An Improved Differential Evolution Algorithm for a Multicommodity Location-Inventory Problem with False Failure Returns

Customer returns are a common phenomenon in many industries, and they have a significant impact on business organizations and their supply chains. False failure returns are returned products that have no functional or cosmetic defects, and they represent a large body of customer returns in practice....

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Bibliographic Details
Published in:Complexity (New York, N.Y.) Vol. 2018; no. 2018; pp. 1 - 13
Main Authors: Deng, Shuai, Zhang, Ying, Guo, Hao, Li, Congdong, Wang, Yu
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018
Hindawi
John Wiley & Sons, Inc
Wiley
Subjects:
Algorithms
Computer peripherals industry
Electronic commerce
Evolutionary algorithms
Failure
Heuristic
Heuristic methods
Industrial engineering
Integer programming
Inventory
Inventory control
Inventory management
Logistics
Management decisions
Management science
Mathematical models
Nonlinear programming
Operations research
Retailing industry
Reverse logistics
Suppliers
Supply chains
United States
ISSN:1076-2787, 1099-0526
Online Access:Get full text
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Summary:Customer returns are a common phenomenon in many industries, and they have a significant impact on business organizations and their supply chains. False failure returns are returned products that have no functional or cosmetic defects, and they represent a large body of customer returns in practice. In this paper, we develop a mixed-integer nonlinear programming model to study a multicommodity location-inventory problem in a forward-reverse logistics network. This model minimizes the total cost in this network by considering false failure returns, and it also considers many real-world business scenarios in forward and reverse logistics flows. Moreover, we design a new heuristic approach to solve the model efficiently. Finally, numerical experiments are conducted to validate our solution approach and provide meaningful managerial insights.
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ISSN:1076-2787
1099-0526
DOI:10.1155/2018/1967398