Hybrid genetic algorithm for a type-II robust mixed-model assembly line balancing problem with interval task times

• Published in: Advances in manufacturing Vol. 7; no. 2; pp. 117 - 132
• Main Authors: Zhang, Jia-Hua, Li, Ai-Ping, Liu, Xue-Mei
• Format: Journal Article
• Language: English
• Published: Shanghai Shanghai University 06.06.2019; Springer Nature B.V
• Subjects: Adaptive search techniques; Assembly lines; Balancing; Control; Engineering; Genetic algorithms; Heuristic methods; Machines; Manufacturing; Mechatronics; Nanotechnology and Microengineering; Optimization; Parameter uncertainty; Processes; Robotics; Robustness; Sequential scheduling; Mixed-model assembly line; Genetic algorithm (GA); Uncertainty; Robust optimization; Assembly line balancing
• ISSN: 2095-3127; 2195-3597
• DOI: 10.1007/s40436-019-00256-3

The type-II mixed-model assembly line balancing problem with uncertain task times is a critical problem. This paper addresses this issue of practical significance to production efficiency. Herein, a robust optimization model for this problem is formulated to hedge against uncertainty. Moreover, the counterpart of the robust optimization model is developed by duality. A hybrid genetic algorithm (HGA) is proposed to solve this problem. In this algorithm, a heuristic method is utilized to seed the initial population. In addition, an adaptive local search procedure and a discrete Levy flight are hybridized with the genetic algorithm (GA) to enhance the performance of the algorithm. The effectiveness of the HGA is tested on a set of benchmark instances. Furthermore, the effect of uncertainty parameters on production efficiency is also investigated.