Fuzzy mathematical programming for multi objective linear fractional programming problem

Chakraborty, Mohuya; Gupta, Surbhi

doi:10.1016/s0165-0114(01)00060-4

Cited by 148 publications

(105 citation statements)

References 14 publications

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“…However, the conventional TSP models cannot optimize the system marginal benefit represented as system maximum output with per unit of input. In real-world agricultural water management problems, the ratio functions of profit and cost are often conflicted in nature, such that types of problems are inherently multiobjective fractional problems [13].…”

Section: Introductionmentioning

confidence: 99%

Planning an Agricultural Water Resources Management System: A Two-Stage Stochastic Fractional Programming Model

Cui

Huang

2015

Sustainability

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Irrigation water management is crucial for agricultural production and livelihood security in many regions and countries throughout the world. In this study, a two-stage stochastic fractional programming (TSFP) method is developed for planning an agricultural water resources management system under uncertainty. TSFP can provide an effective linkage between conflicting economic benefits and the associated penalties; it can also balance conflicting objectives and maximize the system marginal benefit with per unit of input under uncertainty. The developed TSFP method is applied to a real case of agricultural water resources management of the Zhangweinan River Basin China, which is one of the main food and cotton producing regions in north China and faces serious water shortage. The results demonstrate that the TSFP model is advantageous in balancing conflicting objectives and reflecting complicated relationships among multiple system factors. Results also indicate that, under the optimized irrigation target, the optimized water allocation rate of Minyou Channel and Zhangnan Channel are 57.3% and 42.7%, respectively, which adapts the changes in the actual agricultural water resources management problem. Compared with the inexact two-stage water management (ITSP) method, TSFP could more effectively address the sustainable water management problem, provide more information regarding tradeoffs between multiple input factors and system benefits, and help the water managers maintain sustainable water resources development of the Zhangweinan River Basin. OPEN ACCESSSustainability 2015, 7 9847

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Section: Introductionmentioning

confidence: 99%

Planning an Agricultural Water Resources Management System: A Two-Stage Stochastic Fractional Programming Model

Cui

Huang

2015

Sustainability

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“…Also, changing the weights of the objective functions by solving the linearization models (1), (2) and (3) in both variants, we have concluded that the model (1) is not sensitive to the changes of the objective function weights in both variants. The linearization models (2) and (3) in our example give the same solution. It is because the linearization models (2) and (3) differ only in the fact that model (2) contains the additional constraints…”

Section: The Model Solvingmentioning

confidence: 63%

“…, , , 0 , , , (8) we solve the following models: Tables 3 and 4 show that the solutions of the problem solved by model (1) are different from the solutions obtained by models (2) and (3) in both versions. It should be noted that the objective function weights in versions (a) and (b) are different.…”

Section: The Model Solvingmentioning

confidence: 99%

“…The problem of linear fractional programming with one objective function was extensively researched in the second half of the twentieth century and efficient methods were developed for solving such problems ( [3], [4]). Solving multiobjective linear fractional programming models is limited to a small number of inadequately effective multi-objective programming methods ( [2], [7], [8], [9], [11], [12], [14], [16], [17], [18], [21], [22]). The most effective method for solving multi-objective linear fractional programming models is the linear goal programming method based on the simplex algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the efficiency of the linearization techniques for solving multi-objective linear fractional programming problems by goal programming

Perić

Babić

Omerović

2017

CRORR

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Abstract. This paper presents and analyzes the applicability of three linearization techniques used for solving multi-objective linear fractional programming problems using the goal programming method. The three linearization techniques are: (1) Taylor's polynomial linearization approximation, (2) the method of variable change, and (3) a modification of the method of variable change proposed in [20]. All three linearization techniques are presented and analyzed in two variants: (a) using the optimal value of the objective functions as the decision makers' aspirations, and (b) the decision makers' aspirations are given by the decision makers. As the criteria for the analysis we use the efficiency of the obtained solutions and the difficulties the analyst comes upon in preparing the linearization models. To analyze the applicability of the linearization techniques incorporated in the linear goal programming method we use an example of a financial structure optimization problem.

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“…Calvete and Galé [19] studied optimality conditions for LFBLPP. Ahlatcioglu and Tiryaki [20] developed two interactive fuzzy programming algorithms for decentralized two-level linear fractional programming problem by using the technique of multiobjective linear fractional programming problem due to Chakraborty and Gupta [21], and Charnes and Cooper [22]. Mishra [23] discussed weighting method for LFBLPP by using analytical hierarchy process [24].…”

Section: Introductionmentioning

confidence: 99%

Quadratic Bi-level Programming Problem Based On Fuzzy Goal Programming Approach

Pramanik¹,

Dey²,

Ghosh³

et al. 2011

IJSEA

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Fuzzy mathematical programming for multi objective linear fractional programming problem

Cited by 148 publications

References 14 publications

Planning an Agricultural Water Resources Management System: A Two-Stage Stochastic Fractional Programming Model

Planning an Agricultural Water Resources Management System: A Two-Stage Stochastic Fractional Programming Model

Analysis of the efficiency of the linearization techniques for solving multi-objective linear fractional programming problems by goal programming

Quadratic Bi-level Programming Problem Based On Fuzzy Goal Programming Approach

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