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A novel complex network based dynamic rule selection approach for open shop scheduling problem with release dates

School of Mechanical Engineering, Shanghai Jiao Tong University, 200240, Shanghai, China

Special Issues: Optimization methods in Intelligent Manufacturing

In the open shop scheduling problem, resources and tasks are required to be allocated in an optimized manner, but when the arrival of tasks is dynamic, the problem becomes much more difficult. To solve large scale open shop scheduling problem with release dates, heuristic algorithms are more promising compared with metaheuristic algorithms. In this paper, a framework of general scheduling object is developed, under which open shop scheduling problem is described. Then, a complex scheduling network model for open shop scheduling problem is established, and the problem is transformed into reasonably arranging the node traversal order with the goal of traversing all nodes in the network as quickly as possible, on condition that each node has a traversal time and only disconnected nodes can be traversed simultaneously. Considering that network structural features and local time attributes of nodes can provide heuristic information, six single heuristic rules are raised and a novel complex network based dynamic rule selection approach is proposed to solve dynamic open shop problem by switching dynamically the scheduling rules based on real-time production status. Finally, two experiments are carried out and the experimental results show that the proposed heuristic rules have acceptable performance, and the proposed complex network based dynamic rule selection approach is feasible.
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Keywords complex network; open shop; heuristic rule; node traversal order; dynamic rule selection

Citation: Zilong Zhuang, Zhiyao Lu, Zizhao Huang, Chengliang Liu, Wei Qin. A novel complex network based dynamic rule selection approach for open shop scheduling problem with release dates. Mathematical Biosciences and Engineering, 2019, 16(5): 4491-4505. doi: 10.3934/mbe.2019224

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