Tri-objective based scalable balancing of reconfigurable manufacturing systems subject to resource constraints and task processing time uncertainty
研究可重构制造系统在资源有限和任务时间不确定下，如何通过多目标优化找到最具扩展潜力的初始配置，提出三目标超体积作为可扩展性指标，并用模拟退火算法求解。
This article studies reconfigurable manufacturing systems and their ability to efficiently evolve (or scale) through the use of a limited number of additional resources to improve two analysed system characteristics: productivity and robustness against task processing time uncertainty. The goal is to find an initial system configuration (or balancing) representing the best potential for scalability. To this end, we propose a multi-step based multi-objective approach, where productivity and robustness are considered as two objectives to be maximised and the number of used additional resources as the third objective to be minimised. Thus, for a given balancing, we first explore the set of its non-dominated (in the sense of Pareto) configurations, reflecting relevant possibilities for additional use of resources. Then, we calculate its corresponding tri-objective hyper-volume and suggest to consider it as a scalability metric. Finally, to find the most scalable balancing, we develop an efficient simulated annealing algorithm, where the proposed metric is used as a fitness function. The algorithm is tested and analysed on small- and medium-sized instances, extracted and adapted from the well-known benchmark dataset. The numerical results obtained demonstrate the stability and efficiency of the developed algorithm and confirm the relevance of the proposed scalability metric.