Abstract: Dynamic customer demands may sometimes make the logistics distribution plan non-optimal or even infeasible. To satisfy customers’ diverse demands and cope with the disruption caused by changes in customer demand, a disruption management strategy is proposed to optimize the distribution vehicle routing problem. The deviation measurements on companies, customers and distributors are first defined. Then a mathematical model that minimizes the weighted sum of deviation measurements is constructed. Next, an improved genetic algorithm is developed to efficiently solve the model. Considering the mixed load attributes of vehicles, two-stage crossover and mutation operation based on individual double-layer gene expression are proposed. Finally, experiments are conducted on instances extended from the Solomon benchmarks. The results show that the strategy is feasible and superior in solving the distribution routing problem with customer demand uncertainty. This research can provide theoretical basis and practical guidance for the route decision in distribution service.

Key words: logistics distribution, vehicle routing optimization, disruption management, mixed-load vehicle, improved genetic algorithm

摘要： 为满足物流配送中的客户多样性需求，解决客户需求变动导致的初始配送计划丧失最优性甚至无法实施的难题，基于干扰管理理论提出物流配送路径实时优化策略。结合物流配送企业、客户和配送人员三方主体在配送活动中的不同需求，建立干扰对各主体的扰动度量函数，并以其加权函数最小化为目标构建物流配送路径干扰恢复模型。设计改进遗传算法对模型求解，考虑配送车辆的混载特性，提出基于双层基因表达的种群个体编码方式及两阶段交叉和变异操作。选取Solomon标准测试算例进行物流配送仿真实验，结果表明该策略对求解客户需求变动下的物流配送路径问题具有可行性和优越性。研究成果能够为物流配送路径的决策制定提供理论支持和实践指导依据。

关键词: 物流配送, 车辆路径优化, 干扰管理, 车辆混载, 改进遗传算法