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Related Experiment Video

Updated: Sep 13, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Enhance triangular fuzzy parametric framework for solid multi objective transportation problem with split decision

Vishwas Deep Joshi1, Medha Sharma2, Lenka Čepová3

  • 1Department of Mathematics, Faculty of Science, JECRC University, Jaipur, Rajasthan, India. vdjoshi.or@gmail.com.

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|July 29, 2025
PubMed
Summary
This summary is machine-generated.

This study presents a new fuzzy optimization method for uncertain transportation problems. It enhances decision-making by converting fuzzy data into precise models, improving logistics planning.

Keywords:
Accuracy parameterEuclidean distanceExponential membership functionFuzzy parameter based multi objective transportation problemFuzzy programmingPreferred compromise solution

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Area of Science:

  • Operations Research
  • Fuzzy Optimization
  • Logistics Management

Background:

  • Transportation problems often involve uncertain parameters like supply, demand, and costs.
  • Existing methods struggle with flexibility and resilience in dynamic environments.
  • Decision-making requires robust strategies to manage inherent uncertainties.

Purpose of the Study:

  • To introduce a novel two-step generalized parametric approach for Fuzzy Multi-Objective Transportation Problems (FMOTPs).
  • To enhance decision-making capabilities in logistics and transportation systems under uncertainty.
  • To provide a computationally efficient framework for fuzzy optimization in transportation planning.

Main Methods:

  • Utilizes Triangular Fuzzy Numbers (TFNs) and a parametric accuracy parameter μ ∈ [0,1].
  • Transforms imprecise fuzzy data into a sequence of Crisp Multi-Objective Transportation Problems (CMOTPs).
  • Employs Fuzzy Linear Programming (FLP) to solve CMOTPs, selecting solutions based on Euclidean distance from the fuzzy ideal.

Main Results:

  • The proposed method demonstrates superior performance compared to existing models (Nomani's approach, fuzzy DEA, GRA).
  • Achieves enhanced optimality proximity, solution stability, and ranking accuracy.
  • Numerical case studies validate the effectiveness and efficiency of the approach.

Conclusions:

  • The novel parametric approach offers a systematic and efficient framework for addressing FMOTPs.
  • Improves managerial capacity to handle uncertainty and optimize trade-offs in transportation.
  • The model is scalable and suitable for integration into enterprise logistics systems across various industries.