Minimizing Expected Cycle Time of Stochastic Customer Orders Through Bounded Multi-Fidelity Simulations

作者：Zhao, Yaping; Xu, Xiaoyun^*; Li, Haidong

来源：IEEE Transactions on Automation Science and Engineering, 2018, 15(4): 1797-1809.

DOI：10.1109/TASE.2018.2796090

• Customer order
• multifidelity simulation
• production planning
• stochastic scheduling

This paper considers the scheduling of stochastic customer orders to minimize expected cycle time. Customer orders dynamically arrive at a machine station, and each order consists of multiple product types. Random workloads are required by each product type, and the workloads are assigned to a set of unrelated parallel machines in the station to be processed. The objective is to obtain the minimal long-run expected order cycle time through proper workload assignments. In view of the difficulty in evaluating the objective function, this paper models the targeted problem as a simulation optimization problem and proposes to solve it under the multifidelity model framework. To improve the efficiency in evaluating candidate solutions, a low-fidelity model is constructed to select solutions with better performances for high-fidelity simulations. The effectiveness of this low-fidelity model is demonstrated through a series of theoretical evidences. A simulation optimization algorithm, named Bound-Multi-fidelity Optimization with Ordinal Transformation and Optimal Sampling Bound-(MOTOS)-T-2, is developed by taking advantage of the properties of the low-fidelity model. Numerical experiments are conducted to evaluate the performance of the proposed algorithm against three other well-known simulation optimization algorithms in the literature. Results indicate that the BoundMO(2)TOS outperforms all the other tested algorithms, and its performance is robust against changes in problem scale.

• 出版日期2018-10
• 单位北京大学