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论文类型：期刊论文

发表时间：2019-08-01

发表刊物：ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING

收录刊物：SCIE、EI

卷号：58

页面范围：208-219

ISSN号：0736-5845

关键字：Remanufacturing scheduling; Petri net; A(star) algorithm; Engine remanufacturing; Remanufacturing uncertainty

摘要：Scheduling has been extensively applied to remanufacturing for the organization of production activities, and it would directly influence the overall performance of the remanufacturing system. Since the conjunction of Petri net (PN) and artificial intelligence (AI) searching technique was demonstrated to be a promising approach to solve the scheduling problems in manufacturing systems, this study built a transition timed PN combined with heuristic A(star) algorithm to deal with the scheduling in remanufacturing. The PN was applied to the formulation of remanufacturing process, while the A(star) algorithm generated and searched for an optimal or near optimal feasible schedule through the reachability graph (RG). We took the high value-added cylinder block of engine as a research object to minimize the makespan of reprocessing a batch used components. This scheduling problem involved in batch and parallel processing machines, and the uncertain processing time and routes will complicate the scheduling problem. Three heuristics were designed to guide the search process through the RG in PN. To avoid state space explosion and select promising nodes, a new rule-based dynamic window was developed to improve the efficiency of the algorithm, and this rule was examined to outperform the conventional one. Under the determined scheduling strategy, the dynamic behavior of energy consumption rate during the processing time was simulated using PN tool, which would assist remanufacturers to develop potential strategies for energy efficiency improvement. Considering the uncertainty of processing time, the Monte Carlo simulation method was adopted to statistically analyze the distributions of makespan and total energy consumption, which would contribute to the comprehensive production scheduling and energy profile assessment for sustainable re manufacturing.