Collaborative Manufacturing Unit Selection Using a Sorting Adaptive Genetic Algorithm in Networked Environment

Abstract

In order to solve the collaborative manufacturing unit (CMU) selection problem in the networked cooperative manufacturing environment, a sorting adaptive genetic algorithm is proposed. To obtain the optimal executive manufacturing process, the objective function is constructed considering manufacturing cost and product load rate of candidate CMUs under time-sequence constraint. The embedded subtask scheduling procedure in sorting adaptive genetic algorithm is used to ascertain the penalty cost for the tardiness of the task. Finally, a case study is implemented to verify the feasibility of the proposed approach. The results show that the proposed model and algorithm can obtain satisfactory solutions.