A PSO algorithm for multi-objective hull assembly line balancing using the stratified optimization strategy

• Published in: Computers & industrial engineering Vol. 98; pp. 53 - 62
• Main Authors: Yuguang, Zhong, Bo, Ai, Yong, Zhan
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.08.2016; Pergamon Press Inc
• Subjects: Algorithms; Assembly line balancing problem; Assembly lines; Computer simulation; Effectiveness studies; Genetic algorithms; Hulls; Hulls (structures); Operations research; Optimization; Optimization algorithms; Production scheduling; PSO algorithm; Scheduling algorithms; Shipbuilding industry; Stratified optimization; Work stations; Workstations; Stratified optimization; Shipbuilding industry; Assembly line balancing problem; PSO algorithm
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2016.05.026

•A novel mathematical model is proposed for hull assembly line balancing problem.•An improved discrete particle swarm optimization algorithm is provided.•A stratified optimization strategy is applied by the importance of different objectives. Hull assembly line (HAL) is a mixed-model assembly line on which different hull blocks can be assembled at the same time. Aiming at the balance problem of HAL, minimizing the cycle time, minimizing the static load balancing between workstations, minimizing the dynamic load balancing in all workstations, and minimizing the multi-station associated complexity are considered as optimization objectives. An improved discrete particle swarm optimization (IDPSO) algorithm based on the stratified optimization idea is developed for scheduling of the multi-objective problem. In the proposed algorithm, particles were coded by a two-dimensional task-oriented representation method, and then collaboration and competition of particle individuals are simulated by crossover and mutation operators in the genetic algorithm (GA). The performance of the proposed hybrid algorithm is examined over several test problems in terms of solution quality and running time. Finally, a practical case is used to analyze the effectiveness and feasibility of the stratified scheduling strategy.