Li Tao
Associate Professor Supervisor of Doctorate Candidates Supervisor of Master's Candidates
Gender:Female
Alma Mater:Harbin Institute of Technology
Degree:Doctoral Degree
School/Department:School of Mechanical Engineering
Discipline:Mechanical Design and Theory. Intelligent Manufacturing. Mechanical Manufacture and Automation. Mechanical Engineering. Intelligent Manufacturing Technology
Business Address:8027 room, School of Mechanical Engineering building
E-Mail:
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Date:2019-10-19
Indexed by:Journal Article
Date of Publication:2019-08-01
Journal:ROBOTICS AND COMPUTER-INTEGRATED MANUFACTURING
Included Journals:EI、SCIE
Volume:58
Page Number:208-219
ISSN:0736-5845
Key Words:Remanufacturing scheduling; Petri net; A(star) algorithm; Engine remanufacturing; Remanufacturing uncertainty
Abstract: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.
Ph.D, associate professor, doctoral supervisor, graduated from Harbin Institute of technology, mainly engaged in product sustainability evaluation methods, laser repair technology, mechanical equipment energy consumption analysis and evaluation, enterprise information technology development and application. Reviewers of many domestic and foreign journals in related fields, presided over or participated in more than 20 projects of national key research and development plan, national 973 program, national Natural Science Foundation of China and enterprises, and published more than 100 papers. Ph.D Li has trained or assisted in the training of more than 50 doctoral and master degree students.
