A HYBRID ARTIFICIAL INTELLIGENCE AND MATHEMATIC OPTIMIZATION APPROACH FOR TRANSPORTATION OPTIMIZATION IN SUPPLY CHAIN MANAGEMENT
DOI:
https://doi.org/10.67851/ijcmth.vol.1.issue.2.014Keywords:
Artificial Intelligence, Transportation Optimization, Supply Chain Management, Mathematical Optimization, Demand Forecasting, Logistics, Transportation Cost.Abstract
Transport is one of the key elements of SCM that impacts logistics cost, time of delivery, customer satisfaction, and overall efficiency of operations. Classical approaches to modeling of transportation processes mostly concentrated on minimization of transportation cost under supply and demand restrictions. The problem of varying customer demand, cost of transportation, traffic flow, and uncertainty of delivery makes classical approaches of optimization less efficient. At the same time, Artificial Intelligence (AI) opens up new opportunities in transportation decision making through demand prediction, cost calculation, route choice, and intelligent optimization.
An AI-based mathematical approach is introduced for transportation optimization in Supply Chain Management in this paper. The classic transportation problem is built by defining decision variables, objective function, and supply-demand constraints. Artificial Intelligence is added to the formulation in order to make future demand predictions for better transportation decisions. A numerical case study considering 3 warehouses and 4 destinations is illustrated to explain the proposed approach. It is concluded that using the AI approach along with the mathematical modeling technique makes the transportation more efficient and unnecessary transportation is minimized.
