Abstract
For some management programming problems, multiple objectives to be optimized rather than a single objective, and objectives can be expressed with ratio equations such as return/investment, operating profit/net-sales, profit/manufacturing cost, etc. In this paper, we proposed the transformation characteristics to solve the multi objective linear fractional programming (MOLFP) problems. If a MOLFP problem with both the numerators and the denominators of the objectives are linear functions and some technical linear restrictions are satisfied, then it is defined as a multi objective linear fractional programming problem MOLFPP in this research. The transformation characteristics are illustrated and the solution procedure and numerical example are presented.
Highlights
Management programming problems are based upon estimated values
Kornbluth and Steuer [4] have presented an algorithm for solving the multi objective linear fractional programming (MOLFP) by combining aspects of multiple objective, single objective fractional programming and goal programming
The general format of minimum MOLFPP is as the following equivalent multi objective linear programming problem: Min ciT y + α it, s.t
Summary
Management programming problems are based upon estimated values These problems have multiple objectives to be optimized rather than a single objective. For some management programming problems, objectives can be expressed in ratio equations such as return/investment, operating profit/net-sales, profit/manufacturing cost, etc These multiple objective fractional programming models were first studied by Luhandjula [6]. Luhandjula [6] solved MOLFP by applying fuzzy approach to overcome the computational difficulties of using conventional fractional programming approaches to solve multiple objective fractional programming problem. The approach stated that suitable transformation should have been applied to formulate an equivalent multi objective linear programming and the resulting multi objective linear programming could be solved based on fuzzy set theoretic approach. The transformation characteristics are illustrated and the solution procedure and numerical example presented
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