Multi-Criteria Analysis of Genetic Algorithm Applications in Transportation Logistics

This paper investigates the application of genetic algorithms (GAs) in the multi-criteria optimization of transportation networks, with a focus on their role in logistics management. Key considerations include fuel expenses, trip duration, environmental regulations, and vehicle maintenance. The paper provides a detailed examination of the objective function, which includes factors such as loading and unloading queues, traffic congestion, unexpected events, and other variables. Additionally, the paper presents a comparative analysis of various optimization methods, highlighting the benefits of GAs in reducing costs and improving delivery efficiency. The research delves into how genetic algorithms can be utilized for optimizing transportation systems by addressing cost control, delivery timelines, and environmental impact. The objective function discussed covers aspects such as fuel consumption, vehicle upkeep, waiting times, and other critical elements. The paper also offers a succinct comparative analysis of alternative optimization techniques, supported by graphical representations of the results. This comparison aims to showcase the effectiveness of genetic algorithms in solving complex multi-criteria problems and to emphasize their advantages over conventional methods.