Soft Computing Approach for Three-Level Time Minimization Transportation Problem

Abstract

A crucial subset of transportation problems, known as time minimization transportation problems (TMTP), deals with the supply of products or services urgently or as soon as possible to the terminal points to meet the resource efficiency. The required origin-destination solution pairs have been identified using analytical or precise approaches. However, when the dimension grew, the problems of transport systems turned into NP Hard ones that required the use of alternate methods to solve. The Particle Swarm Optimization (PSO) algorithm, a meta-heuristic technique that has been used to solve a variety of practical optimization problems, making efficient use of resources, wasbrought to the forefront in this expedition (continuous solution space). However, PSO has been packaged with various encoding- decoding techniques to discretize the solution space. In this paper,authors proposed a modified PSO to solve three level TMTPwherein the path from origin to terminal points have been sub- divided into three levels as per need of the problem of transport enterprises and objectively total time is minimized. The proposedtechnique was found to be a superior alternative to existing techniques for making logistical decisions. The proposed PSO's exhaustive search prowess has been demonstrated with regard to alternative optimum solutions independent of the number and/orposition of allocations <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(m+n\ 1)$</tex> while traversing towards global optima. This illustrates how discrete combinatorial optimization problems might benefit from Particle Swarm Optimization.