Affiliations:
College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin, China
Correspondence:
[*]
Corresponding author. Yu-Guang Zhong, College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China. Tel.: +86 13100967565; Fax: +86 0451 82519797; E-mail: [email protected].
Abstract: To ensure effective hull assembly line production, it is vital to consider the problems on delivery date of order and sequencing complexity within mixed-model production environments. In this paper two criteria are considered for stratified optimization according to their importance: to minimize the satisfaction ratio of delivery date, and to minimize the complexity degree of the system arising from its state frequent changes. Finding an optimal solution for this complicated problem in reasonable computational time is cumbersome. Therefore, this paper presents an improved particle swarm optimization (IPSO) algorithm to solve the multi-objective sequencing problems. Instead of modeling the positions of particles in a continuous value manner in traditional PSO, IPSO uses an encoding and decoding scheme of task-oriented assignment for representing the discrete input sequences of products. Furthermore, dynamic mutation operator and chaos strategy are introduced to help the particles escape from local optima and the strategy for population decomposition is proposed to further improve the efficiency of the optimization. Numerical simulation suggests that the proposed IPSO scheduler can provide obvious improvement on solution quality and running time. Finally, a case study of the optimization of a panel block assembly line was given to illustrate the effectiveness of the method.
Keywords: Improved particle swarm optimization, multi-objective, satisfaction degree of delivery date, manufacturing complexity, hull mixed model assembly line sequencing
