Optimization Techniques for Solving Multi-Objective Transportation Problem: A Comparative Analysis

Abstract

Efficient transportation planning requires balancing economic performance, operational efficiency, and environmental responsibility. In real-life transportation systems, transportation planner frequently need to include more than one conflicting factors like minimizing transportation cost, minimizing transportation time, and environmental impact. Traditional single-objective models are not sufficient to handle these complex requirements. Therefore, the need for efficient multi-objective optimization techniques has become increasingly significant. The proposed study focuses to solve a "Multi-Objective Transportation Problem (MOTP)” by different optimization techniques namely, Weighted Sum Method and Goal Programming Method. For each method, the mathematical models have been formulated and solved using secondary data in similar scenarios for fair comparison of methods. For each method, the solutions vary based on the prioritization of objectives. The results show that the Weighted Sum Method gives different results when the weights vary, which means it is sensitive to weight selection. Goal Programming proves to be more flexible and consistent by assigning prioritization and targets. A comparative analysis has been performed by focusing on the ability to find compromise solution, effectiveness of each method, efficiency in getting optimal results and practical applicability of the methods. As seen from the analysis, the multi-objective optimization offers more realistic solution as compared to single-objective optimization techniques. This could help decision makers in transportation and logistic planning.