双目标冷链物流车辆路径问题及其遗传蚁群求解

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。

Genetic-Ant Colony Algorithm for A two-objective Vehicle Routing Problem of Cold Chain Logistics

To address the multi-objective vehicle routing problem of fresh produce cold chain logistics with the constraints of capacity and soft time windows, a two-objective model which aims at minimizing total cost and maximizing customer satisfaction is established. In order to solve this problem, the epsilon constraint method is used, a Genetic-Ant colony algorithm is designed which based on ant colony algorithm and the crossover and mutation operators is introduced. The combination of deterministic selection and pseudo-random proportion were used for the state transfer and the total amount of pheromone is optimized by piecewise function. An actual calculation example is solved by the proposed algorithm and the genetic algorithm and ant colony algorithm respectively. Comparison results show that the proposed model and algorithm is practical and effective.