Hybrid adaptive large neighborhood search algorithm for the mixed fleet heterogeneous dial-a-ride problem

Abstract

The mixed fleet heterogeneous dial-a-ride problem (MF-HDARP) consists of designing vehicle routes for a set of users by using a mixed fleet including both heterogeneous conventional and alternative fuel vehicles. In addition, a vehicle is allowed to refuel from a fuel station to eliminate the risk of running out of fuel during its service. We propose an efficient hybrid Adaptive Large Neighborhood Search (hybrid ALNS) algorithm for the MF-HDARP. The computational experiments show that the algorithm produces high quality solutions on our generated instances and on HDARP benchmarks instances. Computational experiments also highlight that the newest components added to the standard ALNS algorithm enhance intensification and diversification during the search process. Keywords Dial-a-ride problem. Alternative fuel station. Adaptive Large Neighborhood Search algorithm. Mixed vehicle fleet

US Army Aviation air movement operations assignment, utilization and routing

“…The model in this paper considers a more realistic setting encountered by Army aviation helicopters, in which there is a limited number of HLZs with refueling capabilities. Multiple vehicle routing problems (MVRP) of unmanned aerial vehicles (UAVs) (Sundar and Rathinam, 2012;Sundar et al, 2016), service network design optimization for helicopters (Mogensen, 2014) and DARP with electric vehicles (EVs) (Masmoudi et al, 2020;Tekil-Erg€ un et al, 2021) also share the feature of having a set of refueling nodes. Zeng et al (2022) provide an additional unique approach to refueling electric UAVs via a nested vehicle routing problem.…”

“…An alternative approach is to minimize unsupported demand by priority level as seen in Yakıcı et al (2018) in their effort to route rotary wing assets in amphibious-ready groups to transport personnel and cargo. Solution methods for the dial-a-ride-problem vary from tabu search heuristics proposed by Cordeau and Laporte (2003) and improved by Ho et al (2018b) to large neighborhood search algorithms described in Masmoudi et al (2020) and Jain and Van Hentenryck (2011). Tripathy et al (2017) use an ant colony approach, while Menezes et al (2010) and de Alvarenga Rosa et al (2016) employ a GRASP heuristic.…”

“…This mathematical model follows from the dial-a-ride problem work by Cordeau and Laporte (2007) and capacitated helicopter routing problem considered by de Alvarenga Rosa et al (2016). The refueling constraints follow work done in Sundar and Rathinam (2012), Masmoudi et al (2020) andTekil-Erg€ un et al (2021). The capacitated helicopter routing problem (CHRP) can be represented by a complete graph G(V; A) defined by a set of nodes, V 5 {P, D, E}, and edges, A. P is the set of AMR pickup nodes, P 5 {1, 2, .…”

Application of Generative Adversarial Network to Optimize Vehicle Allocation at Dispatch Stations of Paratransit Services

“…Its search speed is faster than small neighborhood searches because it has a greater degree of local improvement at each iteration. Since Ropke and Pisinger [50] first applied this method to Dial-a-Ride problems, many scholars have explored this approach, including Gschwind and Drexl [25], Braekers and Kovacs [51], Hungerländer et al [52], Masmoudi et al [53], Montenegro et al [54], Ma et al [55], and Truden et al [56].…”

Bi-Objective Optimization for Vehicle Routing Problems with a Mixed Fleet of Conventional and Electric Vehicles and Soft Time Windows

“…Goeke and Schneider [11], Masmoudi et al [12], and Li et al [13] considered the recharge plans of EVs in the same way when studying the MFVRP, with the constraints that EVs must be fully charged at the charging station. Among them, Li et al [13] conducted sensitivity analysis on the fixed cost, variable cost, and travelling range of EVs and indicated that high fixed costs and low variable costs make EVs need long-distance distribution, which is restricted by the limited travelling range.…”