IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/105074.html
   My bibliography  Save this paper

Generation of the exact Pareto set in multi-objective traveling salesman and set covering problems

Author

Listed:
  • Florios, Kostas
  • Mavrotas, George

Abstract

The calculation of the exact set in Multi-Objective Combinatorial Optimization (MOCO) problems is one of the most computationally demanding tasks as most of the problems are NP-hard. In the present work we use AUGMECON2 a Multi-Objective Mathematical Programming (MOMP) method which is capable of generating the exact Pareto set in Multi-Objective Integer Programming (MOIP) problems for producing all the Pareto optimal solutions in two popular MOCO problems: The Multi-Objective Traveling Salesman Problem (MOTSP) and the Multi-Objective Set Covering problem (MOSCP). The computational experiment is confined to two-objective problems that are found in the literature. The performance of the algorithm is slightly better to what is already found from previous works and it goes one step further generating the exact Pareto set to till now unsolved problems. The results are provided in a dedicated site and can be useful for benchmarking with other MOMP methods or even Multi-Objective Meta-Heuristics (MOMH) that can check the performance of their approximate solution against the exact solution in MOTSP and MOSCP problems.

Suggested Citation

  • Florios, Kostas & Mavrotas, George, 2014. "Generation of the exact Pareto set in multi-objective traveling salesman and set covering problems," MPRA Paper 105074, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:105074
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/105074/1/MPRA_paper_105074.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bérubé, Jean-François & Gendreau, Michel & Potvin, Jean-Yves, 2009. "An exact [epsilon]-constraint method for bi-objective combinatorial optimization problems: Application to the Traveling Salesman Problem with Profits," European Journal of Operational Research, Elsevier, vol. 194(1), pages 39-50, April.
    2. 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.
    3. Christian Prins & Caroline Prodhon & Roberto Calvo, 2006. "Two-phase method and Lagrangian relaxation to solve the Bi-Objective Set Covering Problem," Annals of Operations Research, Springer, vol. 147(1), pages 23-41, October.
    4. Volgenant, A., 1990. "Symmetric traveling salesman problems," European Journal of Operational Research, Elsevier, vol. 49(1), pages 153-154, November.
    5. Altannar Chinchuluun & Panos Pardalos, 2007. "A survey of recent developments in multiobjective optimization," Annals of Operations Research, Springer, vol. 154(1), pages 29-50, October.
    6. Garcia-Martinez, C. & Cordon, O. & Herrera, F., 2007. "A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP," European Journal of Operational Research, Elsevier, vol. 180(1), pages 116-148, July.
    7. Przybylski, Anthony & Gandibleux, Xavier & Ehrgott, Matthias, 2008. "Two phase algorithms for the bi-objective assignment problem," European Journal of Operational Research, Elsevier, vol. 185(2), pages 509-533, March.
    8. Laumanns, Marco & Thiele, Lothar & Zitzler, Eckart, 2006. "An efficient, adaptive parameter variation scheme for metaheuristics based on the epsilon-constraint method," European Journal of Operational Research, Elsevier, vol. 169(3), pages 932-942, March.
    9. Laporte, Gilbert, 1992. "The traveling salesman problem: An overview of exact and approximate algorithms," European Journal of Operational Research, Elsevier, vol. 59(2), pages 231-247, June.
    10. Özpeynirci, Özgür & Köksalan, Murat, 2009. "Multiobjective traveling salesperson problem on Halin graphs," European Journal of Operational Research, Elsevier, vol. 196(1), pages 155-161, July.
    11. Helsgaun, Keld, 2000. "An effective implementation of the Lin-Kernighan traveling salesman heuristic," European Journal of Operational Research, Elsevier, vol. 126(1), pages 106-130, October.
    12. Andrzej Jaszkiewicz, 2004. "A Comparative Study of Multiple-Objective Metaheuristics on the Bi-Objective Set Covering Problem and the Pareto Memetic Algorithm," Annals of Operations Research, Springer, vol. 131(1), pages 135-158, October.
    13. Jaszkiewicz, Andrzej & Zielniewicz, Piotr, 2009. "Pareto memetic algorithm with path relinking for bi-objective traveling salesperson problem," European Journal of Operational Research, Elsevier, vol. 193(3), pages 885-890, March.
    14. Jaszkiewicz, Andrzej, 2002. "Genetic local search for multi-objective combinatorial optimization," European Journal of Operational Research, Elsevier, vol. 137(1), pages 50-71, February.
    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. Ömer Faruk Yılmaz & Büşra Yazıcı, 2022. "Tactical level strategies for multi-objective disassembly line balancing problem with multi-manned stations: an optimization model and solution approaches," Annals of Operations Research, Springer, vol. 319(2), pages 1793-1843, December.
    2. Justus Bonz, 2021. "Application of a multi-objective multi traveling salesperson problem with time windows," Public Transport, Springer, vol. 13(1), pages 35-57, March.
    3. Mohammed Mahrach & Gara Miranda & Coromoto León & Eduardo Segredo, 2020. "Comparison between Single and Multi-Objective Evolutionary Algorithms to Solve the Knapsack Problem and the Travelling Salesman Problem," Mathematics, MDPI, vol. 8(11), pages 1-23, November.
    4. Lakmali Weerasena & Aniekan Ebiefung & Anthony Skjellum, 2022. "Design of a heuristic algorithm for the generalized multi-objective set covering problem," Computational Optimization and Applications, Springer, vol. 82(3), pages 717-751, July.
    5. David Bergman & Merve Bodur & Carlos Cardonha & Andre A. Cire, 2022. "Network Models for Multiobjective Discrete Optimization," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 990-1005, March.
    6. Alexandros Nikas & Angelos Fountoulakis & Aikaterini Forouli & Haris Doukas, 2022. "A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems," Operational Research, Springer, vol. 22(2), pages 1291-1332, April.
    7. Oracio I. Barbosa-Ayala & Jhon A. Montañez-Barrera & Cesar E. Damian-Ascencio & Adriana Saldaña-Robles & J. Arturo Alfaro-Ayala & Jose Alfredo Padilla-Medina & Sergio Cano-Andrade, 2020. "Solution to the Economic Emission Dispatch Problem Using Numerical Polynomial Homotopy Continuation," Energies, MDPI, vol. 13(17), pages 1-15, August.

    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. Lakmali Weerasena & Aniekan Ebiefung & Anthony Skjellum, 2022. "Design of a heuristic algorithm for the generalized multi-objective set covering problem," Computational Optimization and Applications, Springer, vol. 82(3), pages 717-751, July.
    2. Diclehan Tezcaner Öztürk & Murat Köksalan, 2016. "An interactive approach for biobjective integer programs under quasiconvex preference functions," Annals of Operations Research, Springer, vol. 244(2), pages 677-696, September.
    3. 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.
    4. Jaszkiewicz, Andrzej, 2018. "Many-Objective Pareto Local Search," European Journal of Operational Research, Elsevier, vol. 271(3), pages 1001-1013.
    5. N Safaei & D Banjevic & A K S Jardine, 2011. "Bi-objective workforce-constrained maintenance scheduling: a case study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1005-1018, June.
    6. Dinçer Konur & Hadi Farhangi & Cihan H. Dagli, 2016. "A multi-objective military system of systems architecting problem with inflexible and flexible systems: formulation and solution methods," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 38(4), pages 967-1006, October.
    7. Schmidt, Adam & Albert, Laura A. & Zheng, Kaiyue, 2021. "Risk management for cyber-infrastructure protection: A bi-objective integer programming approach," Reliability Engineering and System Safety, Elsevier, vol. 205(C).
    8. Walter J. Gutjahr & Alois Pichler, 2016. "Stochastic multi-objective optimization: a survey on non-scalarizing methods," Annals of Operations Research, Springer, vol. 236(2), pages 475-499, January.
    9. Burdett, Robert & Kozan, Erhan, 2016. "A multi-criteria approach for hospital capacity analysis," European Journal of Operational Research, Elsevier, vol. 255(2), pages 505-521.
    10. Peter Reiter & Walter Gutjahr, 2012. "Exact hybrid algorithms for solving a bi-objective vehicle routing problem," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(1), pages 19-43, March.
    11. Ioanna Makarouni & John Psarras & Eleftherios Siskos, 2015. "Interactive bicriterion decision support for a large scale industrial scheduling system," Annals of Operations Research, Springer, vol. 227(1), pages 45-61, April.
    12. Kirlik, Gokhan & Sayın, Serpil, 2014. "A new algorithm for generating all nondominated solutions of multiobjective discrete optimization problems," European Journal of Operational Research, Elsevier, vol. 232(3), pages 479-488.
    13. Di Martinelly, Christine & Meskens, Nadine, 2017. "A bi-objective integrated approach to building surgical teams and nurse schedule rosters to maximise surgical team affinities and minimise nurses' idle time," International Journal of Production Economics, Elsevier, vol. 191(C), pages 323-334.
    14. Ramos, Tânia Rodrigues Pereira & Gomes, Maria Isabel & Barbosa-Póvoa, Ana Paula, 2014. "Planning a sustainable reverse logistics system: Balancing costs with environmental and social concerns," Omega, Elsevier, vol. 48(C), pages 60-74.
    15. Angelo Aliano Filho & Antonio Carlos Moretti & Margarida Vaz Pato & Washington Alves Oliveira, 2021. "An exact scalarization method with multiple reference points for bi-objective integer linear optimization problems," Annals of Operations Research, Springer, vol. 296(1), pages 35-69, January.
    16. Calvete, Herminia I. & Galé, Carmen & Iranzo, José A., 2016. "MEALS: A multiobjective evolutionary algorithm with local search for solving the bi-objective ring star problem," European Journal of Operational Research, Elsevier, vol. 250(2), pages 377-388.
    17. Jabbarzadeh, Armin & Azad, Nader & Verma, Manish, 2020. "An optimization approach to planning rail hazmat shipments in the presence of random disruptions," Omega, Elsevier, vol. 96(C).
    18. Natashia Boland & Hadi Charkhgard & Martin Savelsbergh, 2015. "A Criterion Space Search Algorithm for Biobjective Integer Programming: The Balanced Box Method," INFORMS Journal on Computing, INFORMS, vol. 27(4), pages 735-754, November.
    19. 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.
    20. Lakmali Weerasena, 2022. "Advancing local search approximations for multiobjective combinatorial optimization problems," Journal of Combinatorial Optimization, Springer, vol. 43(3), pages 589-612, April.

    More about this item

    Keywords

    multi-objective; traveling salesman problem; set covering problem; ε-constraint; exact Pareto set;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

    Statistics

    Access and download statistics

    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:pra:mprapa:105074. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.