Application of Two Improved Particle Swarm Algorithms in a Flexible Assembly Job Shop Scheduling Problem

Abstract

Process planning and scheduling in assembly job shops plays a significant role in enhancing production efficiency and reducing cost of manufacturing systems. However, most existing research on the assembly job shop scheduling problem (AJSSP) is based on assumption that operations are not allowed after the assembly process. Moreover flexibilities in assembly job shop are not fully considered. There are few researches focusing on the flexible assembly job shop which allows operations after assembly. In this paper, a mathematical model is proposed to describe the FAJSSP. To minimize the maximum completion time in the flexible assembly job shop, this paper presents two hybrid algorithms based on particle swarm optimization (PSO), genetic algorithm (GA) and simulated annealing algorithm (SA) called DPSO and IPSO. Numerical experiments are conducted using the realistic production data, and the results of different methods are compared with the realistic completion time.