摘要
COVID-19大流行期间,制造企业供应链将面临更为严峻的产品需求不确定性,具体表现为某类产品需求激增且原材料供应波动变大。针对这种具有需求不确定性的供应链优化问题,建立了以企业利润和产品订单满足率最大化为目标函数的多目标随机规划模型,设计了一种基于NSGA-II和仿真计算资源分配策略自适应结合的进化多目标仿真优化算法,通过算例仿真验证了所提出的模型和算法的有效性。
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.
作者
王洪峰
张翼天
陈景泽
Wang Hongfeng;Zhang Yitian;Chen Jingze(College of Information Science and Engineering,Northeastern University,Shenyang 110819,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2021年第12期2761-2770,共10页
Journal of System Simulation
基金
国家重点研发计划(2020YFB1708200)
国家自然科学基金(62173076)
中央直属高校基本科研业务费(N180408019)。
关键词
供应链
多目标优化
随机规划
多目标进化算法
仿真计算量分配
supply chain
multi-objective optimization
stochastic programming
multi-objective evolutionary algorithm
simulation computing budget allocation
