Multi-Objective brain storm optimization for integrated scheduling of distributed flow shop and distribution with maximal processing quality and minimal total weighted earliness and tardiness

At present, integrated production and distribution scheduling problems have obtained amounts of concern due to their essential roles in enhancing the supply chain performance. Along with the economic globalization, many manufacturing enterprises adopt distributed production structures to reduce operation costs and improve service quality. Besides, processing quality becomes a fundamental standard for manufacturing enterprises to keep their competitiveness. In the distributed production and distribution process, the jobs are first produced on machines in different factories at the production stage, and then shipped to their associated customers using vehicles at the distribution stage. To realize an overall optimization of these two stages, this work investigates a multi-objective integrated distributed flow shop and distribution scheduling problem to reach maximal processing quality and minimal total weighted earliness and tardiness. To define it mathematically, a mixed integer programming model is established. Given the problem's NP-hard nature, a multi-objective brain storm optimization algorithm is addressed to settle it. Through performing numerical experiments and statistical tests on some test instances, the effectiveness of the developed method is demonstrated..

Automated summary

Currently, integrated production and distribution scheduling problems are receiving considerable attention due to their crucial roles in improving supply chain performance. This study investigates a multi-objective integrated distributed flow shop and distribution scheduling problem to maximize processing quality and minimize total weighted earliness and tardiness. A mathematical model is established and a multi-objective brain storm optimization algorithm is proposed to solve the NP-hard problem. Numerical experiments and statistical tests confirm the effectiveness of the proposed method.