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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Bibliographic Details
Published in:AIChE journal Vol. 64; no. 4; pp. 1246 - 1261
Main Authors: Zhou, Rui‐Jie, Li, Li‐Juan
Format: Journal Article
Language:English
Published: New York American Institute of Chemical Engineers 01.04.2018
Subjects:
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
ISSN:0001-1541, 1547-5905
Online Access:Get full text
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Summary: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
Bibliography:The authors contribute equally to this work.
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ISSN:0001-1541
1547-5905
DOI:10.1002/aic.16012