产业互联背景下“智造”供需网多目标优化  

Multi-objective Optimization of"Intelligent Manufacturing"Supply and Demand Network amidst Industrial Interconnection

作　　者：王会停 Wang Huiting(School of Management,University of Shanghai for Science&Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学管理学院,上海200093

出　　处：《物流技术》2020年第8期46-51,共6页Logistics Technology

摘　　要：目前,供给侧企业进行产业互联成为一种趋势,在此情境下供需网节点企业如何借助于互联网技术进行战略性的布局成为人们关注的焦点。鉴于此,以成本最小化、市场需求满足率最大化以及碳排放最小化为目标,在基于信息共享的制造商、物流商以及需求域形成的“智造”供需网内,分析在产业互联的背景下供需网节点企业根据自身战略目标布局时如何针对不同产品进行制造商的选择与产能决策,并且分配物流商承载量以及运输流量问题。并借助于Pareto遗传算法的优势,运用快速非支配排序的遗传算法(NSGA-Ⅱ)进行仿真模拟,求解得出多目标下的pareto前沿,得出了制造商的分配方案以及物流商的流量分配。In this paper,aiming to minimize costs,increase market demand satisfaction rate and cut down carbon emissions,and within the scope of an"intelligent manufacturing"supply and demand network consisting of information-sharing manufacturers,logistics providers and demand areas,we analyzed how nodal enterprises in the supply and demand network should select manufacturers,make capacity decisions,and allocate their demand and transportation volume among the logistics providers regarding different products according to their own strategic goals and layout in the context of industrial interconnection.Then,taking advantage of the Pareto genetic algorithm,we emulated the process using the fast non-dominant sorting NSGA-Ⅱ genetic algorithm,arrived at the Pareto front under the multiple objectives,and obtained the distribution scheme of the manufacturers and the volume distribution among the logistics providers.

关 键 词：产业互联 “智造”供需网 网络优化 NSGA-Ⅱ 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] F274[自动化与计算机技术—计算机科学与技术]