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Αλγόριθμος βελτιστοποίησης σμήνους σωματιδίων για το πρόβλημα δρομολόγησης οχημάτων με ηλεκτρικά οχήματα

Gkatzolas Panagiotis

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Περίληψη

The transition to electric mobility is one of the biggest technological and environmental challenges in transportation today. However, the limited battery autonomy of electric vehicles (EVs) and the need for efficient route planning makes it essential to optimize their routes so that energy consumption is minimized and operational costs are reduced.In this study, we examine the Electric Vehicle Routing Problem (E-VRP), an extension of the classic Vehicle Routing Problem (VRP), which considers additional constraints such as energy consumption, charging station availability, and vehicle capacity. Our goal is to develop an algorithm that will compute optimal routes for a fleet of electric vehicles while minimizing both travel distance and energy consumption.To solve this problem, we use the Particle Swarm Optimization (PSO) algorithm, which is inspired by the collective movement of particle swarms in nature. PSO is a widely used optimization algorithm because it does not require overly complex mathematical calculations and can find high-quality solutions in a relatively short time. Moreover, it converges quickly and retains information from previous high-performing solutions, improving its search efficiency in routing problems. In our study, we adapt the PSO to consider key factors such as:• The total distance traveled by EVs.• Energy consumption, which depends on vehicle load and route characteristics.• Charging station availability and congestion levels.• Delivery time constraints.To evaluate our method, we will implement the PSO and we will test it on benchmark instances from the literature. We will assess the quality of the solutions and computational time, and the results will indicate if the PSO can provide efficient solutions for the E-VRP, optimizing routing while keeping the computational cost relatively low.

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