IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v283y2020i1p83-93.html
   My bibliography  Save this article

Multi-objective evolutionary algorithms for a reliability location problem

Author

Listed:
  • Alcaraz, Javier
  • Landete, Mercedes
  • Monge, Juan F.
  • Sainz-Pardo, José L.

Abstract

Some location problems with unreliable facilities present two different objectives, one consisting of minimizing the opening and transportation costs if none of the facilities fail and another consisting of minimizing the expected transportation costs. Usually, these different targets are combined in a single objective function and the decision maker can obtain some different solutions weighting both objectives. However, if the decision maker prefers to obtain a diverse set of non-dominated optimal solutions, then such procedure would not be effective. We have designed and implemented two multi-objective evolutionary algorithms for the realibility fixed-charge location problem by exploiting the peculiarities of this problem in order to obtain sets of solutions that are properly distributed along the Pareto-optimal frontier. The computational results demonstrate the outstanding efficiency of the proposed algorithms, although they present clear differences.

Suggested Citation

  • Alcaraz, Javier & Landete, Mercedes & Monge, Juan F. & Sainz-Pardo, José L., 2020. "Multi-objective evolutionary algorithms for a reliability location problem," European Journal of Operational Research, Elsevier, vol. 283(1), pages 83-93.
    • Handle: RePEc:eee:ejores:v:283:y:2020:i:1:p:83-93
    DOI: 10.1016/j.ejor.2019.10.043
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221719308951
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2019.10.043?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Li, Xiaopeng & Ouyang, Yanfeng, 2011. "Reliable sensor deployment for network traffic surveillance," Transportation Research Part B: Methodological, Elsevier, vol. 45(1), pages 218-231, January.
    2. Abdullah Konak & Alice E. Smith, 2011. "Efficient Optimization of Reliable Two-Node Connected Networks: A Biobjective Approach," INFORMS Journal on Computing, INFORMS, vol. 23(3), pages 430-445, August.
    3. Elena Fernández & Mercedes Landete, 2015. "Fixed-Charge Facility Location Problems," Springer Books, in: Gilbert Laporte & Stefan Nickel & Francisco Saldanha da Gama (ed.), Location Science, edition 127, chapter 0, pages 47-77, Springer.
    4. Zuo-Jun Max Shen & Roger Lezhou Zhan & Jiawei Zhang, 2011. "The Reliable Facility Location Problem: Formulations, Heuristics, and Approximation Algorithms," INFORMS Journal on Computing, INFORMS, vol. 23(3), pages 470-482, August.
    5. José L. Sainz-Pardo & Javier Alcaraz & Mercedes Landete & Juan F. Monge, 2017. "On relaxing the integrality of the allocation variables of the reliability fixed-charge location problem," Journal of Global Optimization, Springer, vol. 67(4), pages 787-804, April.
    6. Matthias Ehrgott & Xavier Gandibleux, 2004. "Approximative solution methods for multiobjective combinatorial optimization," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 12(1), pages 1-63, June.
    7. Alcaraz, Javier & Landete, Mercedes & Monge, Juan F., 2012. "Design and analysis of hybrid metaheuristics for the Reliability p-Median Problem," European Journal of Operational Research, Elsevier, vol. 222(1), pages 54-64.
    8. Mavrotas, George & Florios, Kostas, 2013. "An improved version of the augmented epsilon-constraint method (AUGMECON2) for finding the exact Pareto set in Multi-Objective Integer Programming problems," MPRA Paper 105034, University Library of Munich, Germany.
    9. Lawrence V. Snyder & Mark S. Daskin, 2005. "Reliability Models for Facility Location: The Expected Failure Cost Case," Transportation Science, INFORMS, vol. 39(3), pages 400-416, August.
    10. M. Ehrgott & S. Ruzika, 2008. "Improved ε-Constraint Method for Multiobjective Programming," Journal of Optimization Theory and Applications, Springer, vol. 138(3), pages 375-396, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Duro, João A. & Ozturk, Umud Esat & Oara, Daniel C. & Salomon, Shaul & Lygoe, Robert J. & Burke, Richard & Purshouse, Robin C., 2023. "Methods for constrained optimization of expensive mixed-integer multi-objective problems, with application to an internal combustion engine design problem," European Journal of Operational Research, Elsevier, vol. 307(1), pages 421-446.
    2. Liagkouras, Konstantinos & Metaxiotis, Konstantinos, 2021. "Improving multi-objective algorithms performance by emulating behaviors from the human social analogue in candidate solutions," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1019-1036.
    3. Xu, Jiuping & Zhao, Chuandang & Wang, Fengjuan & Yang, Guocan, 2022. "Industrial decarbonisation oriented distributed renewable generation towards wastewater treatment sector: Case from the Yangtze River Delta region in China," Energy, Elsevier, vol. 256(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Zhaodong & Xie, Siyang & Ouyang, Yanfeng, 2022. "Planning reliable service facility location against disruption risks and last-mile congestion in a continuous space," Transportation Research Part B: Methodological, Elsevier, vol. 165(C), pages 123-140.
    2. Mohammadi, Mehrdad & Jula, Payman & Tavakkoli-Moghaddam, Reza, 2019. "Reliable single-allocation hub location problem with disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 123(C), pages 90-120.
    3. José L. Sainz-Pardo & Javier Alcaraz & Mercedes Landete & Juan F. Monge, 2017. "On relaxing the integrality of the allocation variables of the reliability fixed-charge location problem," Journal of Global Optimization, Springer, vol. 67(4), pages 787-804, April.
    4. Aldrighetti, Riccardo & Battini, Daria & Ivanov, Dmitry & Zennaro, Ilenia, 2021. "Costs of resilience and disruptions in supply chain network design models: A review and future research directions," International Journal of Production Economics, Elsevier, vol. 235(C).
    5. An, Yu & Zhang, Yu & Zeng, Bo, 2015. "The reliable hub-and-spoke design problem: Models and algorithms," Transportation Research Part B: Methodological, Elsevier, vol. 77(C), pages 103-122.
    6. Davood Shishebori & Lawrence Snyder & Mohammad Jabalameli, 2014. "A Reliable Budget-Constrained FL/ND Problem with Unreliable Facilities," Networks and Spatial Economics, Springer, vol. 14(3), pages 549-580, December.
    7. Li, Xiaopeng & Ouyang, Yanfeng & Peng, Fan, 2013. "A supporting station model for reliable infrastructure location design under interdependent disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 60(C), pages 80-93.
    8. Zhang, Ying & Snyder, Lawrence V. & Qi, Mingyao & Miao, Lixin, 2016. "A heterogeneous reliable location model with risk pooling under supply disruptions," Transportation Research Part B: Methodological, Elsevier, vol. 83(C), pages 151-178.
    9. Cheng, Chun & Qi, Mingyao & Zhang, Ying & Rousseau, Louis-Martin, 2018. "A two-stage robust approach for the reliable logistics network design problem," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 185-202.
    10. Ansari, Sina & Başdere, Mehmet & Li, Xiaopeng & Ouyang, Yanfeng & Smilowitz, Karen, 2018. "Advancements in continuous approximation models for logistics and transportation systems: 1996–2016," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 229-252.
    11. Basciftci, Beste & Ahmed, Shabbir & Shen, Siqian, 2021. "Distributionally robust facility location problem under decision-dependent stochastic demand," European Journal of Operational Research, Elsevier, vol. 292(2), pages 548-561.
    12. O’Hanley, Jesse R. & Scaparra, M. Paola & García, Sergio, 2013. "Probability chains: A general linearization technique for modeling reliability in facility location and related problems," European Journal of Operational Research, Elsevier, vol. 230(1), pages 63-75.
    13. Ahmadi-Javid, Amir & Seddighi, Amir Hossein, 2013. "A location-routing problem with disruption risk," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 53(C), pages 63-82.
    14. An, Shi & Cui, Na & Bai, Yun & Xie, Weijun & Chen, Mingliu & Ouyang, Yanfeng, 2015. "Reliable emergency service facility location under facility disruption, en-route congestion and in-facility queuing," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 82(C), pages 199-216.
    15. Luohao Tang & Cheng Zhu & Zaili Lin & Jianmai Shi & Weiming Zhang, 2016. "Reliable Facility Location Problem with Facility Protection," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-24, September.
    16. Holzmann, Tim & Smith, J.C., 2018. "Solving discrete multi-objective optimization problems using modified augmented weighted Tchebychev scalarizations," European Journal of Operational Research, Elsevier, vol. 271(2), pages 436-449.
    17. Fahimnia, Behnam & Jabbarzadeh, Armin, 2016. "Marrying supply chain sustainability and resilience: A match made in heaven," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 91(C), pages 306-324.
    18. Nader Azad & Elkafi Hassini, 2019. "A Benders Decomposition Method for Designing Reliable Supply Chain Networks Accounting for Multimitigation Strategies and Demand Losses," Transportation Science, INFORMS, vol. 53(5), pages 1287-1312, September.
    19. Yun, Lifen & Wang, Xifu & Fan, Hongqiang & Li, Xiaopeng, 2020. "Reliable facility location design with round-trip transportation under imperfect information Part I: A discrete model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    20. Zarrinpoor, Naeme & Fallahnezhad, Mohammad Saber & Pishvaee, Mir Saman, 2018. "The design of a reliable and robust hierarchical health service network using an accelerated Benders decomposition algorithm," European Journal of Operational Research, Elsevier, vol. 265(3), pages 1013-1032.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:283:y:2020:i:1:p:83-93. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.