Abstarct: Abstract Fractional linear programming is an optimization frxamework in which the decision criterion is expressed as the ratio of two linear functions (e.g., profit to cost) and is widely used for modeling efficiency-oriented problems. This thesis presents the mathematical structure of such problems and explains the standard and general forms, the concepts of feasibility and optimality, and the well-definedness condition of the objective function (in particular, positivity of the denominator over the feasible region). It then examines the relationship between linear fractional programming (LFP) and classical linear programming, along with the rules for transforming constraints and variables to obtain equivalent formulations. Next, the main solution approaches—namely the Charnes–Cooper transformation and the Dinkelbach algorithm—are analyzed, and several dual construction routes (including direct duals and formulations baxsed on fractional Lagrangians) are presented together with weak and strong duality results. In the numerical section, deterministic examples are solved and compared using both methods; the results show that the two approaches are equivalent in terms of accuracy, while the Charnes–Cooper method is advantageous in simplicity and computational cost. Overall, the