Enhance triangular fuzzy parametric framework for solid multi objective transportation problem with split decision variables

This paper introduces a novel two-step generalized parametric approach for addressing Fuzzy Multi-Objective Transportation Problems (FMOTPs), commonly encountered in logistics and transportation systems when essential parameters—such as supply, demand, and transportation costs—are uncertain. Driven...

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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 27474 - 17
Main Authors: Joshi, Vishwas Deep, Sharma, Medha, Čepová, Lenka, Alsaud, Huda, Kalita, Kanak
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 28.07.2025
Nature Publishing Group
Nature Portfolio
Subjects:
639/166
639/705
Accuracy parameter
Approximation
Comparative analysis
Cost control
Decision making
Electronic commerce
Euclidean distance
Expected values
Exponential membership function
Fuzzy parameter based multi objective transportation problem
Fuzzy programming
Fuzzy sets
Humanities and Social Sciences
Linear programming
Logistics
Manufacturing
Methods
multidisciplinary
Objectives
Optimization techniques
Preferred compromise solution
Production planning
Science
Science (multidisciplinary)
Supply chains
Transportation planning
Exponential membership function
Fuzzy programming
Fuzzy parameter based multi objective transportation problem
Preferred compromise solution
Euclidean distance
Accuracy parameter
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:This paper introduces a novel two-step generalized parametric approach for addressing Fuzzy Multi-Objective Transportation Problems (FMOTPs), commonly encountered in logistics and transportation systems when essential parameters—such as supply, demand, and transportation costs—are uncertain. Driven by the necessity for resilient and flexible decision-making amidst uncertainty, the method employs Triangular Fuzzy Numbers (TFNs) and an accuracy parameter μ ∈ [0,1] to turn fuzzy data into precise equivalents through parametric transformation. Initially, imprecise input data are methodically converted into a sequence of Crisp Multi-Objective Transportation Problems (CMOTPs). In the subsequent phase, these CMOTPs are addressed by Fuzzy Linear Programming (FLP), and the most equitable solution at each μ-level is determined by its Euclidean distance from the fuzzy ideal solution. The suggested method is tested by numerical case studies and compared with current models—such as Nomani’s approach, fuzzy DEA, and Grey Relational Analysis (GRA)—showing enhanced performance in optimality proximity, solution stability, and ranking accuracy. This research has practical applications, including improved managerial capacity to manage uncertainty, reconcile trade-offs among cost, time, and service quality, and execute robust transportation strategies in fluctuating environments. The model’s scalability and openness make it suited for integration into enterprise logistics systems across industries such as manufacturing, retail, distribution, and e-commerce. The study offers a systematic and computationally efficient framework that enhances both theoretical comprehension and practical implementation of fuzzy optimization in multi-objective transportation planning.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-10949-4