Journal of applied mathematics & informatics

The Korean Society for Computational and Applied Mathematics (한국전산응용수학회)
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A STATISTICAL TECHNIQUE: NORMAL DISTRIBUTION AND INVERSE ROOT MEAN SQUARE FOR SOLVING TRANSPORTATION PROBLEM

  • M. AMREEN (Department of Mathematics,School of Advanced Sciences, Vellore Institute of Technology) ;
  • VENKATESWARLU B (Department of Mathematics, School of Advanced Sciences, Vellore Institute of Technology)
  • Received : 2024.03.04
  • Accepted : 2024.09.03
  • Published : 2024.09.30
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Abstract

This research aims to determine an optimal (best) solution for transporting the logistics at a minimum cost from various sources to various destinations. We proposed a new algorithm for the initial basic feasible solution (IBFS). Developing a new IBFS is the first step towards finding the optimal solution. A new approach for the initial basic feasible solution that reduces iterations and produces the best answer in the initial process of the transportation issue. Different IBFS approaches have been generated in the literature review. Some statistical fundamentals, such as normal distribution and the root mean square technique, are employed to find new IBFS. A TP is transformed into a normal distribution, and penalties are determined using the root mean square method. Excel Solver is used to calculate normal distribution values. The second step involves using a stepping-stone approach to compute the optimum solution. The results of our study were calculated using numerical examples and contrasted with a few other methodologies, such as Vogel's approximation, the Continuous Allocation Method (CAM), the Supply Demand Repair Method (SDRM), and the Karagul-Sahin Approximation Method (KSAM). The conclusion of our proposed method gives more accurate results than the existing approach.

Keywords

Acknowledgement

This work was supported by the Vellore Institute of Technology, Vellore, Tamil Nadu, India, 632014.

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