Dealing with a Transportation Problem with Multi Choice Cost Coefficients and Fuzzy Supplies and Demands

Nayak, Jadunath; Acharya, Srikumar

doi:10.37622/gjpam/16.6.2020.783-788

Cited by 2 publications

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References 2 publications

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“…They converted the multi-choice parameters into a single choice using the interpolating polynomials and obtained the solution by utilizing fuzzy approach. Nayak et al [40] studied the TP with transportation cost as multi-choices and other parameters like supply and demand as fuzzy trapezoidal numbers. They used binary variable approach to choose a single choice among the multiple choices of the parameters.…”

Section: Introductionmentioning

confidence: 99%

A belief-degree based multi-objective transportation problem with multi-choice demand and supply

Kakran

Dhodiya

2022

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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This paper focusses on the development of a Multi-choice Multi-objective Transportation Problem (MCMOTP) in the uncertain environment. The parameters associated with the objective functions in MCMOTP are regarded as uncertain variables and the other parameters associated with supply capacity and demand requirements are considered under the multi-choice environment. In this paper, two ranking criteria have been utilized to convert the uncertain objectives into their crisp form. Using these two ranking criteria for the uncertain MCMOTP model, two deterministic models have been developed namely, Expected Value Model (EV Model) and Optimistic Value Model (OV Model). The multi-choice parameters in the constraints are converted to a single choice parameters with the help of binary variable approach. The EV and OV models are solved directly in the LINGO 18.0 software using minimizing distance method and fuzzy programming technique. At last, a numerical illustration is provided to demonstrate the application and algorithm of the models. The sensitivity of the objective functions in OV Model is also examined with respect to the confidence levels to investigate variation in the objective functions.

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Section: Introductionmentioning

confidence: 99%

A belief-degree based multi-objective transportation problem with multi-choice demand and supply

Kakran

Dhodiya

2022

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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The PSK Method

Kumar¹

2023

Advances in Logistics, Operations, and Management Science

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We have several logics to obtain the fuzzy optimal solution for fuzzy transportation problems (FTPs). But there is no logic to solve transportation problem (TP) under four different uncertain environments using a particular method in the literature. So, the author divided the TP under four different environments and formulates the problems and utilizes the fuzzy numbers to solve them. A method, namely PSK (P. Senthil Kumar) method, to find the fuzzy optimal solutions to the FTPs is proposed. Applications of the PSK method compared to other existing methods are demonstrated with four different numerical examples. To illustrate the PSK method, different types of FTPs are solved by using the PSK method with the help of software, for example, Lingo, Matlab, Tora, RGui, and RStudio, and the obtained results are compared, analyzed, and discussed. Conclusions on the study and recommendations for future research are also given.

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Dealing with a Transportation Problem with Multi Choice Cost Coefficients and Fuzzy Supplies and Demands

Cited by 2 publications

References 2 publications

A belief-degree based multi-objective transportation problem with multi-choice demand and supply

A belief-degree based multi-objective transportation problem with multi-choice demand and supply

The PSK Method

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