Multi-phase integrated scheduling of hybrid tasks in cloud manufacturing environment

• Published in: Robotics and computer-integrated manufacturing 2020-02, Vol.61, p.101850, Article 101850
• Main Authors: Laili, Yuanjun, Lin, Sisi, Tang, Diyin
• Format: Article
• Language: English
• Subjects: Algorithms; Cloud manufacturing; Evolutionary algorithms; Integrated scheduling; Manufacturing; Multi-objective optimization; Multiphase; Multiple objective analysis; Order priority assignment; Process selection; Production costs; Production line scheduling; Production planning; Production scheduling; Supplier and process selection; Suppliers; Task scheduling
• ISSN: 0736-5845; 1879-2537
• DOI: 10.1016/j.rcim.2019.101850

•Three problems including order assignment, supplier selection, and production line scheduling are jointly considered for hybrid manufacturing processes.•The task queuing time, resource idle time, and in-production transportation time are modeled to assess the overall time and cost for each production order.•Six multi-objective evolutionary algorithms are applied and compared to determine the one best suited for integrated problem.•Experimental results show that integrated scheduling is more suitable for hybrid task compared than traditional step-by-step decision. 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.