A sweep-heuristic algorithm combined with partition of time and district for large-scale vehicle routing and scheduling problem of pickup and delivery of customers to airport

Bingxing Liu, Jiafu Tang, Yang Yu
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引用次数: 1

Abstract

This paper proposes a sweep-heuristic algorithm combined with partition of time and district to solve large-scale single-trip vehicle routing and scheduling problem (VRSP) of pickup and delivery of customers to airport. Firstly, customers with the same pickup time and pickup location are aggregated into customer orders, which are the basic units for consideration in this paper. The customer orders are further classified into sets according to their pickup time range; these sets are sequenced in ascending order of their pickup time. Secondly, with the depot being the center of all customer orders, the whole service area are divided into circulars according to the driving distance. Every circular indicates different time when car keeps an even speed, and it cuts the service areas into several zones, from the center to the border. Thirdly, every time, we choose customer orders located in the inside circulars from earlier sets and the ones in the outside circulars from later sets to form a new set to be optimized. Finally, generate a radial from the center, and scan the circle with the sweep method to gain the optimized results for this set. Repeat the aforementioned processes, until all customer orders have been serviced. Furthermore, by comparing the results under the criteria of minimum cost and maximum customer satisfaction degree, one can verify that the customer satisfaction degree obtained from the former objective is close to the one from the later objective. However, the cost of the former is apparently lower than the later.
一种结合时间和区域划分的扫描启发式算法用于大规模车辆路线和客户到机场的接送调度问题
本文提出了一种结合时间和区域划分的扫描启发式算法,用于解决客户到机场接送的大规模单行程车辆路径调度问题。首先,将具有相同取货时间和取货地点的客户聚合为客户订单,这是本文考虑的基本单位。将客户订单根据提货时间范围进一步划分为组;这些集合按照拾取时间的升序排列。其次,车厂是所有客户订单的中心,整个服务区根据行驶距离划分为环形。每个圆形表示车辆保持均匀速度的不同时间,并将服务区从中心到边界划分为几个区域。第三,每次从前期循环中选取位于内循环的客户订单,再从后期循环中选取位于外循环的客户订单,形成新的一组进行优化。最后,从中心生成一个径向,用扫描方法对圆进行扫描,得到该集合的优化结果。重复上述过程,直到所有客户订单都得到服务。通过比较成本最小和顾客满意度最大准则下的结果,可以验证前一个目标下的顾客满意度与后一个目标下的顾客满意度是接近的。但是,前者的成本明显低于后者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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