Integrated partner selection and production–distribution planning for manufacturing chains

•A model is developed to describe the characteristics of the manufacturing chain.•A hybrid algorithm combining PSO and GA with learning scheme has been developed.•A simple example is used to illustrate the approach.•Performance of the approach is evaluated by solving a set of test problems.•Analysis...

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Bibliographic Details
Published in:Computers & industrial engineering 2015-06, Vol.84, p.32-42
Main Authors: Su, W., Huang, S.X., Fan, Y.S., Mak, K.L.
Format: Article
Language:English
Subjects:
Algorithms
Decision analysis
Design optimization
Distribution planning
Genetic algorithm
Genetic algorithms
Hybrid algorithm
Integrated approach
Learning
Manufacturing
Manufacturing chain
Markets
Optimization
Optimization algorithms
Particle swarm optimization
Partnerships
Production and distribution planning
Production planning
Studies
Swarm intelligence
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Summary:•A model is developed to describe the characteristics of the manufacturing chain.•A hybrid algorithm combining PSO and GA with learning scheme has been developed.•A simple example is used to illustrate the approach.•Performance of the approach is evaluated by solving a set of test problems.•Analysis of results indicates that the proposed approach is efficient and effective. This paper presents an integrated approach to solve the partner selection, and production–distribution planning problem in the design of manufacturing chains operating under a multi-product, multi-stage, multi-production route, multi-machine, and multi-period manufacturing environment. Such a problem often occurs when manufacturing companies and material suppliers establish partnerships to form a virtual enterprise, a manufacturing chain, in which its members cooperate to capture rising market opportunities. An optimization model and a hybrid algorithm which combines particle swarm optimization and genetic algorithm with learning scheme are developed to derive the optimal decisions. The performance of the developed approach is illustrated by using a simple case problem and a set of randomly generated test problems. Indeed, it is shown that the proposed hybrid algorithm can outperform the conventional genetic algorithm, the particle swarm optimization and the genetic algorithm with learning scheme, and is therefore an excellent tool for designing optimal manufacturing chains.
ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2015.01.015