An elitist multi-objective genetic algorithm for minimizing vehicle numbers and energy consumption in the context of Personal Rapid Transit

Abstract

Personal Rapid Transit (PRT) is a taxi-like transportation system in which automated driverless vehicles transport people directly from one station to another without any intermediate stops. In this work, we focus on a static problem related to PRT where we aim to minimize the energy consumption and number of PRT vehicles. This paper proposes and analyzes a multi-objective genetic algorithm that assigns rank and fitness values to solve the PRT problem. This algorithm is applied to a set of randomly generated test instances, and produces very promising solutions.