IDEAS home Printed from https://ideas.repec.org/a/spr/jglopt/v63y2015i3p515-536.html
   My bibliography  Save this article

Multi-objective variable neighborhood search: an application to combinatorial optimization problems

Author

Listed:
  • Abraham Duarte
  • Juan Pantrigo
  • Eduardo Pardo
  • Nenad Mladenovic

Abstract

Solutions to real-life optimization problems usually have to be evaluated considering multiple conflicting objectives. These kind of problems, known as multi-objective optimization problems, have been mainly solved in the past by using evolutionary algorithms. In this paper, we explore the adaptation of the Variable Neighborhood Search (VNS) metaheuristic to solve multi-objective combinatorial optimization problems. In particular, we describe how to design the shake procedure, the improvement method and the acceptance criterion within different VNS schemas (Reduced VNS, Variable Neighborhood Descent and General VNS), when two or more objectives are considered. We validate these proposals over two multi-objective combinatorial optimization problems. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Abraham Duarte & Juan Pantrigo & Eduardo Pardo & Nenad Mladenovic, 2015. "Multi-objective variable neighborhood search: an application to combinatorial optimization problems," Journal of Global Optimization, Springer, vol. 63(3), pages 515-536, November.
    • Handle: RePEc:spr:jglopt:v:63:y:2015:i:3:p:515-536
    DOI: 10.1007/s10898-014-0213-z
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10898-014-0213-z
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10898-014-0213-z?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. Mladenovic, Nenad & Urosevic, Dragan & Pérez-Brito, Dionisio & García-González, Carlos G., 2010. "Variable neighbourhood search for bandwidth reduction," European Journal of Operational Research, Elsevier, vol. 200(1), pages 14-27, January.
    2. Gomes da Silva, Carlos & Climaco, Joao & Figueira, Jose, 2006. "A scatter search method for bi-criteria {0, 1}-knapsack problems," European Journal of Operational Research, Elsevier, vol. 169(2), pages 373-391, March.
    3. Juan Pantrigo & Rafael Martí & Abraham Duarte & Eduardo Pardo, 2012. "Scatter search for the cutwidth minimization problem," Annals of Operations Research, Springer, vol. 199(1), pages 285-304, October.
    4. Gomes da Silva, Carlos & Figueira, Jose & Climaco, Joao, 2007. "Integrating partial optimization with scatter search for solving bi-criteria {0, 1}-knapsack problems," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1656-1677, March.
    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. Nevena Čolić & Pavle Milošević & Ivana Dragović & Miljan S. Ćeranić, 2024. "IBA-VNS: A Logic-Based Machine Learning Algorithm and Its Application in Surgery," Mathematics, MDPI, vol. 12(7), pages 1-21, March.
    2. Yıldız, Gazi Bilal & Soylu, Banu, 2019. "A multiobjective post-sales guarantee and repair services network design problem," International Journal of Production Economics, Elsevier, vol. 216(C), pages 305-320.
    3. Xiang Song & Dylan Jones & Nasrin Asgari & Tim Pigden, 2020. "Multi-objective vehicle routing and loading with time window constraints: a real-life application," Annals of Operations Research, Springer, vol. 291(1), pages 799-825, August.
    4. Eduardo G. Pardo & Antonio García-Sánchez & Marc Sevaux & Abraham Duarte, 2020. "Basic variable neighborhood search for the minimum sitting arrangement problem," Journal of Heuristics, Springer, vol. 26(2), pages 249-268, April.
    5. Iliopoulou, Christina & Makridis, Michail A., 2023. "Critical multi-link disruption identification for public transport networks: A multi-objective optimization framework," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    6. Abraham Duarte & Eduardo G. Pardo, 2020. "Special issue on recent innovations in variable neighborhood search," Journal of Heuristics, Springer, vol. 26(3), pages 335-338, June.
    7. Wu, Weitiao & Li, Yu, 2024. "Pareto truck fleet sizing for bike relocation with stochastic demand: Risk-averse multi-stage approximate stochastic programming," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
    8. Soylu, Banu & Katip, Hatice, 2019. "A multiobjective hub-airport location problem for an airline network design," European Journal of Operational Research, Elsevier, vol. 277(2), pages 412-425.
    9. Baoyu Liao & Qingru Song & Jun Pei & Shanlin Yang & Panos M. Pardalos, 2020. "Parallel-machine group scheduling with inclusive processing set restrictions, outsourcing option and serial-batching under the effect of step-deterioration," Journal of Global Optimization, Springer, vol. 78(4), pages 717-742, December.
    10. Lamiaa Dahite & Abdeslam Kadrani & Rachid Benmansour & Rym Nesrine Guibadj & Cyril Fonlupt, 2022. "Multi-Objective Model and Variable Neighborhood Search Algorithms for the Joint Maintenance Scheduling and Workforce Routing Problem," Mathematics, MDPI, vol. 10(11), pages 1-37, May.
    11. Angelo Sifaleras, 2023. "In memory of Professor Nenad Mladenović (1951–2022)," SN Operations Research Forum, Springer, vol. 4(1), pages 1-18, March.
    12. Tarik Chargui & Abdelghani Bekrar & Mohamed Reghioui & Damien Trentesaux, 2019. "Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption," Sustainability, MDPI, vol. 11(11), pages 1-23, June.
    13. Wu, Xueqi & Che, Ada, 2020. "Energy-efficient no-wait permutation flow shop scheduling by adaptive multi-objective variable neighborhood search," Omega, Elsevier, vol. 94(C).
    14. Sergio Gil-Borrás & Eduardo G. Pardo & Antonio Alonso-Ayuso & Abraham Duarte, 2020. "GRASP with Variable Neighborhood Descent for the online order batching problem," Journal of Global Optimization, Springer, vol. 78(2), pages 295-325, October.

    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. Rong, Aiying & Figueira, José Rui, 2013. "A reduction dynamic programming algorithm for the bi-objective integer knapsack problem," European Journal of Operational Research, Elsevier, vol. 231(2), pages 299-313.
    2. Rong, Aiying & Figueira, José Rui, 2014. "Dynamic programming algorithms for the bi-objective integer knapsack problem," European Journal of Operational Research, Elsevier, vol. 236(1), pages 85-99.
    3. Florios, Kostas & Mavrotas, George & Diakoulaki, Danae, 2010. "Solving multiobjective, multiconstraint knapsack problems using mathematical programming and evolutionary algorithms," European Journal of Operational Research, Elsevier, vol. 203(1), pages 14-21, May.
    4. Barbati, Maria & Greco, Salvatore & Kadziński, Miłosz & Słowiński, Roman, 2018. "Optimization of multiple satisfaction levels in portfolio decision analysis," Omega, Elsevier, vol. 78(C), pages 192-204.
    5. José Figueira & Luís Paquete & Marco Simões & Daniel Vanderpooten, 2013. "Algorithmic improvements on dynamic programming for the bi-objective {0,1} knapsack problem," Computational Optimization and Applications, Springer, vol. 56(1), pages 97-111, September.
    6. Eduardo G. Pardo & Antonio García-Sánchez & Marc Sevaux & Abraham Duarte, 2020. "Basic variable neighborhood search for the minimum sitting arrangement problem," Journal of Heuristics, Springer, vol. 26(2), pages 249-268, April.
    7. Christian Roed Pedersen & Lars Relund Nielsen & Kim Allan Andersen, 2008. "The Bicriterion Multimodal Assignment Problem: Introduction, Analysis, and Experimental Results," INFORMS Journal on Computing, INFORMS, vol. 20(3), pages 400-411, August.
    8. Guan, Jian & Lin, Geng, 2016. "Hybridizing variable neighborhood search with ant colony optimization for solving the single row facility layout problem," European Journal of Operational Research, Elsevier, vol. 248(3), pages 899-909.
    9. Miguel A. González & Juan José Palacios & Camino R. Vela & Alejandro Hernández-Arauzo, 2017. "Scatter search for minimizing weighted tardiness in a single machine scheduling with setups," Journal of Heuristics, Springer, vol. 23(2), pages 81-110, June.
    10. Pallavi Jain & Kamal Srivastava & Gur Saran, 2016. "Minimizing cyclic cutwidth of graphs using a memetic algorithm," Journal of Heuristics, Springer, vol. 22(6), pages 815-848, December.
    11. João Claro & Jorge Sousa, 2010. "A multiobjective metaheuristic for a mean-risk static stochastic knapsack problem," Computational Optimization and Applications, Springer, vol. 46(3), pages 427-450, July.
    12. Elshaikh, Abdalla & Salhi, Said & Nagy, Gábor, 2015. "The continuous p-centre problem: An investigation into variable neighbourhood search with memory," European Journal of Operational Research, Elsevier, vol. 241(3), pages 606-621.
    13. Barbati, Maria & Corrente, Salvatore & Greco, Salvatore, 2020. "A general space-time model for combinatorial optimization problems (and not only)," Omega, Elsevier, vol. 96(C).
    14. Behrooz Koohestani & Riccardo Poli, 2015. "Addressing the envelope reduction of sparse matrices using a genetic programming system," Computational Optimization and Applications, Springer, vol. 60(3), pages 789-814, April.
    15. Gomes da Silva, Carlos & Figueira, Jose & Climaco, Joao, 2007. "Integrating partial optimization with scatter search for solving bi-criteria {0, 1}-knapsack problems," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1656-1677, March.
    16. Palubeckis, Gintaras & Tomkevičius, Arūnas & Ostreika, Armantas, 2019. "Hybridizing simulated annealing with variable neighborhood search for bipartite graph crossing minimization," Applied Mathematics and Computation, Elsevier, vol. 348(C), pages 84-101.
    17. S. L. Gonzaga de Oliveira & C. Carvalho, 2022. "Metaheuristic algorithms for the bandwidth reduction of large-scale matrices," Journal of Combinatorial Optimization, Springer, vol. 43(4), pages 727-784, May.
    18. Mladenović, Nenad & Kratica, Jozef & Kovačević-Vujčić, Vera & Čangalović, Mirjana, 2012. "Variable neighborhood search for metric dimension and minimal doubly resolving set problems," European Journal of Operational Research, Elsevier, vol. 220(2), pages 328-337.
    19. Cavero, Sergio & Pardo, Eduardo G. & Duarte, Abraham, 2023. "Efficient iterated greedy for the two-dimensional bandwidth minimization problem," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1126-1139.
    20. Santos, Vinícius Gandra Martins & Carvalho, Marco Antonio Moreira de, 2021. "Tailored heuristics in adaptive large neighborhood search applied to the cutwidth minimization problem," European Journal of Operational Research, Elsevier, vol. 289(3), pages 1056-1066.

    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:spr:jglopt:v:63:y:2015:i:3:p:515-536. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.