COLLABORATIVE PLANNING IN SUPPLY CHAINS BY LAGRANGIAN RELAXATION AND GENETIC ALGORITHMS

Lanshun Nie (), Xiaofei Xu () and Dechen Zhan ()
Additional contact information
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

International Journal of Information Technology & Decision Making (IJITDM), 2008, vol. 07, issue 01, 183-197

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.

Keywords: Supply chain planning; collaborative planning; Lagrangian Relaxation; Genetic Algorithms (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (5)

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DOI: 10.1142/S0219622008002879

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