New Approach to Obtain the Maximum Flow in a Network and Optimal Solution for the Transportation Problems

E. Ekanayake, W. B. Daundasekara, S. Perera
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Abstract

The maximum flow problem is also one of the highly regarded problems in the field of optimization theory in which the objective is to find a feasible flow through a flow network that obtains the maximum possible flow rate from source to sink. The literature demonstrates that different techniques have been developed in the past to handle the maximum amount of flow that the network can handle. The Ford-Fulkerson algorithm and Dinic's Algorithm are the two major algorithms for solving these types of problems. Also, the Max-Flow Min-Cut Theorem, the Scaling Algorithm, and the Push–relabel maximum flow algorithm are the most acceptable methods for finding the maximum flows in a flow network. In this novel approach, the paper develops an alternative method of finding the maximum flow between the source and target nodes of a network based on the "max-flow." Also, a new algorithmic approach to solving the transportation problem (minimizing the transportation cost) is based upon the new maximum flow algorithm. It is also to be noticed that this method requires a minimum number of iterations to achieve optimality. This study's algorithmic approach is less complicated than the well-known meta-heuristic algorithms in the literature. 
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网络中获取最大流量的新方法及交通问题的最优解
最大流量问题也是优化理论领域中备受关注的问题之一,其目标是通过流网络找到从源到汇获得最大可能流量的可行流。文献表明,过去已经开发了不同的技术来处理网络可以处理的最大流量。Ford-Fulkerson算法和Dinic算法是解决这类问题的两种主要算法。此外,最大流量最小割定理、缩放算法和推-重标签最大流量算法是在流量网络中寻找最大流量的最可接受的方法。在这种新颖的方法中,本文开发了一种基于“max-flow”的替代方法来寻找网络源节点和目标节点之间的最大流量。在此基础上,提出了一种求解运输问题(使运输成本最小化)的新算法。还需要注意的是,该方法需要最少的迭代次数来实现最优性。本研究的算法方法比文献中众所周知的元启发式算法不那么复杂。
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