Journal of System Simulation ›› 2021, Vol. 33 ›› Issue (12): 2761-2770.doi: 10.16182/j.issn1004731x.joss.21-0837

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An Evolutionary Multi-Objective Simulation Optimization Algorithm for Supply Chain with Uncertain Demands

Wang Hongfeng, Zhang Yitian, Chen Jingze   

  1. College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
  • Received:2021-08-18 Revised:2021-11-17 Online:2021-12-18 Published:2022-01-13

Abstract: During the COVID-19 pandemic, supply chain of manufacturing companies is facing more severe product demand uncertainty, which is manifested in the sharp increase in demand for certain types of products and the increased fluctuations in supply for raw materials. For this supply chain optimization problem with demand uncertainty, a multi-objective stochastic programming model is developed in order to maximize the total profit and product order fulfillment rate simultaneously in this paper. For solving the investigated problem, a new evolutionary multi-objective simulation optimization algorithm is proposed by combining the mechanism of NSGA-II and simulation computing budget allocation adaptively. Experimental results show the validity of the presented model and algorithm.

Key words: supply chain, multi-objective optimization, stochastic programming, multi-objective evolutionary algorithm, simulation computing budget allocation

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