Optimization of Transportation Cost Using an Innovative Technique

Abstract

Objectives: To generate an optimal or near-optimal solution while minimizing the optimality gap, thereby ensuring that the final total cost remains as close as possible to the theoretical lower bound. Method: This study proposes a novel Total Opportunity Cost Matrix–Standard Deviation (TOCM-SD) algorithm for generating a superior Initial Basic Feasible Solution (IBFS). The proposed method uniquely integrates opportunity cost analysis with standard deviation metrics to guide initial allocation decisions, thereby enhancing solution quality from the initial phase. The algorithm was rigorously evaluated on a diverse set of 30 balanced and unbalanced TP instances sourced from established literature. Findings: Comparative performance evaluation reveals that TOCM-SD secures the optimal solution in 18 of the 30 cases, translating to a 60% success rate that outperforms existing heuristic approaches. Additionally, the method severely reduces the number of iterations required to reach the final optimal solution and consistently produces lower total transportation costs. Novelty: The effective integration of the Total Opportunity Cost Matrix with Standard Deviation, which improves decision-making robustness during the initial phase. Enhanced refinement and accuracy metrics confirm the computational efficiency and reliability of TOCM-SD, establishing it as a highly competitive and practical alternative for transportation cost optimization. Keywords: Total Opportunity Cost Matrix, Standard Deviation, Initial Basic Feasible Solution, Optimal solution, Transportation cost