New Vogel’s Approximation Method (NVAM) to Determine Better Feasible Solution of Transportation Problem

Q4 Mathematics
Amulu Priya S., Maheswari V., V. Balaji
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引用次数: 0

Abstract

Nowadays, getting substantial results for problems in operations research is crucial. The transportation problem can be solved very effectively with Vogel's Approximation Method (VAM) and discover a workable solution that is closer to the ideal solution. The basic principle of VAM is to allocate as many resources as possible to the cell with the smallest cost in the column or row with the greatest penalty. This cell will receive the maximum number of resources. A problem arises when the magnitudes of the least cost and the next-least cost are identical. Then, we devised a new method called the "New Vogel's Approximation Method (NVAM)" to provide a workable solution to the transportation problem that may then be used to determine the optimal solution. Transporting capacity to the required location is done by three distinct companies. The supply chain's participants seek to move their goods from warehouse to distribution centre, from plant to warehouse, and from distribution centre to retail store. In this study, the VAM method and the NVAM approach are used to determine the optimal transportation cost. These techniques are examined to provide potential supply chain management techniques.
求解交通问题最佳可行解的新Vogel近似法
如今,对运筹学中的问题取得实质性成果至关重要。Vogel近似方法(VAM)可以非常有效地解决运输问题,并找到一个更接近理想解的可行解。VAM的基本原理是将尽可能多的资源分配给代价最大的列或行中成本最小的单元。此单元格将接收最大数量的资源。当最小成本和次最小成本的大小相同时,就会出现问题。然后,我们设计了一种新的方法,称为“新沃格尔近似方法(NVAM)”,为运输问题提供了一个可行的解决方案,然后可以用来确定最佳解决方案。将运力运送到所需地点由三家不同的公司完成。供应链的参与者寻求将他们的商品从仓库转移到配送中心,从工厂转移到仓库,从配送中心转移到零售店。在本研究中,使用VAM方法和NVAM方法来确定最优运输成本。对这些技术进行了研究,以提供潜在的供应链管理技术。
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来源期刊
Philippine Statistician
Philippine Statistician Mathematics-Statistics and Probability
CiteScore
0.50
自引率
0.00%
发文量
92
期刊介绍: The Journal aims to provide a media for the dissemination of research by statisticians and researchers using statistical method in resolving their research problems. While a broad spectrum of topics will be entertained, those with original contribution to the statistical science or those that illustrates novel applications of statistics in solving real-life problems will be prioritized. The scope includes, but is not limited to the following topics:  Official Statistics  Computational Statistics  Simulation Studies  Mathematical Statistics  Survey Sampling  Statistics Education  Time Series Analysis  Biostatistics  Nonparametric Methods  Experimental Designs and Analysis  Econometric Theory and Applications  Other Applications
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