A fuzzy approach to the multiobjective transportation problem

Li, Lushu; Lai, Kin Keung

doi:10.1016/s0305-0548(99)00007-6

Cited by 183 publications

(83 citation statements)

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“…The stringent notion of optimality must be dropped when considering multi-objective problems. In general, a solution that optimizes one objective function may not optimize other objective functions [13].…”

Section: Goal Programming a Background Informationmentioning

confidence: 99%

“…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]. Iteratively, goal programming, identifies non-inferior solutions and refines them to achieve the best possible compromise solution.…”

Section: B a Generalized Goal Programming Proceduresmentioning

confidence: 99%

“…The power conservation condition of (13) states that the instantaneous power allocated is bounded. That is, at any given instance, the total power consumption of all of the machines must be less than when all of the machines are running at their peak power consumption.…”

Section: Conditions Of Optimalitymentioning

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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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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“…Bit.et.al [6], have also presented an additive fuzzy programming model that considers weights and priorities for all non-equivalent objectives for the MOTP problem. A fuzzy compromise programming method to obtain a non-dominated compromise solution to the MOTP was developed by Li and Lai [10]. Fuzzy programming techniques with hyperbolic and exponential membership functions to obtain optimal compromise solutions of the MOTP were introduced by Verma et.al [11].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Programming With Quadratic Membership Functions For Multi-objective Transportation Problem

Akkapeddi

2015

Pak.j.stat.oper.res.

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In the present paper, a fuzzy programming model with quadratic membership functions has been developed for the solution of a Multi-Objective Transportation problem. In literature, several fuzzy programming approaches exist with various types of membership functions such as linear, exponential, hyperbolic etc. These membership functions are defined, by taking the lower and upper values of the objective functions into account. In some cases, these methods fail to obtain an integer compromise optimal solution. In the present method, two coefficients of the quadratic membership functions are determined by the lower and upper values of the objective functions. The other coefficient is taken as a variable in the fuzzy programming approach. This means that the membership curve is fixed at the two end points and set free in between. Application of the method on numerical examples proved that the approach could generate integer compromise optimal solutions.

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“…Verma et al [11], Bit at al. [12,13], Jimenez and Verdegay [14], Li and Lai [15] and Waiel [16] presented the fuzzy compromise programming approach to multi-objective transportation problem.…”

Section: Introductionmentioning

confidence: 99%

A Solid Transportation Problem with Partial Non-Linear Transportation Cost

Mond¹

2014

J Appl Computat Math

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This paper deals with a capacitated, multi-objective and solid transportation problem with imprecise nature of resources, demands, capacity of conveyance and cost. Here transportation cost is inversely varying with the quantity-to be transported from source to destinations in addition with a fixed per unit cost and a small vehicle cost. The transportation problem has been formulated as a constrained fuzzy non-linear programming problem. Next it is transformed into an equivalent crisp multi-objective problem using fuzzy interval approximation and then solved by Interactive Fuzzy Programming Technique (IFPT) and Generalized Reduced Gradient (GRG) method. An illustrative numerical example is demonstrated to find the optimal solution of the proposed model.

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A fuzzy approach to the multiobjective transportation problem

Cited by 183 publications

References 0 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

Fuzzy Programming With Quadratic Membership Functions For Multi-objective Transportation Problem

A Solid Transportation Problem with Partial Non-Linear Transportation Cost

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