An effective hybrid evolutionary algorithm for stochastic multiobjective assembly line balancing problem

Stochastic assembly line balancing distributes tasks with uncertain processing times at each station so that precedence relationship constraints are satisfied and a given objective function is optimized. In real assembly line balancing systems, the stochastic, multiobjective, assembly line balancing (S-MoALB) problem is an important and practical issue involving conflicting criteria, such as cycle time, processing cost, and/or variation of workload. In this paper, we propose an effective hybrid evolutionary algorithm (hEA) to solve an S-MoALB problem involving the minimization of cycle time and processing cost for a fixed number of stations. The hEA implements a simple mechanism to select Pareto optimal solutions between the Pareto-dominating and dominated relationship-based fitness function and the vector evaluated genetic algorithm to enhance the convergence and distribution performance. The experimental results show that our hEA achieves better convergence and distribution performance than two typical multiple objective genetic algorithms such as the non-dominated sorting genetic algorithm-II and the strength Pareto evolutionary algorithm 2.