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首页> 外文期刊>Journal of Intelligent Manufacturing >An exact algorithm for vehicle routing and scheduling problem of free pickup and delivery service in flight ticket sales companies based on set-partitioning model
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An exact algorithm for vehicle routing and scheduling problem of free pickup and delivery service in flight ticket sales companies based on set-partitioning model

机译:基于集合划分模型的机票销售公司免费取送服务车辆路径与调度问题的精确算法

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摘要

This paper addresses a vehicle routing and scheduling problem arising in Flight Ticket Sales Companies for the service of free pickup and delivery of airline passengers to the airport. The problem is formulated under the framework of Vehicle Routing Problem with Time Windows (VRPTW), with the objective of minimizing the total operational costs, i.e. fixed start-up costs and variable traveling costs. A 0–1 mixed integer programming model is presented, in which service quality is factored in constraints by introducing passenger satisfaction degree functions that limit time deviations between actual and desired delivery times. The problem addressed in this paper has two distinctive characteristics—small vehicle capacities and tight delivery time windows. An exact algorithm based on the set-partitioning model, concerning both characteristics, is developed. In the first phase of the algorithm the entire candidate set of best feasible routes is generated, and then the optimal solution is obtained by solving the set-partitioning model in the second phase. Finally, we use four actual instances to illustrate application of the proposed algorithm. Moreover, the proposed algorithm is applied to a random instance containing more orders to verify the general effectiveness of the proposed algorithm even if the number of passengers increases in future.
机译:本文解决了在机票销售公司中出现的为免费接送和运送乘客到机场的服务而产生的车辆路线和调度问题。该问题是在带有时间窗的车辆路径问题(VRPTW)的框架内提出的,目的是将总运营成本(即固定启动成本和可变行驶成本)降至最低。提出了一种0-1混合整数规划模型,其中通过引入乘客满意度函数来限制服务质量,该函数可限制实际交付时间与期望交付时间之间的时间偏差。本文所解决的问题具有两个鲜明的特点-车辆容量小和交货时间窗口紧凑。提出了一种基于集合划分模型的精确算法,涉及这两个特征。在算法的第一阶段,生成最佳可行路线的整个候选集,然后在第二阶段通过求解集合划分模型来获得最优解。最后,我们使用四个实际实例来说明该算法的应用。此外,将所提出的算法应用于包含更多订单的随机实例,以验证所提出算法的总体有效性,即使将来乘客数量增加。

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