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A Reinforcement Learning Approach for the Dynamic Container Relocation Problem

Paul Alexandru Bucur(pabucur***at***edu.aau.at)
Philipp Hungerländer(philipp.hungerlaender***at***aau.at)

Abstract: Given an initial configuration of a container bay and an a-priori known departure sequence of the containers, the goal of the Container Relocation Problem is to retrieve the requested containers in the predefined order, while minimizing the number of container relocations inside the bay. The Dynamic Container Relocation Problem (DCRP) introduces an additional aspect by also considering arriving containers. Although the DCRP originates from the port operations environment, its applications extend to other fields, such as industrial warehousing or the steel industry. In this paper, motivated by a cooperation with an Austrian company, we propose a Reinforcement Learning (RL) approach for solving the DCRP. In particular we use RL and problem-specific heuristics for guiding a Monte Carlo Tree Search. In our computational experiments we compare our method with a Beam Search (BS) algorithm on benchmark instances from the literature. While our RL approach cannot quite match the results provided by the problem-specific BS algorithm, it is more flexible and can be adapted much easier whenever we have to consider extensions to the standard version of the DCRP.

Keywords: Dynamic Container Relocation Problem, Monte Carlo Tree Search, Reinforcement Learning.

Category 1: Applications -- OR and Management Sciences (Production and Logistics )

Category 2: Combinatorial Optimization (Meta Heuristics )

Citation: Technical report, Alpen-Adria Universität Klagenfurt, Mathematics, Optimization Group, TR-AAUK-M-O-21-07-17, 2017.

Download: [PDF]

Entry Submitted: 07/21/2017
Entry Accepted: 07/21/2017
Entry Last Modified: 07/21/2017

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