CNF Encodings for the Min-Max Multiple Traveling Salesmen Problem

Aolong Zha, Rongxuan Gao, Qiong Chang, Miyuki Koshimura, Itsuki Noda

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

In this study, we consider the multiple traveling salesmen problem (mTSP) with the min-max objective of minimizing the longest tour length. We begin by reviewing an existing integer programming (IP) formulation of this problem. Then, we present several novel conjunctive normal form (CNF) encodings and an approach based on modifying a maximum satisfiability (MaxSAT) algorithm for the min-max mTSP. The correctness and the space complexity of each encoding are analyzed. In our experiments, we compare the performance of solving the TSP benchmark instances using an existing encoding and our new encodings comparing the results achieved using an implemented group MaxSAT solver to those achieved using the IP method. The results show that for the same problem, the new encodings significantly reduce the number of generated clauses over the existing CNF encoding. Although the proposals are still not competitive compared to the IP method, one of them may be more effective on relatively large-scale problems, and it has an advantage over the IP method in solving an instance with a small ratio of the number of cities to the number of salesmen.

本文言語英語
ホスト出版物のタイトルProceedings - IEEE 32nd International Conference on Tools with Artificial Intelligence, ICTAI 2020
編集者Miltos Alamaniotis, Shimei Pan
出版社IEEE Computer Society
ページ285-292
ページ数8
ISBN(電子版)9781728192284
DOI
出版ステータス出版済み - 11 2020
外部発表はい
イベント32nd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2020 - Virtual, Baltimore, 米国
継続期間: 11 9 202011 11 2020

出版物シリーズ

名前Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI
2020-November
ISSN(印刷版)1082-3409

会議

会議32nd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2020
Country米国
CityVirtual, Baltimore
Period11/9/2011/11/20

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Computer Science Applications

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