A robust optimization approach to closed-loop supply chain network design under uncertainty

The concern about significant changes in the business environment (such as customer demands and transportation costs) has spurred an interest in designing scalable and robust supply chains. This paper proposes a robust optimization model for handling the inherent uncertainty of input data in a close...

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Bibliographic Details
Published in:Applied mathematical modelling Vol. 35; no. 2; pp. 637 - 649
Main Authors: Pishvaee, Mir Saman, Rabbani, Masoud, Torabi, Seyed Ali
Format: Journal Article
Language:English
Published: Kidlington Elsevier Inc 01.02.2011
Elsevier
Subjects:
Applied sciences
Closed-loop supply chain network
Exact sciences and technology
Logistics
Mathematical programming
Operational research and scientific management
Operational research. Management science
Reverse logistics
Robust optimization
Closed-loop supply chain network
Robust optimization
Reverse logistics
Closed loop
Mixed integer programming
Economy of transports
Modeling
Recovery
Linear model
Optimization
Uncertain system
Transportation problem
Supply demand balance
Uncertainty handling
Robustness
ISSN:0307-904X
Online Access:Get full text
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Summary:The concern about significant changes in the business environment (such as customer demands and transportation costs) has spurred an interest in designing scalable and robust supply chains. This paper proposes a robust optimization model for handling the inherent uncertainty of input data in a closed-loop supply chain network design problem. First, a deterministic mixed-integer linear programming model is developed for designing a closed-loop supply chain network. Then, the robust counterpart of the proposed mixed-integer linear programming model is presented by using the recent extensions in robust optimization theory. Finally, to assess the robustness of the solutions obtained by the novel robust optimization model, they are compared to those generated by the deterministic mixed-integer linear programming model in a number of realizations under different test problems.
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ISSN:0307-904X
DOI:10.1016/j.apm.2010.07.013