Multi-phase integrated scheduling of hybrid tasks in cloud manufacturing environment |
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Affiliation: | 1. School of Automation Science and Electrical Engineering, and Beijing Advanced Innovation Center for Big Data-based Precision Medicine, Beihang University, Beijing 100191, China.;2. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.;1. Beijing Information Science and Technology University (BISTU), Beijing 100101, China;2. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China;1. School of Mechatronics Engineering, Harbin Institute of Technology, Dazhi 92, 150001, Harbin, China;1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;2. Department of Mechanical & Industrial Engineering, Louisiana State University, Baton Rouge, LA 70803, USA |
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Abstract: | Cloud manufacturing paradigm aims at gathering distributed manufacturing resources and enterprises to serve for more customized production. Production order which involving several tasks can be taken by distributed suppliers collaboratively at lower cost. The cloud manufacturing platform is responsible for not only arranging reasonable priorities, suitable suppliers, and production processes to multiple orders, but also scheduling hybrid tasks from different orders to manufacturing resources. To maximize the production efficiency and balance the trade-off among different production orders, this paper studies multi-phase integrated scheduling of hybrid tasks in cloud manufacturing environment, which containing order priority assignment, supplier and production process selection, and production line scheduling. Five key objectives are taken into account to analyze the interconnections among different resources and production processes. Six representative multi-objective evolutionary algorithms are adopted to solve the integrated scheduling problem. Experimental results on six production cases show that integrated scheduling is more effective than the traditional step-by-step decision, leading to less production cost and time. In addition, a comparison among the six algorithms is carried out to determine the one best suited for the integrated scheduling problem in different circumstances. |
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