Integrated partner selection and production–distribution planning for manufacturing chains. (June 2015)

• Record Type: Journal Article
• Authors: Su, W.; Huang, S.X.; Fan, Y.S.; Mak, K.L.
• Is Part Of: Computers & industrial engineering. Volume 84(2015)
• Page Start: 32
• Page End: 42
• Publication Date: 2015-06
• Subjects: Manufacturing chain -- Production and distribution planning -- Hybrid algorithm -- Genetic algorithm -- Particle swarm optimization; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2015.01.015
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: 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. Abstract: 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.