A multi-objective optimization algorithm for solving the supplier selection problem with assembly sequence planning and assembly line balancing. (March 2017)

• Record Type: Journal Article
• Authors: Che, Z.H.
• Is Part Of: Computers & industrial engineering. Volume 105(2017)
• Page Start: 247
• Page End: 259
• Publication Date: 2017-03
• Subjects: Supplier selection -- Assembly sequence planning -- Assembly line balancing -- Multi-objective particle swarm optimization; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2016.12.036
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Propose a multi-objective optimization mathematical model for supplier selection. ASP and ALB are introduced into the mathematical model. A MMPSO method based on particle swarm optimization is proposed to solve the model. Provide a case study of a computer assembly plant to illustrate the MMPSO method. Abstract: Supplier selection is a key strategic decision-making activity for building a competitive advantage at an assembly plant. Quality suppliers can understand a firm's operational goals and provide high-quality components. Simultaneously, achieving efficient production requires a production plan. Therefore, a superior competitive strategy should consider the suppliers' availability and the plant's ability. We apply production line planning to address specific problems associated with supplier selection by constructing a multi-objective optimization model. The proposed model considers both assembly sequence planning and assembly line balancing. In addition, a novel hybrid algorithm is proposed to solve the model. The algorithm combines the guided search algorithm and multi-objective particle swarm optimization (MPSO) algorithm, as well as a metic multi-objective particle swarm optimization (MMPSO) algorithm. A real case of a computer assembly plant is used to verify the performance of the MMPSO. The analysis results show that the proposed algorithm not only identifies more non-dominated solutions, but also obtains higher Pareto-optimal solution ratios.