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Solving discrete multi-objective optimization problems using modified augmented weighted Tchebychev scalarizations

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  • Holzmann, Tim
  • Smith, J.C.

Abstract

In this paper we present the modified augmented weighted Tchebychev norm, which can be used to generate a complete efficient set of solutions to a discrete multi-objective optimization problem. We contribute a generating algorithm that will, without supervision, generate the entire non-dominated set for any number of objectives. To our knowledge, this is the first generating method for general discrete multi-objective problems that uses a variant of the Tchebychev norm. In a computational study, our algorithm’s running times are comparable to previously proposed algorithms.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:271:y:2018:i:2:p:436-449
    DOI: 10.1016/j.ejor.2018.05.036
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    3. Chi-Yo Huang & Jih-Jeng Huang & You-Ning Chang & Yen-Chu Lin, 2021. "A Fuzzy-MOP-Based Competence Set Expansion Method for Technology Roadmap Definitions," Mathematics, MDPI, vol. 9(2), pages 1-26, January.
    4. Tsionas, Mike G., 2019. "Multi-objective optimization using statistical models," European Journal of Operational Research, Elsevier, vol. 276(1), pages 364-378.
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    6. Tim Holzmann & J. Cole Smith, 2019. "Shortest path interdiction problem with arc improvement recourse: A multiobjective approach," Naval Research Logistics (NRL), John Wiley & Sons, vol. 66(3), pages 230-252, April.
    7. Argyris, Nikolaos & Karsu, Özlem & Yavuz, Mirel, 2022. "Fair resource allocation: Using welfare-based dominance constraints," European Journal of Operational Research, Elsevier, vol. 297(2), pages 560-578.
    8. Karakaya, G. & Köksalan, M., 2021. "Evaluating solutions and solution sets under multiple objectives," European Journal of Operational Research, Elsevier, vol. 294(1), pages 16-28.
    9. Stephan Helfrich & Tyler Perini & Pascal Halffmann & Natashia Boland & Stefan Ruzika, 2023. "Analysis of the weighted Tchebycheff weight set decomposition for multiobjective discrete optimization problems," Journal of Global Optimization, Springer, vol. 86(2), pages 417-440, June.
    10. Salo, Ahti & Andelmin, Juho & Oliveira, Fabricio, 2022. "Decision programming for mixed-integer multi-stage optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 299(2), pages 550-565.
    11. Zhang, Xingmin & Zhang, Shuai, 2021. "Optimal time-varying tail risk network with a rolling window approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).

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