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Generate Competitive Solutions for Uncapacitated Facility Location Problem by Learning from Small Instances

Published: 24 July 2023 Publication History

Abstract

The uncapacitated facility location problem (UFLP) is an NP-hard problem with a wide range of applications. It aims to choose a set of facilities to serve customers with the lowest total cost. This paper explores the idea of learning good heuristics, which could be regarded as a kind of optimization experiences, over a set of small problem instances. Then the learned heuristics (i.e., gained experiences) are used to generate good solutions for large-scale UFLPs although the large-scale ones are never used during learning. In this paper, we propose a novel facility opening estimation (FOE) heuristic for UFLP. Each facility's opening probability is estimated by a model related to its local apportioned cost (LAC). The model learns from the experience extracted in solving small UFLPs. Then, the model is embedded into the FOE heuristic to generate solutions for large UFLPs. The empirical results and analysis demonstrate that the optimization experience extraction is effective and can assist in generating high-quality solutions for large UFLPs.

References

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cover image ACM Conferences
GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation
July 2023
2519 pages
ISBN:9798400701207
DOI:10.1145/3583133
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

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Association for Computing Machinery

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Publication History

Published: 24 July 2023

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Author Tags

  1. facility location problem
  2. experience extract
  3. meta-heuristic algorithm
  4. combinatorial optimization

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  • the National Natural Science Foundation of China
  • the Guangdong Provincial Key Laboratory
  • the Program for Guangdong Introducing Innovative and Enterpreneurial Teams
  • the Shenzhen Science and Technology Program
  • the Shenzhen Fundamental Research Program
  • the Research Institute of Trustworthy Autonomous Systems

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