Paper Info
Title
Collaborative Planning In Supply Chains By Lagrangian Relaxation And Genetic Algorithms
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.
Year
DOI
Venue
2008
10.1142/S0219622008002879
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
Keywords
Field
DocType
supply chain planning, collaborative planning, Lagrangian Relaxation, Genetic Algorithms
Mathematical optimization,Lagrange multiplier,Computer science,Robustness (computer science),Production planning,Supply chain management,Supply chain,Lagrangian relaxation,Private information retrieval,Genetic algorithm
Journal
Volume
Issue
ISSN
7
1
0219-6220
Citations 
PageRank 
References 
1
0.35
3
Authors
3
Name
Order
Citations
PageRank
Lanshun Nie114017.83
Xiaofei Xu240870.26
De-chen Zhan3345.83