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

Authors

Hongfeng Wang, College of Information Science and Engineering, Northeastern University, Shenyang 110819, China;
Yitian Zhang, College of Information Science and Engineering, Northeastern University, Shenyang 110819, China;
Jingze Chen, College of Information Science and Engineering, Northeastern University, Shenyang 110819, China;

Abstract

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.

Recommended Citation

Wang, Hongfeng; Zhang, Yitian; and Chen, Jingze (2022) "An Evolutionary Multi-Objective Simulation Optimization Algorithm for Supply Chain with Uncertain Demands," Journal of System Simulation: Vol. 33: Iss. 12, Article 1.
DOI: 10.16182/j.issn1004731x.joss.21-0837
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss12/1

First Page

2761

Revised Date

2021-11-17

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-0837

Last Page

2770

CLC

TP391.9

Recommended Citation

Wang Hongfeng, Zhang Yitian, Chen Jingze. An Evolutionary Multi-Objective Simulation Optimization Algorithm for Supply Chain with Uncertain Demands[J]. Journal of System Simulation, 2021, 33(12): 2761-2770.

DOI

10.16182/j.issn1004731x.joss.21-0837