Solving Multi-objective Multi-choice Stochastic Transportation Problem with Fuzzy Programming Approach

Nasseri, Seyed Hadi; Bavandi, Salim

doi:10.1109/cfis49607.2020.9238695

Cited by 12 publications

(4 citation statements)

References 5 publications

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“…The terms for TP that are modelled under such circumstances are fuzzy transportation problems (FTP) and stochastic transportation problems (STP). Comparision of the TP with different authors defined on Gessesse [16], Jerbi [21], Al Qahtani et al [30], Nasseri and Bavandi [28], Dutta et al [13], Mahapatra et al [26], Agrawal and Ganesh [5], Das…”

Section: Introductionmentioning

confidence: 91%

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: Introductionmentioning

confidence: 91%

The Stochastic Transportation Problem with Imprecise Data using Lomax Distribution

Pulloru Bhavana,

Priya

2024

Eur. J. Pure Appl. Math.

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“…Various studies that had been conducted emphasize the need of inventory models in real world problems such as pharmaceutical inventory [22], textile industries [23], grocery retail [24], and many more. In particular, case studies that contain uncertain parameters can be found in numerous reports from various fields such as supply chain management [25], mechanical devices [26,27], and transportation [28], further demonstrating that uncertain parameters naturally occur in real world problems.…”

Section: Introductionmentioning

confidence: 99%

Integrated Supplier Selection and Inventory Management Problems Considering Discounts and Uncertainty: A Fuzzy-Probabilistic Approach

Sutrisno,

Widowati,

Sunarsih

et al. 2023

JESA

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This study addresses the integration of supplier selection and inventory management issues that involve discounts, with a unified decision-making support formulated through a mathematical programming approach. The challenges addressed encompass uncertain parameters such as defective goods rates, late delivery rates, and demand, some of which are treated as probabilistic/random variables under data availability assumptions, while others are managed as fuzzy variables where data is not explicitly required. The joint problems were synthesized into a piecewise fuzzy-probabilistic optimization model with the aim of minimizing total operational costs, and the optimal decision was deduced by solving this model. Further, the model was constructed incorporating multi-period observations, indicating its ability to generate optimal solutions for multiple procurement activity periods. Computational simulations were executed to demonstrate the calculation of the optimal decision and to appraise the proposed model. All calculations were performed in LINGO 19.0 optimization software, leveraging its uncertain programming package. The computational process employed the generalized reduced gradient -a popular method for solving optimization problems due to its requirement of only a differentiable objective function -in conjunction with the branch and bound algorithmrecognized for its simplicity in branching and bounding feasible solutions. The results affirmed that the proposed model successfully delivered the optimal solution for the problem at hand. Therefore, the proposed model is deemed appropriate for implementation by practitioners in manufacturing/retail industries as a decision-making tool to curtail operational costs associated with their procurement and warehousing operations.

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“…Roy et al [38] introduced the conic scalarization approach to solve the multiobjective TP with an interval goal under a multichoice environment. Nasseri [39] solved MOTP with multi-choice parameters where the alternative choices of the parameters were taken as random variables. They converted the multi-choice parameters into a single choice using the interpolating polynomials and obtained the solution by utilizing fuzzy approach.…”

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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Solving Multi-objective Multi-choice Stochastic Transportation Problem with Fuzzy Programming Approach

Cited by 12 publications

References 5 publications

The Stochastic Transportation Problem with Imprecise Data using Lomax Distribution

The Stochastic Transportation Problem with Imprecise Data using Lomax Distribution

Integrated Supplier Selection and Inventory Management Problems Considering Discounts and Uncertainty: A Fuzzy-Probabilistic Approach

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

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