Abstract
This paper addresses a stochastic assembly line balancing problem with flexible task times and zoning constraints. In this problem, task times are regarded as interval variables with given lower and upper bounds. Machines can compress processing times of tasks to improve the line efficiency, but it may increase the equipment cost, which is defined via a negative linear function of task times. Thus, it is necessary to make a compromise between the line efficiency and the equipment cost. To solve this problem, a bi-objective chance-constrained mixed 0–1 programming model is developed to simultaneously minimize the cycle time and the equipment cost. Then, a hybrid Particle swarm optimization algorithm is proposed to search a set of Pareto-optimal solutions, which employs the simulated annealing as a local search strategy. The Taguchi method is used to investigate the influence of parameters, and accordingly a suitable parameter setting is suggested. Finally, the comparative results show that the proposed algorithm outperforms the existing algorithms by obtaining better solutions within the same running time.
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