A robust optimization approach in a multi-objective closed-loop supply chain model under imperfect quality production

The closed-loop supply chain model with an assumption of perfect produced items is used commonly in the literature. We consider the imperfect quality production to provide meaningful solutions to practical problems. In this novel model, we assume that the screening is not always perfect, and inspect...

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Bibliographic Details
Published in:Annals of operations research Vol. 319; no. 2; pp. 1479 - 1505
Main Authors: Almaraj, Ismail I., Trafalis, Theodore B.
Format: Journal Article
Language:English
Published: New York Springer US 01.12.2022
Springer
Springer Nature B.V
Subjects:
Business and Management
Combinatorics
Inspection
Integer programming
Linear programming
Mathematical optimization
Mixed integer
Operations research
Operations Research/Decision Theory
Optimization
Original Research
Production management
Robustness (mathematics)
Supply chains
Theory of Computation
Saudi Arabia
Tchebycheff method
Robust optimization
Closed-loop supply chain
Imperfect quality production
ISSN:0254-5330, 1572-9338
Online Access:Get full text
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Summary:The closed-loop supply chain model with an assumption of perfect produced items is used commonly in the literature. We consider the imperfect quality production to provide meaningful solutions to practical problems. In this novel model, we assume that the screening is not always perfect, and inspection errors are more likely to take place in practice. The model considers multiple periods, echelons, and uncertainties. Moreover, the proposed robust multi-objective mixed integer linear programming model considers the optimization of three objectives simultaneously. The first objective function is optimized to minimize the total cost of the supply chain. The second objective function seeks to minimize the environmental influence, and the third objective function is optimized to maximize the social benefits. The augmented weighted Tchebycheff method is used to aggregate the objective functions into one objective and produce the set of efficient solutions. Robust optimization, based on the extended Mulvey et al. ( 1995 ) approach, is used to obtain a set of solutions that are robust against the future fluctuation of parameters. Finally, numerical examples have been presented to test and analyze the tradeoff between solution robustness and model robustness.
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ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-021-04286-8