A multiple single-pass heuristic algorithm solving the stochastic assembly line rebalancing problem

Abstract

Assembly line rebalancing is a problem companies are frequently confronted with as continuous changes in product features and volume demand caused by the volatility of modern markets result in re-definition of assembly tasks and line cycle time fluctuations. Consequently, managers are forced to adjust the balancing of their lines in order to adapt to the new conditions while trying to minimise both increases in completion costs and costs related to changes in task assignment. In particular, when modifications are made to line balancing, costs are incurred for operator training, equipment switching and moving, and quality assurance. The stochastic assembly line rebalancing problem is essentially composed of a multi-objective problem in which two joint objectives, total expected completion cost of the new line and similarity between the new and the existing line, must be optimised. Consequently, this paper presents a multiple single-pass heuristic algorithm developed for the purpose of finding the most complete set of dominant solutions representing the Pareto front of the problem. The operative parameters of the heuristic are set as a result of a great deal of experimentation. Moreover, a multi-objective genetic algorithm is developed and then compared with the proposed heuristic in order to demonstrate its effectiveness. Finally, an illustrative case study is presented.