Policy adaptation for vehicle routing

IF 1.4 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
T. Cazenave, J. Lucas, T. Triboulet, Hyoseok Kim
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引用次数: 9

Abstract

Nested Rollout Policy Adaptation (NRPA) is a Monte Carlo search algorithm that learns a playout policy in order to solve a single player game. In this paper we apply NRPA to the vehicle routing problem. This problem is important for large companies that have to manage a fleet of vehicles on a daily basis. Real problems are often too large to be solved exactly. The algorithm is applied to standard problem of the literature and to the specific problems of EDF (Electricité De France, the main French electric utility company). These specific problems have peculiar constraints. NRPA gives better result than the algorithm previously used by EDF.
车辆路线的策略适应
嵌套推出策略适应(NRPA)是一种蒙特卡罗搜索算法,它学习播放策略以解决单人游戏。本文将NRPA算法应用于车辆路径问题。对于每天都要管理车队的大公司来说,这个问题很重要。真正的问题往往太大而无法精确解决。该算法应用于文献中的标准问题和法国电力公司(EDF,法国主要电力公司)的具体问题。这些具体问题有特殊的限制。NRPA算法的结果优于EDF先前使用的算法。
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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来源期刊
AI Communications
AI Communications 工程技术-计算机:人工智能
CiteScore
2.30
自引率
12.50%
发文量
34
审稿时长
4.5 months
期刊介绍: AI Communications is a journal on artificial intelligence (AI) which has a close relationship to EurAI (European Association for Artificial Intelligence, formerly ECCAI). It covers the whole AI community: Scientific institutions as well as commercial and industrial companies. AI Communications aims to enhance contacts and information exchange between AI researchers and developers, and to provide supranational information to those concerned with AI and advanced information processing. AI Communications publishes refereed articles concerning scientific and technical AI procedures, provided they are of sufficient interest to a large readership of both scientific and practical background. In addition it contains high-level background material, both at the technical level as well as the level of opinions, policies and news.
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