A new MCTS-based algorithm for multi-objective flexible job shop scheduling problem

Jen Jai Chou, Chao Chin Liang, Hung Chun Wu, I-Chen Wu, Tung Ying Wu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Multi-objective flexible job-shop scheduling problem (MO-FJSP) is very important in both fields of production management and combinatorial optimization. Wu et al. proposed a Monte-Carlo Tree Search (MCTS) to solve MO-FJSP and successfully improved the performance of MCTS to find 17 Pareto solutions: 4 of Kacem 4×5, 3 of 10×7, 4 of 8×8, 4 of 10×10, and 2 of 15×10. This paper proposes a new MCTS-based algorithm for MO-FJSP problem by modifying their algorithm. Our experimental results show that our new algorithm significantly outperforms their algorithm for large problems, especially for Kacem 15×10. This shows that the new algorithm tends to have better potential of solving harder MO-FJSP problems.

Original languageEnglish
Title of host publicationTAAI 2015 - 2015 Conference on Technologies and Applications of Artificial Intelligence
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages136-141
Number of pages6
ISBN (Electronic)9781467396066
DOIs
StatePublished - 12 Feb 2016
EventConference on Technologies and Applications of Artificial Intelligence, TAAI 2015 - Tainan, Taiwan
Duration: 20 Nov 201522 Nov 2015

Publication series

NameTAAI 2015 - 2015 Conference on Technologies and Applications of Artificial Intelligence

Conference

ConferenceConference on Technologies and Applications of Artificial Intelligence, TAAI 2015
CountryTaiwan
CityTainan
Period20/11/1522/11/15

Keywords

  • Evolutionary Algorithm
  • Monte-Carlo Tree Search
  • Multi-Objective Flexible Job Shop Scheduling Problem
  • Rapid Action Value Estimates

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