Creation of a Mathematical Framework for Addressing the Multi-Objective Multi-Item Transportation Challenge Through Linear Programming

Abstract

 

Supply chain is a complex area of research because it highlights the non-targeted movement of many commodities across multiple origins and destinations, given the multi-working goal and multi-thing transportation issue, which has vastness in terms of competing objectives. We suggest a systematic mathematical approach using linear programming to address this complex optimization problem. This paper develops a multi-objective linear programming (MOLP) model to optimally minimize cost, time of delivery, and environmental impacts as the first objectives through various published observations. Constraints imposed on the model comprise supply availability, demand allocation requirements, vehicle capacity, and product allocation regulations if multiple products are available. A real practice case study is done using actual operational data in a regional distribution network, optimizing its transportation planning solution approach with WinQSB software to revise the best routes. Results suggest that the balance of conflicting objectives is effectively achieved by the proposed model, leading to a total cost reduction of 18.5%, delivery time reduction of12.3%, as well as lower CO2 emissions by approximately 15.2%.The research applies the weighted sum-constraint method for Pareto optimization to facilitate decision-making trade-offs, resulting in a comprehensive analysis of trade-offs and potential transportation planning solutions for decision-makers.