An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows

皮卡 水准点(测量) 车辆路径问题 数学优化 计算机科学 布线(电子设计自动化) 启发式 任务(项目管理) 构造(python库) 增量启发式搜索 搜索算法 算法 波束搜索 数学 工程类 人工智能 程序设计语言 系统工程 地理 图像(数学) 计算机网络 大地测量学
作者
Stefan Røpke,David Pisinger
出处
期刊:Transportation Science [Institute for Operations Research and the Management Sciences]
卷期号:40 (4): 455-472 被引量:1844
标识
DOI:10.1287/trsc.1050.0135
摘要

The pickup and delivery problem with time windows is the problem of serving a number of transportation requests using a limited amount of vehicles. Each request involves moving a number of goods from a pickup location to a delivery location. Our task is to construct routes that visit all locations such that corresponding pickups and deliveries are placed on the same route, and such that a pickup is performed before the corresponding delivery. The routes must also satisfy time window and capacity constraints. This paper presents a heuristic for the problem based on an extension of the large neighborhood search heuristic previously suggested for solving the vehicle routing problem with time windows. The proposed heuristic is composed of a number of competing subheuristics that are used with a frequency corresponding to their historic performance. This general framework is denoted adaptive large neighborhood search. The heuristic is tested on more than 350 benchmark instances with up to 500 requests. It is able to improve the best known solutions from the literature for more than 50% of the problems. The computational experiments indicate that it is advantageous to use several competing subheuristics instead of just one. We believe that the proposed heuristic is very robust and is able to adapt to various instance characteristics.
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