摘要
本文针对整车物流网络优化问题,建立低碳经济下的鲁棒优化模型,设计遗传退火算法对模型进行求解,并通过算例仿真验证了模型和算法的有效性。研究结果表明,与随机规划模型相比,鲁棒优化模型能够有效减少决策风险,但提高决策方案的鲁棒性需付出更高的成本;遗传退火算法较参数取值相同下的遗传算法和模拟退火算法性能更优;碳税的增加对碳排放量的影响呈现明显的阶梯状下降特征,碳税政策制定者可利用这一规律,合理设置碳税值,使其达到减排效果的同时尽可能的减轻企业的经济负担。
Aiming at the problem of vehicle logistics network optimization,a robust optimization model based on low-carbon economy is established.Genetic annealing algorithm is designed to solve the model,and the availability of the model and algorithm is verified by a simulation example.The results show that,compared with stochastic programming model,robust optimization model can effectively reduce decision-making risk,but it needs to pay higher cost to improve the robustness of decision-making scheme;genetic annealing algorithm has better performance than genetic algorithm and simulated annealing algorithm by using the same data with the same dataset;the increase of carbon tax has obvious step-by-step decline characteristics on carbon emissions.Carbon tax policy makers can use this law to set carbon tax value reasonably,so that it can not only achieve the effect of emission reduction,but also decrease the operating cost of the enterprise.
作者
蔡鉴明
吴松城
Cai Jianming;Wu Songcheng(School of Traffic and Transportation Engineering,Central South University,Changsha 410075,China)
出处
《工业技术经济》
CSSCI
北大核心
2020年第7期74-82,共9页
Journal of Industrial Technological Economics
基金
国家重点研发计划先进轨道交通重点专项“公共路权运行环境下非轮轨接触导向运输系统关键技术与装备研制”(项目编号:2018YFB1201600)。
关键词
物流网络
整车
碳税
遗传退火算法
鲁棒优化模型
算例仿真分析
logistics network
vehicle
Carbon tax
genetic annealing algorithm
robust optimization model
example simulation analysis
