A hybrid evolutionary algorithm for reliable facility location problem

Han ZHANG, Jialin LIU, Xin YAO*

*Corresponding author for this work

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

2 Citations (Scopus)


The reliable facility location problem (RFLP) is an important research topic of operational research and plays a vital role in the decision-making and management of modern supply chain and logistics. Through solving RFLP, the decision-maker can obtain reliable location decisions under the risk of facilities’ disruptions or failures. In this paper, we propose a novel model for the RFLP. Instead of assuming allocating a fixed number of facilities to each customer as in the existing works, we set the number of allocated facilities as an independent variable in our proposed model, which makes our model more close to the scenarios in real life but more difficult to be solved by traditional methods. To handle it, we propose EAMLS, a hybrid evolutionary algorithm, which combines a memorable local search (MLS) method and an evolutionary algorithm (EA). Additionally, a novel metric called l3-value is proposed to assist the analysis of the algorithm’s convergence speed and exam the process of evolution. The experimental results show the effectiveness and superior performance of our EAMLS, compared to a CPLEX solver and a Genetic Algorithm (GA), on large-scale problems. © The Author(s) 2020.
Original languageEnglish
Title of host publicationParallel Problem Solving from Nature – PPSN XVI : 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part II
EditorsThomas BÄCK, Mike PREUSS, André DEUTZ, Hao WANG, Carola DOERR, Michael EMMERICH, Heike TRAUTMANN
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages14
ISBN (Electronic)9783030581152
ISBN (Print)9783030581145
Publication statusPublished - 2020
Externally publishedYes
EventInternational Conference on Parallel Problem Solving from Nature 2020 - Leiden, Netherlands
Duration: 5 Sept 20209 Sept 2020

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
NameTheoretical Computer Science and General Issues
ISSN (Print)2512-2010
ISSN (Electronic)2512-2029


ConferenceInternational Conference on Parallel Problem Solving from Nature 2020
Abbreviated titlePPSN 2020

Bibliographical note

This work was supported by the National Key R&D Program of China (Grant No. 2017YFC0804003), the National Natural Science Foundation of China (Grant No. 61976111, 61906083), the Guangdong Provincial Key Laboratory (Grant No. 2020B121201001), the Program for Guangdong Introducing Innovative and Enter-preneurial Teams (Grant No. 2017ZT07X386), the Science and Technology Innovation Committee Foundation of Shenzhen (Grant No. JCYJ20190809121403553), the Shenzhen Science and Technology Program (Grant No. KQTD2016112514355531) and the Program for University Key Laboratory of Guangdong Province (Grant No. 2017KSYS008).


  • Evolutionary algorithm
  • Hybrid algorithm
  • Integer programming
  • Local search
  • Reliable facility location problem


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