FUZZY COMBINATORIAL OPTIMIZATION WITH MULTIPLE RANKING CRITERIA: A STAGED TABU SEARCH FRAMEWORK

Traditional approaches to fuzzy optimization typically use a single criterion for ranking fuzzy variables, which often causes information losing and limits the decision-supporting capability. The paper presents a multi-stage tabu search framework for fuzzy combinatorial optimization problems, which takes multiple ranking criteria into consideration, performs heuristic search based on a preferred criterion at each stage, and returns a set of non-dominated solutions approximating the Pareto optimal set. Experiments and real-world applications show that our approach can achieve good solution quality with low computational costs to support effective decision-making.