Evaluating solutions and solution sets under multiple objectives

KARAKAYA G., Köksalan M.

European Journal of Operational Research, vol.294, no.1, pp.16-28, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 294 Issue: 1
  • Publication Date: 2021
  • Doi Number: 10.1016/j.ejor.2021.01.021
  • Journal Name: European Journal of Operational Research
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, International Bibliography of Social Sciences, ABI/INFORM, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Computer & Applied Sciences, EconLit, INSPEC, Public Affairs Index, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.16-28
  • Keywords: Multiple objective programming, Tchebycheff function, Weight set partitioning, APPROXIMATE SOLUTION SETS, OPTIMIZATION PROBLEMS
  • Middle East Technical University Affiliated: Yes


© 2021 Elsevier B.V.In this study we address evaluating solutions and solution sets that are defined by multiple objectives based on a function. Although any function can be used, we focus on mostly weighted Tchebycheff functions that can be used for a variety of purposes when multiple objectives are considered. One such use is to approximate a decision maker's preferences with a Tchebycheff utility function. Different solutions can be evaluated in terms of expected utility conditional on weight values. Another possible use is to evaluate a set of solutions that approximate a Pareto set. It is not straightforward to find the Pareto set, especially for large-size multi-objective combinatorial optimization problems. To measure the representation quality of approximate Pareto sets and to compare such sets with each other, there are some performance indicators such as the hypervolume measure, the ε indicator, and the integrated preference functional (IPF) measure. A Tchebycheff function based IPF measure can be used to estimate how well a set of solutions represents the Pareto set. We develop the necessary theory to practically evaluate solutions and solution sets. We develop a general algorithm and demonstrate it for two, three, and four objectives.