Heuristic approach for the multiobjective optimization of the customized bus scheduling problem

Abstract

Customized bus is a typical demand‐responsive transit (DRT) service that allows passengers to customize their bus route on demand and be picked up and dropped off at their desired location. A set of ride requests sent at various times by geographically dispersed passengers are operated in real time by the controller and then assigned to the shared bus with the assurance of maintaining the minimum vehicle and driver costs, which introduces a new challenge to scheduling. In this paper, a mixed integer linear programming (MILP) model is presented to solve the customized bus scheduling problem. To quickly generate satisfactory scheduling solutions in the case of single‐ and multi‐depots, a heuristic multi‐objective optimization algorithm based on preemptive scheduling is proposed, which can converge to the optimal solution among a set of vehicle blocks (consecutive trips by one bus). A real‐world customized bus scheduling problem from the public transport company of Chengdu, China that contains 2,000‐trip instances operated on a day is used to demonstrate that the proposed approach reduces total operating costs by 30.51% and 30.71%, compared with the simulated annealing algorithm and genetic algorithm, respectively.

Trends in passenger transport optimisation

“…Most of the optimisation problems reviewed in this section are tackled by solving a vehicle routing problem at every optimisation period (Tong et al., 2017; Guo et al., 2019, 2020; Huang et al., 2020c; Sun et al., 2020a; Zhang et al., 2021; Gong et al., 2021; Liu et al., 2022; Li et al., 2022a), often by also incorporating time windows associated with the passengers or by allowing vehicles to carry out multiple trips. Optimising real‐world instances of these problems, where data are mostly collected from actual passengers, can prove to be very challenging due to the massive size of the instance that has to be solved.…”

“…As pointed out by Liu et al. (2022), the problem of scheduling the operations and the drivers, for DRT online buses is more complicated than scheduling conventional bus service, since optimisation of travel times when passengers are already on board must be carried out with care as it may significantly impact the profitability of the service, as repositioning and idle times are expensive operations.…”

“…Having a mixed fleet of vehicles with multiple capacities could prove to be a suitable choice in order to improve the route flexibility, serve a greater portion of the demand and reduce travel times for passengers. As pointed out by Liu et al (2022), the problem of scheduling the operations and the drivers, for DRT online buses is more complicated than scheduling conventional bus service, since optimisation of travel times when passengers are already on board must be carried out with care as it may significantly impact the profitability of the service, as repositioning and idle times are expensive operations.…”

Towards a more flexible demand responsive transit service with compensation mechanism considering boundedly rational passengers

“…Urban development is facing enormous challenges against the backdrop of population growth, urban sprawl, environmental concerns, and austerity policies [1,2]. Door-to-door demand responsive transit (DRT) has emerged as a promising solution for sustainable mobility to meet travellers' diverse and individualized demand [3,4]. Motivated by breakthroughs in mobile internet technologies, a more flexible DRT service with appbased reservation platforms has been emergence to drive the reinvention of transit services as a whole [5].As shown in Table 1 [6,7], in contrast to conventional bus and customized bus (CB), dynamic DRT can moderately adjust routes and schedules to respond to real-time travel demands [8,9].…”

“…Previous studies associated with DRT mainly focused on vehicle scheduling design [4,[10][11][12][13][14], passenger demand forecast [1,6], algorithm optimization [4,[15][16][17], and market adaptability [2,18]. Researchers interested in DRT continuously explored various methods to optimize and design DRT networks as a means to stimulate the progressive development of service system.…”