To Use an Ant Colony Technique to Solve a Crispy Type Bi- and Tri-Objective Transportation Problem
Keywords:multi-objective, transportation problem, optimization, efficient solution, ant colony optimization algorithm
Transportation problem (TP) aims to reduce the entire transportation cost of moving resources from various supply hubs to various demand hubs. However, in real-life situations, all organizations want to achieve numerous objectives while making transportation of goods. The degree of deterioration may vary depending on the mode, route, and time of transport. In some cases, the multiobjective could be used to reduce the use of a scarce resource, such as energy. As a result, the proposed approach was discovered to be an algorithm that improves bi- and triobjective TP techniques. This is an innovative way for solving the new bi- and triobjective transportation algorithm using a modified ant colony optimization (ACO) algorithm. According to the literature, various strategies have been developed in the past to tackle the multiobjective transportation problem (MOTP). The MOTP is solved using goal programming, fuzzy programming, interactive solution algorithms, and other techniques. These strategies are occasionally good or bad in achieving better results in a reasonable amount of time. The heuristic technique used in this work is the improved ACO algorithm, which is based on the ant colony algorithm and has been found to provide solutions with a reasonable degree of satisfaction for two and three objective TPs. When the findings are compared, the solution achieved using the proposed method has delivered the best performance and provides a case study to show the new strategy.
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