The static on‐demand bus routing problem: large neighborhood search for a dial‐a‐ride problem with bus station assignment

Abstract

We introduce a novel optimization problem to support the planning and routing of on-demand buses in an urban context. We call this problem the on-demand bus routing problem (ODBRP). Given are a fleet of buses with fixed capacity, a set of bus stations and travel times between them, and a set of transportation requests. Each transportation request consists of a set of potential departure and a set of potential arrival bus stations, as well as a time window, that is, an earliest departure time and a latest arrival time. The aim of the ODBRP is to (1) assign each passenger to a departure and arrival bus station and (2) develop a set of bus routes to fulfill each request in time while minimizing the total travel time of all users. We present the static version of the ODBRP, as well as a straightforward large neighborhood search heuristic to solve it. The performance of the heuristic is established by comparing it to an off-the-shelve heuristic solver (LocalSolver). We also use our heuristic to solve (slightly modified instances of) the well-known dial-a-ride problem. The results found by the heuristic for the on-demand bus system are compared to those of a simulated traditional public bus system with fixed lines and timetables. A thorough analysis of the comparison demonstrates that total user ride times can be significantly lower in an on-demand public bus system and shows that an on-demand bus system works best with a large number of small buses.

Patient admission scheduling problems with uncertain length of stay: optimization models and an efficient matheuristic approach

“…The degree of destruction can be gradually increased (Shaw, 1998) or chosen from a specific range dependent on the instance size (Ropke and Pisinger, 2006). Recent approaches on LNS based are the heuristics developed in Smet (2021) and Melis and Sörensen (2022) and the adaptive LNS hybridized with a kernel search for solving nurse routing problems in Gobbi et al. (2023).…”

A survey on demand-responsive public bus systems

“…However, a comparison concludes that a stop-based approach can save costs with a lower required fleet size (Viergutz and Schmidt, 2019;Bischoff et al, 2018). Only one paper is found on many-to-many fully flexible DON-PBS that includes bus stop assignment (Melis and Sörensen, 2021b), even though this has proven to yield better results in static many-to-many fully flexible DR-PBS (Melis and Sörensen, 2021a;Czioska et al, 2019).…”

“…Except for Bakas et al (2016), all contributions include a constraint on the vehicle capacity. The vehicle travel time is only considered in Stiglic et al (2018), the vehicle travel distance in Melis and Sörensen (2021a) and Guo et al (2019). Only Stiglic et al (2018) and Luo et al (2019) propose an exact algorithm to optimize the performance of the S-PBS.…”

Flexible vehicle scheduling with precedence constraints for tourists

“…The utilization ratio of vehicles in the charter mode (Rajendran and Srinivas, 2020), in which a vehicle serves only one group of tourists during its entire visiting duration, is usually not high. However, tourists may need to wait at some locations under the bus mode (Melis and Sorensen, 2022), in which vehicles travel along fixed routes and pick up and drop off tourists at certain stops, similar to the buses in cities.…”

“…The combination of different tourist groups on a route segment is limited by a given riding time of tourists, which influences both the utilization ratio of vehicles and the satisfaction degree of tourists. As a result, the flexible vehicle scheduling (FVS) for tourists is more complicated than the operational mode in similar existing problems, for example, the pickup and delivery (P&D) problem (Lucci et al., 2021), the dial‐a‐ride problem (Melis and Sorensen, 2022), or the routing problem (Archetti et al., 2021).…”