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