Mufti-objective balancing of assembly lines by population heuristics

This paper is concerned with the solution of the mufti-objective single-model deterministic assembly line balancing problem (ALBP). Two bi-criteria objectives are considered: 1. Minimising the cycle time of the assembly line and the balance delay time of the workstations. 2. Minimising the cycle time and the smoothness index of the workload of the line. A new population heuristic is proposed to solve the problem based on the general differential evolution (DE) method. The main characteristics of the proposed mufti-objective DE (MODE) heuristic are: a. It formulates the cost function of each individual ALB solution as a weighted-sum of multiple objectives functions with self-adapted weights. b. It maintains a separate population with diverse Pareto-optimal solutions. c. It injects the actual evolving population with some Pareto-optimal solutions. d. It uses a new modified scheme for the creation of the mutant vectors. Moreover, special representation and encoding schemes are developed and discussed which adapt MODE on ALBPs. The efficiency of MODE is measured over known ALB benchmarks taken froth the open literature and compared to that of two other previously proposed population heuristics, namely, a weighted-sum Pareto genetic algorithm (GA), and a Pareto-niched GA. The experimental comparisons showed a promising high quality performance for MODE approach.