Application Of Fuzzy Goal Programming Research Articles

LAND ALLOCATION PLANNING IN AGRICULTURAL SYSTEMS UNDER UNCERTAINTY: A CHANCE-CONSTRAINED APPROACH IN INEXACT DECISION-MAKING ENVIRONMENTS

This paper explores the application of fuzzy goal programming (FGP) for modeling and solving land allocation problems with chance constraints, aimed at optimizing the production of seasonal crops within agricultural systems in uncertain environments. The decision-making environment in agricultural systems is often characterized by imprecision, particularly due to unpredictable rainfall patterns and limited availability of irrigation water, which are influenced by socio-economic factors. These uncertainties pose significant challenges in real-world agricultural scenarios. The proposed model incorporates fuzzy descriptions for the utilization of cultivable land, farming resources, and the achievement of production goals for seasonal crops. Water supply, as a critical resource, and socio-economic constraints are represented probabilistically within this uncertain decision-making framework. The model addresses land-use planning for the cultivation of five major crops—Paddy, Wheat, Mustard, Potato, and Pulses—across three crop cycles: Pre-Kharif, Kharif, and Rabi, throughout the planning year for the Bardhaman district of West Bengal, India. The solution process focuses on maximizing the membership value (unity) of the fuzzy goals, reflecting the aspirations of the decision maker (DM) as closely as possible, based on their needs and objectives. The potential of this approach is demonstrated through a case study of Bardhaman district, West Bengal, India, illustrating how FGP can be used to address agricultural planning challenges under uncertain conditions.

Hospital Supply Chain Optimization under Uncertainty: Application of Fuzzy Goal Programming

Hospital Supply Chain Optimization under Uncertainty: Application of Fuzzy Goal Programming

Sustainable cropping pattern in North Iran: application of fuzzy goal programming

Due to the important role that the application of mathematical programming models have in determining optimal cropping patterns, this research presents a sustainable cropping pattern that considers selected economic, environmental, and social goals together. Using a random sampling method, a sample size of 168 farmers was selected in the Sari County, Iran. Our results showed that economic, self-sufficiency, environmental, and social goals have a distinctly different impact on cropping pattern performance. Compared to the current cropping pattern, the gross margins for economic and social goals increased by nearly 11 and 2 %, respectively, and the gross margins for self-sufficiency and environmental goals decreased by nearly 2 and 36 %. Interestingly, it has been found that the performance of the current cropping pattern has an average positive impact of 6 % if economic, self-sufficiency, environmental, and social (employment) goals are realized simultaneously.

Nutrient management system for sustainable sugarcane production: an application of fuzzy goal programming and decision theoretic approach

This paper presents a nutrient management model for sustainable sugarcane production. It will help sugarcane farmers to make better nutrient management decisions to achieve greater sugarcane yield at a minimum cost. The decision making environment for such a model is highly uncertain and conflicting. As a consequence, a new solution technique which combines fuzzy goal programming and decision theory for selecting the fertiliser combinations subject to fertiliser expenditures has been proposed. A case study from Indian perspective has been demonstrated to show the effectiveness of the proposed technique.

Fractional Goal Programming for Fuzzy Solid Transportation Problem with Interval Cost

In this paper, we study a solid transportation problem with interval cost using fractional goal programming approach (FGP). In real life applications of the FGP problem with multiple objectives, it is difficult for the decision-maker(s) to determine the goal value of each objective precisely as the goal values are imprecise, vague, or uncertain. Therefore, a fuzzy goal programming model is developed for this purpose. The proposed model presents an application of fuzzy goal programming to the solid transportation problem. Also, we use a special type of non-linear (hyperbolic) membership functions to solve multi-objective transportation problem. It gives an optimal compromise solution. The proposed model is illustrated by using an example.

FUZZY GOAL PROGRAMMING TECHNIQUE TO SOLVE MULTIOBJECTIVE TRANSPORTATION PROBLEMS WITH SOME NON-LINEAR MEMBERSHIP FUNCTIONS

The linear multiobjective transportation problem is a special type of vector minimum problem in which constraints are all equality type and the objectives are conicting in nature. This paper presents an application of fuzzy goal programming to the linear multiobjective transportation problem. In this paper, we use a special type of nonlinear (hyperbolic and exponential) membership functions to solve multiobjective transportation problem. It gives an optimal compromise solution. The obtained result has been compared with the solution obtained by using a linear membership function. To illustrate the methodology some numerical examples are presented.

An application of fuzzy goal programming to a multiobjective project network problem

This paper presents a practical application of fuzzy goal programming (FGP) in a real-life project network problem with two objectives as minimum completion time and crashing costs wanted to be optimized simultaneously. Fuzziness in the problem stems from the imprecise aspiration levels attained by the decision maker (DM) to both objectives. These imprecise aspiration levels are quantified through the use of piecewise linear and continuous membership functions. The objective function of the FGP is to maximize the membership value of both objectives’ intersection which form the fuzzy decision. The problem is solved by using LINDO computer package and the best compromised solution is found. Comparisons between solutions of FGP, fuzzy linear programming (FLP), lexicographic maximization method (LMM) and linear programming (LP) are also presented.

Fuzzy goal programming in forestry: An application with special solution problems

This paper documents a practical application of fuzzy goal programming in forestry which is extremely difficult to solve using standard linear programming codes on mainframe computers. The application considers selection of the sequence of timber harvests applied over time to a forest. The objective of the fuzzy goal program is to maximize the minimum periodic harvest across all periods. This approach generates solutions with nearly even harvest flows, and with significantly higher present net value than the traditional approach with crisp constraints. The solution difficulty results from the mutually exclusive nature of the harvest decision. The problem arises when many of the fuzzy goals (periodic harvests) are identical and minimal in the current tableau. In this situation, no single pivot step can increase the value of the objective function, and termination generally occurs at a suboptimal solution. Solution performance using several different software products on computers produced by two venders (IBM and UNIVAC (now UNISYS))_are discussed. The problem is solved through numeric tolerance modifications using an algorithm that is relatively less sensitive to termination criteria.