Solution proposal to fuzzy multiobjective linear fractional transportation problem

Transportation problems and their solution techniques play an important role in logistics and supply chain management for reducing cost and improving service. While having a transportation process from supply points with limited capacity to demand points or consumption centers with definite demands, more than one criteria can be optimized at the same time. The minimization of the cost, fuel consumption and average distribution time to the customers with high priority can be given as examples of such criteria. In addition some of those criteria can be in a fractional structure such as profit/cost or profit/time or the maximization of profitability ratio. We called those type of transportation problems as Multi-Objective Linear Fractional Transportation Problems (MLFTP).In this study the MLFTP whose objectives are the ratios of two linear functions and whose constraints are the transportation problem's constraints is dealt with and fuzzy solution proposals for this problem are proposed.This study consists of five sections.In the first section we called it as introduction, we outlined the subjects to be deal with in this thesis.In the second section, fuzzy set theory and fuzzy decision making; in the third section, linear fractional programming (LFP); in the fourth section, the classical transportation problem, the multiobjective transportation problem and the linear fractional transportation problem that are subtitles of the transportation problems, were studied.In the fifth section, which is the original part of our study, we gave the MLFTP formulization and basic theorems about the solvability of the problem. Defining Pareto-optimal, weak Pareto-optimal and compromise solution concepts for this problem, we offered fuzzy solution proposals using fuzzy approaches.Our fuzzy approaches are groupped under two basic topics according to the structure of their membership functions: approaches using lineer membership functions and approaches using non-lineer membership functions. The execution for each approach is displayed on the same basic sample problem.