This paper investigates the Heterogeneous Dial-A-Ride Problem (H-DARP) that consists of determining a vehicle route planning for heterogeneous users' transportation with a heterogeneous fleet of vehicles. A hybrid Genetic Algorithm (GA) is proposed to solve the problem. Efficient construction heuristics, crossover operators and local search techniques, specifically tailored to the characteristics of the H-DARP, are provided. The proposed algorithm is tested on 92 benchmarks instances and 40 newly introduced larger instances. Computational experiments show the effectiveness of our approach compared to the current state-of-the-art algorithms for the DARP and H-DARP. When tested on the existing instances, we achieved average gaps of only 0.47% to the bestknown solutions for the DARP, and 0.05% to the optimal solutions for the H-DARP, compared to 0.85% and 0.10%, respectively, obtained by the current state-of-the-art algorithms. For the 40 newly generated instances, average gaps of the hybrid GA are 0.35% smaller compared to the current stateof-the-art method. Besides, our method provides best results for 31 of these instances and ties with the existing method on 8 other instances.

The Heterogeneous Dial-a-Ride problem (HDARP) is an important problem in reduced mobility transportation. Recently, several extensions have been proposed towards more realistic applications of the problem. In this paper, a new variant called the Multi-Depot Multi-Trip Heterogeneous Dial-a-Ride Problem (MD-MT-HDARP) is considered. A mathematical programming formulation and three metaheuristics are proposed: an improved Adaptive Large Neighborhood Search (ALNS), Hybrid Bees Algorithm with Simulated Annealing (BA-SA), and Hybrid Bees Algorithm with Deterministic Annealing (BA-DA). Extensive experiments show the effectiveness of the proposed algorithms for solving the underlying problem. In addition, they are competitive to the current state-of-the-art algorithm on the MD-HDARP.

No abstract

No abstract

No abstract