Multiobjective Differential Evolution Algorithm for Solving Robotic Cell Scheduling Problem With Batch-Processing Machines
Robotic cell scheduling problem with batch-processing machines (RCSP-BMs) needs to determine the processing sequence and the transferring sequence simultaneously. The buffer size before and after the batch-processing machines has a big influence on the scheduling solution. A big amount of energy is...
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| Vydáno v: | IEEE transactions on automation science and engineering Ročník 18; číslo 2; s. 757 - 775 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
IEEE
01.04.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1545-5955, 1558-3783 |
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| Abstract | Robotic cell scheduling problem with batch-processing machines (RCSP-BMs) needs to determine the processing sequence and the transferring sequence simultaneously. The buffer size before and after the batch-processing machines has a big influence on the scheduling solution. A big amount of energy is always consumed by batch-processing machines. Hybrid flow shop scheduling has been proven NP-hard, and the features of the batch-processing machines in a flow shop make the hybrid flow shop scheduling more difficult. This study proposes a multiobjective differential evolution (DE) algorithm to address these issues. First, a mathematical optimization model is formulated for the RCSP-BMs to minimize makespan and energy consumption of the batch-processing machines. Second, the multiobjective DE algorithm (MODE) is developed. A green scheduling algorithm is designed to decode the individuals to balance the makespan and energy consumption. A local search method is also presented to help the searching escape from the local optimum. Finally, experiments are carried out, and the results show that the MODE can solve the robotic cell scheduling problem with batch-processing machines effectively and efficiently. Note to Practitioners -This study focuses on the robotic cell scheduling problem with batch-processing machines (RCSP-BMs) and discusses the influence of the buffer sizes and different batching methods on scheduling. In this study, we propose a green scheduling algorithm and a multiobjective differential evolution algorithm to optimize the makespan and the energy consumption of the batch-processing machines simultaneously. In future research, we will address more complicated situations, such as many-objective optimization and many-robot scheduling. |
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| AbstractList | Robotic cell scheduling problem with batch-processing machines (RCSP-BMs) needs to determine the processing sequence and the transferring sequence simultaneously. The buffer size before and after the batch-processing machines has a big influence on the scheduling solution. A big amount of energy is always consumed by batch-processing machines. Hybrid flow shop scheduling has been proven NP-hard, and the features of the batch-processing machines in a flow shop make the hybrid flow shop scheduling more difficult. This study proposes a multiobjective differential evolution (DE) algorithm to address these issues. First, a mathematical optimization model is formulated for the RCSP-BMs to minimize makespan and energy consumption of the batch-processing machines. Second, the multiobjective DE algorithm (MODE) is developed. A green scheduling algorithm is designed to decode the individuals to balance the makespan and energy consumption. A local search method is also presented to help the searching escape from the local optimum. Finally, experiments are carried out, and the results show that the MODE can solve the robotic cell scheduling problem with batch-processing machines effectively and efficiently. Note to Practitioners —This study focuses on the robotic cell scheduling problem with batch-processing machines (RCSP-BMs) and discusses the influence of the buffer sizes and different batching methods on scheduling. In this study, we propose a green scheduling algorithm and a multiobjective differential evolution algorithm to optimize the makespan and the energy consumption of the batch-processing machines simultaneously. In future research, we will address more complicated situations, such as many-objective optimization and many-robot scheduling. |
| Author | Wu, Xiuli Yuan, Qi Wang, Ling |
| Author_xml | – sequence: 1 givenname: Xiuli surname: Wu fullname: Wu, Xiuli email: wuxiuli@ustb.edu.cn organization: School of Mechanic Engineering, University of Science and Technology Beijing, Beijing, China – sequence: 2 givenname: Qi orcidid: 0000-0003-3288-9548 surname: Yuan fullname: Yuan, Qi email: yuanqiustb@163.com organization: School of Mechanic Engineering, University of Science and Technology Beijing, Beijing, China – sequence: 3 givenname: Ling orcidid: 0000-0003-1226-2801 surname: Wang fullname: Wang, Ling email: wangling@mail.tsinghua.edu.cn organization: Department of Automation, Tsinghua University, Beijing, China |
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| SubjectTerms | Algorithms Batch-processing scheduling buffer size Buffers Collision avoidance Energy consumption Evolutionary algorithms Evolutionary computation green scheduling algorithm hybrid flow shop scheduling Job shop scheduling multiobjective differential evolution (DE) algorithm Multiple objective analysis Optimization robotic cell Robotics Robots Scheduling Scheduling algorithms smart manufacturing system |
| Title | Multiobjective Differential Evolution Algorithm for Solving Robotic Cell Scheduling Problem With Batch-Processing Machines |
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