Fuzzy goal programming for multiobjective transportation problems

Zangiabadi, Maryam; Maleki, Hamid Reza

doi:10.1007/bf02832333

Cited by 51 publications

(20 citation statements)

References 27 publications

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“…In goal programming, the decision maker indicates the goals that are the desired achievement levels of the objective functions [27]. In certain cases, the decision maker may also provide the relevant rankings of the achievement levels.…”

Section: Goal Programming a Background Informationmentioning

confidence: 99%

“…Goal programming implicitly assumes that a desired goal is always obtainable and that it can be used to converge to a compromised solution through some high-level intervention [27]. The information attainable during each of the iteration is the current best compromise solution, referred to as the Main Solution (MS), and a set of Possible Solutions (PS) that are the compromise solutions obtainable if each of the goals are satisfied serially [13].…”

Section: B a Generalized Goal Programming Proceduresmentioning

confidence: 99%

“…The optimization of the PS would result in x r and f r , which is known in the literature as the achievable solution [27].…”

Section: B a Generalized Goal Programming Proceduresmentioning

confidence: 99%

See 2 more Smart Citations

A goal programming approach for the joint optimization of energy consumption and response time in computational grids

Khan

2009

2009 IEEE 28th International Performance Computing and Communications Conference

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Abstract-We study the multi-objective problem of mapping independent tasks onto a set of computational grid machines that simultaneously minimizes the energy consumption and response time (makespan) subject to the constraints of deadlines and architectural requirements. We propose an algorithm based on goal programming that effectively converges to the compromised Pareto optimal solution. Compared to other traditional multi-objective optimization techniques that require identification of the Pareto frontier, goal programming directly converges to the compromised solution. Such a property makes goal programming a very efficient multi-objective optimization technique. Moreover, simulation results show that the proposed technique achieves superior performance compared to the greedy and linear relaxation heuristics, and competitive performance relative to the optimal solution implemented in LINDO for small-scale problems.

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Section: Goal Programming a Background Informationmentioning

confidence: 99%

Section: B a Generalized Goal Programming Proceduresmentioning

confidence: 99%

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A goal programming approach for the joint optimization of energy consumption and response time in computational grids

Khan

2009

2009 IEEE 28th International Performance Computing and Communications Conference

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“…Their approach mainly focuses on minimizing the worst upper bound to obtain an efficient solution which is close to the best lower bound of each objective function. Zangiabadi and Maleki (2007) presented a fuzzy goal programming approach to determine an optimal compromise solution for the multi-objective transportation problem with the same assumption. They assign a special type of non-linear (hyperbolic) membership function to each objective function to describe each fuzzy goal.…”

Section: Related Workmentioning

confidence: 99%

An Empirical Study on Multi-objective Transportation Problem in Fuzzy Environment

Mishra¹,

Panda²

2018

IJET

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For both in economical and social development of country transportation system plays a vital role. As it is directly involved with financial growth of the country, for that a complete well planned transportation infrastructure is necessary. Most of the transportation models are formulated with minimization of transportation cost as the basic objective. But consideration of transportation system with a single objective is not able to meet the various requirements of transportation industry for which it may not lead to the practical optimal solution. It bounds the decision makers (DMs) to consider several objectives at a time instead of single objective. To handle a multiobjective transportation problem with fixed parameters is a challenging issue; rather it is easy to consider all parameters in terms of linguistic variables. In this paper, a multi criteria multi-objective transportation models is formulated based on fuzzy relations under the fuzzy logic with several objectives like (i) minimization of total transportation cost and (ii) minimization of total transportation time. Another objective, maximization of the transported amount from a source to a destination is determined on the basis of previous two objectives. All the objectives are associated with multiple numbers of criteria like breakable items, shipping distance, service charge, mode of transportation etc. These relations are imprecise in nature and represented in terms of verbal words such as low, medium, high and very high. The fuzzy rule based multi-objective transportation problem is formulated and result is discussed.

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“…In other existing method like fuzzy multi-objective goal programming (FMOGP) method (Hwang and Lee 2004;Zangiabadi and Maleki 2007), initially the objective goal and the maximum tolerances for resources should be given. In the real-world situations, it is unrealistic to initially ask the decision maker (DM) to give goal and tolerances without providing any information about them.…”

Section: Introductionmentioning

confidence: 99%

An interactive weighted fuzzy goal programming technique to solve multi-objective reliability optimization problem

Kundu

Islam

2019

J Ind Eng Int

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This paper presents an application of interactive fuzzy goal programming to the nonlinear multi-objective reliability optimization problem considering system reliability and cost of the system as objective functions. As the decision maker always have an intention to produce highly reliable system with minimum cost, therefore, we introduce the interactive method to design a high productivity system here. This method plays an important role to maximize the worst lower bound to obtain the preferred compromise solution which is close to the best upper bound of each objective functions. Until the preferred compromise solution is reached, new lower bounds corresponding to each objective functions will be determined based on the present solution to develop the updated membership functions as well as aspiration levels to resolve the proposed problem. Considering judgmental vagueness of decision maker, here we consider the resources as trapezoidal fuzzy numbers and apply total integral value of fuzzy number to transform into crisp one. To illustrate the methodology and performance of this approach, numerical examples are presented and evaluated by comparing with the other method at the end of this paper.

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Fuzzy goal programming for multiobjective transportation problems

Cited by 51 publications

References 27 publications

A goal programming approach for the joint optimization of energy consumption and response time in computational grids

A goal programming approach for the joint optimization of energy consumption and response time in computational grids

An Empirical Study on Multi-objective Transportation Problem in Fuzzy Environment

An interactive weighted fuzzy goal programming technique to solve multi-objective reliability optimization problem

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