A Modified Ant Colony Optimization Algorithm for Solving a Transportation Problem

Abstract

Transportation of products from sources to destinations with minimal total cost plays a key role in logistics and supply chain management. The transportation problem (TP) is an extraordinary sort of Linear Programming problem where the objective is to minimize the total cost of disseminating resources from several various sources to several destinations. Initial feasible solution (IFS) acts as a foundation of an optimal cost solution technique to any TP. Better is the IFS lesser is the number of iterations to reach the final optimal solution. This paper presents a meta-heuristic algorithm, modified ant colony optimization algorithm (MACOA) to attain an IFS to a Transportation Problem. The proposed algorithm is straightforward, simple to execute, and gives us closeness optimal solutions in a finite number of iterations. The efficiency of this algorithm is likewise been advocated by solving validity and applicability examples An extensive numerical study is carried out to see the potential significance of our modified ant colony optimization algorithm (MACOA). The comparative assessment shows that both the MACOA and the existing JHM are efficient as compared to the studied approaches of this paper in terms of the quality of the solution. However, in practice, when researchers and practitioners deal with large-sized transportation problems, we urge them to use our proposed MACOA due to the time-consuming computation of JHM. Therefore this finding is important in saving time and resources for minimization of transportation costs and optimizing transportation processes which could help significantly to improve the organization’s position in the market.

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“…Since exact method requires expensive computational cost, metaheuristics that require cheaper computation cost [14] than the exact methods may be promising. There are metaheuristics that has been applied to solve TP, e.g., Genetic Algorithm (GA), Ant Colony Optimization (ACO) [15], Particle Swarm Optimization (PSO) [15], the hybrid algorithm of Particle Swarm Optimization and Genetic Algorithm (PSOGA). In [17], GA is used to solve Linear TP.…”

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