Genetic algorithm based fuzzy programming approach for multi-objective linear fractional stochastic transportation problem involving four-parameter Burr distribution

Gessesse, Adane Abebaw; Mishra, Rajashree; Acharya, M.; Das, Kedar Nath

doi:10.1007/s13198-019-00928-0

Cited by 11 publications

(3 citation statements)

References 22 publications

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“…This study offers a useful paradigm that the decision making may use to handle some unpredictable variables without compromising customer reliability. Agrawal [4], Aruna Chalam [9], Giri et al [18][19], Acharya et al [1], Gessesse [17], Maity et al [27] were solved the STP articles that involve both fuzziness and randomness. This study might be expanded to include additional areas where plans or decisions must be made under unknown circumstances.…”

Section: And Leementioning

confidence: 99%

The Stochastic Transportation Problem with Imprecise Data using Lomax Distribution

Pulloru Bhavana,

Priya

2024

Eur. J. Pure Appl. Math.

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The stochastic transportation problem with imprecise data is a probabilistic chance-constrained programming (CCP) problem in which the objective function is fuzzy and the supply and demand are random. Three models for the STP with ID with mixed-type restrictions that follow the Lomax distribution (LD) are created in this research. Optimizing the transportation cost in FTP under probabilistic mixed constraints is the goal of the research project. To do this, the probabilistic mixed constraints are transformed into deterministic form using the LD, and the cost coefficient of the fuzzy objective function is changed with alpha cut representation. Numerical examples are presented to demonstrate the suggested models.

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Section: And Leementioning

confidence: 99%

The Stochastic Transportation Problem with Imprecise Data using Lomax Distribution

Pulloru Bhavana,

Priya

2024

Eur. J. Pure Appl. Math.

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“…Yang and Feng [20] studied a bi-criteria STP with a fixed-charge cost under a stochastic environment. Gessesse et al [21] studied a multi-objective linear fractional transportation problem in a stochastic environment.…”

Section: Literature Reviewmentioning

confidence: 99%

A Multi-Objective Stochastic Solid Transportation Problem with the Supply, Demand, and Conveyance Capacity Following the Weibull Distribution

Das

Lee

2021

Mathematics

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This study addresses a multi-objective stochastic solid transportation problem (MOSSTP) with uncertainties in supply, demand, and conveyance capacity, following the Weibull distribution. This study aims to minimize multiple transportation costs in a solid transportation problem (STP) under probabilistic inequality constraints. The MOSSTP is expressed as a chance-constrained programming problem, and the probabilistic constraints are incorporated to ensure that the supply, demand, and conveyance capacity are satisfied with specified probabilities. The global criterion method and fuzzy goal programming approach have been used to solve multi-objective optimization problems. Computational results demonstrate the effectiveness of the proposed models and methodology for the MOSSTP under uncertainty. A sensitivity analysis is conducted to understand the sensitivity of parameters in the proposed model.

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“…Mehrjerdi [36] introduced the concept of fractional programming problems through fuzzy goal settings and approximation. Gessesse et al [37] provided the genetic algorithm-based fuzzy programming approach for multi-objective linear fraction stochastic transportation problems with four-parameter Burr distribution. Quadratic programming [38] with fuzzy parameters was also studied in which a membership function-based approach is used to handle the problem.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Optimization Technique for Multi-Objective Aspirational Level Fractional Transportation Problem

et al. 2021

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In this research work, a soft computing optimization operating approach is developed for a multi-objective aspirational level fractional transportation problem. In the proposed technique, a mathematical model is formulated for the multi-objective aspirational level fractional transportation problem (MOFTP) based on the highest value of one and all objectives of the model. We also used the symmetry concept over our model to identify the best optimum solution based on symmetrical data. We constructed the membership grades for the set of fetched parameters having symmetry. In this work, we also used the concept of ranking function in our mathematical model to obtain the optimum solution of the fuzzy multi-objective fractional transportation. In this proposed algorithm, the aspiration levels are also associated with the objective function of MOFTP. We are also proposing a new approach for the optimization of fractional problems in which the objectives are being optimized by using the numerator function and denominator function simultaneously. Further, a methodology is also developed to find the average cost for each fractional objective of the model. After that, we will find the ranking function for each parameter by using the defuzzification method. By this methodology, we will be able to convert the MOFTP into a bi-objective transportation problem. The provided technique is elaborated with the help of numerical computations to prove the beauty and power of the proposed technique.

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Genetic algorithm based fuzzy programming approach for multi-objective linear fractional stochastic transportation problem involving four-parameter Burr distribution

Cited by 11 publications

References 22 publications

The Stochastic Transportation Problem with Imprecise Data using Lomax Distribution

The Stochastic Transportation Problem with Imprecise Data using Lomax Distribution

A Multi-Objective Stochastic Solid Transportation Problem with the Supply, Demand, and Conveyance Capacity Following the Weibull Distribution

A Fuzzy Optimization Technique for Multi-Objective Aspirational Level Fractional Transportation Problem

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