USING SOME FUZZY AGGREGATION OPERATORS FOR MULTI-OBJECTIVE LINEAR TRANSPORTATION PROBLEM

Abstract

Using fuzzy aggregation operators, compensatory fuzzy approaches can be proposed for multi-objective problems. The variety of operators for the aggregation of objectives might be confusing and might make it difficult to decide which one to apply to the problem. For example while Zimmermann's min operator provides numerical efficiency, it does not guarantee compensatory and Pareto-optimality. In this paper, we present brief information about some important compensatory fuzzy aggregation operators and then apply them to the Multi-objective Linear Transportation Problem (MOLTP) to obtain a compensatory compromise Pareto-optimal solution set. And an illustrative example is provided to compare these aggregation operators and to conclude which operator is more appropriate for the concerning problem.