‐based operational decisions (i.e., facility operations and capacity expansions, outsourcing policy, and transportation and inventory strategies) can be simultaneously determined using the model. By exploiting the nested decomposable structure of the model, we develop a new distributed parallel optimization algorithm based on nonconvex generalized Bender decomposition and Lagrangean relaxation to mitigate the computation resource limitation. One of the main CSCs in China is studied to demonstrate the applicability of the proposed model and the performance of the algorithm. © 2017 American Institute of Chemical Engineers AIChE J , 64: 1246–1261, 2018 |
| Author | Zhou, Rui‐Jie Li, Li‐Juan |
| Author_xml | – sequence: 1 givenname: Rui‐Jie orcidid: 0000-0002-0070-4871 surname: Zhou fullname: Zhou, Rui‐Jie email: ruijie@alumni.stanford.edu organization: Dept. of Electrical Engineering, Stanford University – sequence: 2 givenname: Li‐Juan surname: Li fullname: Li, Li‐Juan organization: Rensselaer Polytechnic Institute |
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| SubjectTerms | Algorithms Coal coal supply chains compound real options Decisions Decomposition distributed parallel optimization Distribution management Integer programming Networks Optimization Outsourcing risk and ambiguity Supply chains two‐stage stochastic integer programming Uncertainty |
| Title | Joint capacity planning and distribution network optimization of coal supply chains under uncertainty |
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