Nested Rollout Policy Adaptation for Optimizing Vehicle Selection in Complex VRPs

被引：0

作者：

Abdo, Ashraf ^{[1
]}

Edelkamp, Stefan ^{[1
]}

Lawo, Michael ^{[1
]}

机构：

[1] Univ Bremen, Fac Math & Comp Sci, Inst Artificial Intelligence, TZI Ctr Comp & Commun Technol, Bremen, Germany

来源：

PROCEEDINGS OF THE 2016 IEEE 41ST CONFERENCE ON LOCAL COMPUTER NETWORKS - LCN WORKSHOPS 2016 | 2016年

关键词：

Routing; Monte Carlo Methods; Nested Rollout Policy Adaptation;

D O I：

10.1109/LCNW.2016.29

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The goal of Vehicle Routing Problems (VRP) and their variations is to transport a set of orders with the minimum number of vehicles at least cost. Most approaches are designed to solve specific problem variations independently, whereas in real world applications, those constraints are handled concurrently. This paper describes a novel approach to solve variants of Open VRP, Multi Depot VRP, Capacitated VRP as well as Pickup and Delivery with Time Windows applying Monte-Carlo Tree Search (MCTS) and in particular Nested Rollout Policy Adaptation. For evaluation, real data from the industry was obtained and tested on the developed approach.

引用

页码：213 / 221

页数：9