Low-carbon joint scheduling in flexible open-shop environment with constrained automatic guided vehicle by multi-objective particle swarm optimization

Abstract

Reduction of carbon emissions is receiving growing attention in manufacturing industry. In the real-life flexible open-shop manufacturing environments, the workpieces generally need frequent transport between machines so that the limitation of the transport resource is non-negligible. Joint scheduling is a better way to achieve global optimum than sequential scheduling due to the interactive relationship between production and transport. However, owing to the extreme complexity, joint scheduling has received little attention over the past years. In this paper, the low-carbon joint scheduling in flexible open-shop environment with constrained automatic guided vehicle (LCJS-FOSCA) is investigated. A mixed-integer programming (MIP) model is formulated with the objectives to minimize the total carbon emission and makespan. Facing the extreme complexity of LCJS-FOSCA, an enhanced multi-objective particle swarm optimization (EMOPSO) is developed. In EMOPSO, several improvement strategies including the initialization method for high-quality solutions, chaotic position updating and mutation for global exploration, and problem-knowledge-based neighborhood search for local exploitation, are proposed. Finally, the following results are obtained through a comprehensive case study: (1) the proposed strategies make significant promotion on the convergence and comprehensive quality, and EMOPSO is verified to be effective for solving LCJS-FOSCA; (2) compared with the two sequential scheduling methods, the makespan obtained by joint scheduling is reduced by approximately 27.7% and 25.5%, and the total carbon emission is reduced by 3.3% and 4.2%; (3) the optimal numbers of AGV in various scales of transport time are provided based on the analysis of optimization results. This work presents a significance on promoting cleaner production of the modern manufacturing industry.