双目标冷链物流车辆路径问题及其遗传蚁群求解  

Genetic-ant Colony Algorithm for a Two-objective Vehicle Routing Problem of Cold Chain Logistics

作　　者：张瑾[1] 毕国通 戴二壮 ZHANG Jin;BI Guo-tong;DAI Er-zhuang(School of Computer and Information Engineering,Henan University,Kaifeng 475004,China;Business School,Henan University,Kaifeng 475004,China)

机构地区：[1]河南大学计算机与信息工程学院,开封475004 [2]河南大学商学院,开封475004

出　　处：《科学技术与工程》2020年第18期7413-7421,共9页Science Technology and Engineering

基　　金：国家自然科学基金(41801310)。

摘　　要：针对带容量和软时间窗约束的双目标生鲜农产品冷链物流车辆路径问题,建立了以最小化总成本和最大化客户满意度为目标的双目标优化模型。为了求解问题,运用ε约束法处理双目标模型,以蚁群算法为基础,加入交叉与变异算子,设计了遗传蚁群算法。算法求解过程中,蚂蚁个体在进行状态转移时按照确定性选择和伪随机比例选择相结合的方式,信息素总量采用分段函数进行优化。为验证模型与算法的有效性,对实际算例进行求解,并与遗传算法、蚁群算法求得结果进行对比。结果表明所建模型符合实际需求,所设计的遗传蚁群算法收敛速度和求解结果均优于遗传算法和蚁群算法。To address the multi-objective vehicle routing problem in fresh produce cold chain logistics with the constraints of capacity and soft time windows, a two-objective model aiming at minimizing total cost and maximizing customer satisfaction was established. The epsilon constraint method was used, a genetic-ant colony algorithm was designed in ant colony algorithm, and the crossover and mutation operators were introduced. The combination of deterministic selection and pseudo-random proportion were used for the state transfer, and the total amount of pheromone was optimized by piecewise function. An actual calculation example was studied using the proposed algorithm, the genetic algorithm, and the ant colony algorithm, separately. Comparison results show that the proposed model is practical and effective.

关 键 词：冷链物流车辆路径问题 客户满意度 遗传蚁群算法 双目标 ε约束 

分 类 号：U116.2[交通运输工程]