Evaluation of scatter-search approach for scheduling optimization of flexible manufacturing systems

Many optimization problems from the industrial engineering world (in particular manufacturing systems) are very complex in nature and quite hard to solve by conventional optimization techniques. There has been increasing interest to apply meta-heuristic methods to solve such kinds of hard optimization problems. In this work, a meta-heuristic approach called scatter-search (SS) is applied for scheduling optimization of flexible manufacturing systems by considering multiple objectives, i.e., minimizing the idle time of the machine and minimizing the total penalty cost for not meeting the due date concurrently. Scatter search (SS) contrasts with other evolutionary procedures in that it provides a wide exploration of the search space through intensification and diversification. In addition, it has a unifying principle for joining solutions and they exploit adaptive memory principle to avoid generating or incorporating duplicate solutions at various stages of the problem. In this paper, various meta-heuristic methods are used for solving three different sizes of scheduling problems taken from the literature. The results available for the various existing meta-heuristic methods are compared with results obtained by the scatter-search method. The proposed framework achieves better results for all the three problems and also achieves an average deviation of 16.67% from the best results obtained by other methods.