Integrated partner selection and production-distribution planning for manufacturing chains

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.