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. (May 2023)

• Record Type: Journal Article
• Authors: Hou, Yushuang; Wang, Hongfeng; Fu, Yaping; Gao, Kaizhou; Zhang, Hui
• Is Part Of: Computers & industrial engineering. Volume 179(2023)
• Publication Date: 2023-05
• Subjects: Production and distribution -- Distributed flow shop -- Processing quality -- Multi-objective optimization -- Brain storm optimization; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2023.109217
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: An integrated distributed flow shop and distribution scheduling problem is studied. A multi-objective mixed integer programming model is provided to define the problem. A multi-objective brain storm optimization algorithm is introduced. Optimal solutions are achieved through employing a mathematical programming optimizer. State-of-the-art results are obtained by the developed method. Abstract: 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..