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Collaborative Planning In Supply Chains By Lagrangian Relaxation And Genetic Algorithms

Author

Listed:
  • LANSHUN NIE

    (School of Computer Science and Technology, Harbin Institute of Technology, P. O. Box 315, 92 Xi Dazhi Street, Nangang District, Harbin 150001, China)

  • XIAOFEI XU

    (School of Computer Science and Technology, Harbin Institute of Technology, P. O. Box 315, 92 Xi Dazhi Street, Nangang District, Harbin 150001, China)

  • DECHEN ZHAN

    (School of Computer Science and Technology, Harbin Institute of Technology, P. O. Box 315, 92 Xi Dazhi Street, Nangang District, Harbin 150001, China)

Abstract

A collaborative planning framework combining the Lagrangian Relaxation method and Genetic Algorithms is developed to coordinate and optimize the production planning of the independent partners linked by material flows in multiple tier supply chains. Linking constraints and dependent demand constraints were added to the monolithic Multi-Level, multi-item Capacitated Lot Sizing Problem (MLCLSP) for supply chains. Model MLCLSP was Lagrangian relaxed and decomposed into facility-separable sub-problems based on the separability of it. Genetic Algorithms was incorporated into Lagrangian Relaxation method to update Lagrangian multipliers, which coordinated decentralized decisions of the facilities in supply chains. Production planning of independent partners could be appropriately coordinated and optimized by this framework without intruding their decision authorities and private information. This collaborative planning schema was applied to a large set problem in supply chain production planning. Experimental results show that the proposed coordination mechanism and procedure come close to optimal results as obtained by central coordination in terms of both performance and robustness.

Suggested Citation

  • 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.
    • Handle: RePEc:wsi:ijitdm:v:07:y:2008:i:01:n:s0219622008002879
    DOI: 10.1142/S0219622008002879
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    Cited by:

    1. 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.
    2. G. Rius-Sorolla & J. Maheut & S. Estellés-Miguel & J. P. Garcia-Sabater, 2020. "Coordination mechanisms with mathematical programming models for decentralized decision-making: a literature review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 61-104, March.
    3. Guodong Yu & Li Zhang & Huiping Sun, 2018. "A Method for Partner Selection of Supply Chain Using Interval-Valued Fuzzy Sets — Fuzzy Choquet Integral and Improved Dempster–Shafer Theory," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 17(06), pages 1777-1804, November.
    4. Gregorio Rius-Sorolla & Julien Maheut & Sofia Estelles-Miguel & Jose P. Garcia-Sabater, 2021. "Collaborative Distributed Planning with Asymmetric Information. A Technological Driver for Sustainable Development," Sustainability, MDPI, vol. 13(12), pages 1-23, June.
    5. Surafel Luleseged Tilahun & Hong Choon Ong, 2015. "Prey-Predator Algorithm: A New Metaheuristic Algorithm for Optimization Problems," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 14(06), pages 1331-1352, November.

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