Multiobjective Stochastic Fractional Goal Programming Model for Water Resources Optimal Allocation among Industries

In this article a fuzzy goal programming model is developed to solve multiobjective unbalanced transportation problems with fuzzy random parameters. In model formulation process the cost coefficients of the objectives are considered as fuzzy numbers and the supplies and demands are considered as fuzzy random variables with known fuzzy probability distribution from the view point of probabilistic as well as possibilistic uncertainties involved with the model. A fuzzy programming model is first constructed by applying chance constrained programming methodology in fuzzy environment. Then, the model is decomposed on the basis of the tolerance ranges of the fuzzy numbers associated with it. The individual optimal solution of each decomposed objectives is found in isolation to construct the membership goals of the objectives. Finally, priority based fuzzy goal programming technique is used to achieve the highest degree of each of the defined membership goals to the extent possible by minimizing the under deviational variables and thereby obtaining optimal allocation of products by using distance function in a cost minimizing decision making environment. An illustrative example is solved and compared with existing technique to explore the potentiality of the proposed methodology.

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An inexact fractional programming model for irrigation water resources optimal allocation under multiple uncertainties

PLoS ONE ◽

10.1371/journal.pone.0217783 ◽

2019 ◽

Vol 14 (6) ◽

pp. e0217783

Author(s):

Chongfeng Ren ◽

Jiantao Yang ◽

Hongbo Zhang

Keyword(s):

Water Resources ◽

Irrigation Water ◽

Fractional Programming ◽

Optimal Allocation ◽

Programming Model ◽

Multiple Uncertainties ◽

Water Resources Optimal Allocation

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Multi-Water Resources Optimal Allocation Based on Multi-Objective Uncertain Chance-Constrained Programming Model

Water Resources Management ◽

10.1007/s11269-020-02697-z ◽

2020 ◽

Vol 34 (15) ◽

pp. 4881-4899

Author(s):

Xiaona Li ◽

Xiaosheng Wang ◽

Haiying Guo ◽

Weimin Ma

Keyword(s):

Water Resources ◽

Optimal Allocation ◽

Programming Model ◽

Chance Constrained Programming ◽

Multi Objective ◽

Constrained Programming ◽

Water Resources Optimal Allocation

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A Goal Programming Model for Capital Budgeting Decisions

Artha Vijnana Journal of The Gokhale Institute of Politics and Economics ◽

10.21648/arthavij/1981/v23/i3-4/116609 ◽

1981 ◽

Vol 23 (3-4) ◽

pp. 299

Author(s):

Jaidev Agarwal ◽

L. S. Porwal ◽

Kanti Swarup

Keyword(s):

Goal Programming ◽

Programming Model ◽

Capital Budgeting ◽

Goal Programming Model

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A Multicriteria Goal Programming Model for Ranking Universities

Mathematics ◽

10.3390/math9050459 ◽

2021 ◽

Vol 9 (5) ◽

pp. 459

Author(s):

Fernando García ◽

Francisco Guijarro ◽

Javier Oliver

Keyword(s):

Goal Programming ◽

Programming Model ◽

Decision Makers ◽

University Rankings ◽

Central Tendency ◽

Performance Variables ◽

World University Rankings ◽

Goal Programming Model ◽

Objective Quantification

This paper proposes the use of a goal programming model for the objective ranking of universities. This methodology has been successfully used in other areas to analyze the performance of firms by focusing on two opposite approaches: (a) one favouring those performance variables that are aligned with the central tendency of the majority of the variables used in the measurement of the performance, and (b) an alternative one that favours those different, singular, or independent performance variables. Our results are compared with the ranking proposed by two popular World University Rankings, and some insightful differences are outlined. We show how some top-performing universities occupy the best positions regardless of the approach followed by the goal programming model, hence confirming their leadership. In addition, our proposal allows for an objective quantification of the importance of each variable in the performance of universities, which could be of great interest to decision-makers.

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