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A Discrete Artificial Bee Colony Algorithm for the Reverse Logistics Location and Routing Problem

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  • Kun Guo

    (College of Mathematics and Computer Science, Fuzhou University, 2 Xue Yuan Road, Fuzhou Fujian 350108, China)

  • Qishan Zhang

    (School of Management, Fuzhou University, 2 Xue Yuan Road, Fuzhou Fujian 350108, China)

Abstract

Reverse logistics (RL) emerges as a hot topic in both research and business with the increasing attention on the collection and recycling of the waste products. Since Location and Routing Problem (LRP) in RL is NP-complete, heuristic algorithms, especially those built upon swarm intelligence, are very popular in this research. In this paper, both Vehicle Routing Problem (RP) and Location Allocation Problem (LAP) of RL are considered as a whole. First, the features of LRP in RL are analyzed. Second, a mathematical model of the problem is developed. Then, a novel discrete artificial bee colony (ABC) algorithm with greedy adjustment is proposed. The experimental results show that the new algorithm can approach the optimal solutions efficiently and effectively.

Suggested Citation

  • Kun Guo & Qishan Zhang, 2017. "A Discrete Artificial Bee Colony Algorithm for the Reverse Logistics Location and Routing Problem," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(05), pages 1339-1357, September.
    • Handle: RePEc:wsi:ijitdm:v:16:y:2017:i:05:n:s0219622014500126
    DOI: 10.1142/S0219622014500126
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    References listed on IDEAS

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    1. Abdel-Rahman Hedar & Emad Mabrouk & Masao Fukushima, 2011. "Tabu Programming: A New Problem Solver Through Adaptive Memory Programming Over Tree Data Structures," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 10(02), pages 373-406.
    2. Lanshun Nie & Xiaofei Xu & Dechen Zhan, 2008. "Collaborative Planning In Supply Chains By Lagrangian Relaxation And Genetic Algorithms," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 7(01), pages 183-197.
    3. David Peidro & Josefa Mula & Raúl Poler, 2010. "Fuzzy Linear Programming For Supply Chain Planning Under Uncertainty," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 9(03), pages 373-392.
    4. Fengmei Yang & Guowei Hua & Hiroshi Inoue & Jianming Shi, 2006. "Two Bi-Objective Optimization Models For Competitive Location Problems," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 5(03), pages 531-543.
    5. G. Reza Nasiri & Hamid Davoudpour & Behrooz Karimi, 2010. "A Lagrangian-Based Solution Algorithm For Strategic Supply Chain Distribution Design In Uncertain Environment," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 9(03), pages 393-418.
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    Cited by:

    1. Selcuk Aslan, 2020. "An Artificial Bee Colony-Guided Approach for Electro-Encephalography Signal Decomposition-Based Big Data Optimization," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 19(02), pages 561-600, April.

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