A Biased Random Key Genetic Algorithm for the Dial-a-Ride Problem

The Dial-a-Ride Problem (DARP) is a classic vehicle routing problem raised in the field of shared mobility systems. Till now, several algorithms have been proposed to tackle the DARP; however, the performance of population-based techniques in solving this problem has not been extensively studied. With this regard, the Biased Random Key Genetic Algorithm (BRKGA) is a matter of interest. In this paper, a brief overview of the BRKGAs proposed for solving the vehicle routing problems is provided. A new method based on the BRKGA in combination with a Variable Neighborhood Descent (VND) procedure is also proposed to tackle the DARP. In contrast with the preceding similar study on the DARPs, in our algorithm, mating operators are directly involved in the process of constructing a DARP solution. Besides, our algorithm takes advantage of a problem-specific mutation operator and two crossover methods. Considering standard benchmark instances of the DARP, experimental results are totally promising and indicate that this study can be considered as a starting point for further investigation about this view in future.