@inproceedings{059adbb0808d4c90aa24c6b921da590d,
title = "A hybrid evolutionary algorithm for reliable facility location problem",
abstract = "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{\textquoteright} 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{\textquoteright}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. {\textcopyright} The Author(s) 2020.",
keywords = "Evolutionary algorithm, Hybrid algorithm, Integer programming, Local search, Reliable facility location problem",
author = "Han ZHANG and Jialin LIU and Xin YAO",
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).; International Conference on Parallel Problem Solving from Nature 2020, PPSN 2020 ; Conference date: 05-09-2020 Through 09-09-2020",
year = "2020",
doi = "10.1007/978-3-030-58115-2_32",
language = "English",
isbn = "9783030581145",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "454--467",
editor = "B{\"A}CK, {Thomas } and PREUSS, {Mike } and Andr{\'e} DEUTZ and WANG, {Hao } and Carola DOERR and EMMERICH, {Michael } and TRAUTMANN, {Heike }",
booktitle = "Parallel Problem Solving from Nature – PPSN XVI : 16th International Conference, PPSN 2020, Leiden, The Netherlands, September 5-9, 2020, Proceedings, Part II",
address = "Germany",
}