摘要
在不确定需求环境下,针对循环取货问题,提出了基于VRP与3D-KLP协同优化模型(3KL-CVRPCSO),并设计了求解该模型的多阶段算法(HPGBT).首先,采用基于遗传算法的混合粒子群优化算法和启发式正交二叉树搜索算法求解不同车型最优行驶路径及车厢内装载的各种类货物的最优数量,以此确定各车型车辆的单车最优路径-装载方案;再以这些方案作为决策变量,以实际货物需求量为约束条件,建立新的基于实际需求的路径-装载协同优化模型并进行求解,得到按不同车型单车最优方案执行的车辆数.通过与国外学者近期在权威期刊的优化结果比较和实际案例的应用两方面的研究与验证,证明了本文方法的可行性及有效性,从而建立了一种以不同车型单车"路径+装载"复合方案为决策单元,以实际需求为约束条件,进而以最优组合方案解决不确定性需求问题的物流车辆调度新方法.
Under the uncertain demand environment,for the problem of cyclic pickup,a collaborative optimization model based on VRP and 3 D-KLP(3 KL-CVRPCSO) was proposed,and a multi-stage algorithm(HPGBT) to solve the model was designed.First,a hybrid particle swarm optimization algorithm based on genetic algorithm and a heuristic orthogonal binary tree search algorithm are used to solve the optimal driving routing of different vehicle types and the optimal quantity of various types of goods loaded in the compartment.In this way,the optimal single-vehicle routing-loading plan of each type of vehicle is determined;and these plans are used as decision variables,and the actual cargo demand is the constraint condition,a new routing-loading collaborative optimization model based on actual demand is established and solved,and the result is that the number of vehicles executed according to the optimal plan of different models of bicycles.By comparing with the optimization results of foreign scholars in authoritative journals and the application of actual cases,the research and verification of two aspects have proved the feasibility and effectiveness of this method.Thus,a new method of logistics vehicle scheduling is established that takes the "routing+loading " composite plan of different models of bicycles as the decision-making unit,takes the actual demand as the constraint,and then uses the optimal combination plan to solve the uncertain demand problem.
作者
李彤
崔晶
LI Tong;CUI Jing(School of Economics and Management,Business School,Dalian University of Technology,Dalian 116024,China)
出处
《系统工程理论与实践》
EI
CSSCI
CSCD
北大核心
2021年第10期2561-2580,共20页
Systems Engineering-Theory & Practice
基金
国家自然科学基金(71671022)
国家自然科学基金重点项目(71531002)。
