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Introduction
Published in Joseph Y.-T. Leung, Handbook of SCHEDULING, 2004
Other research challenges may lie in widening the scope of the scheduling problem. The driver scheduling problem could be tackled simultaneously with its preceding or subsequent scheduling tasks thereby achieving more global optimization in the overall transport operation. Delays and service reliability are very important issues in public transport. Therefore, research in producing robust driver schedules and rescheduling methods to recover from delays would be useful.
Timetabling with flexible frequencies to synchronise groups of bus lines at common stops
Published in Transportmetrica A: Transport Science, 2021
Y. I. Silva-Soto, O. J. Ibarra-Rojas
Transit Network Design: Defines the layout of the transit lines and the separation between consecutive stops covered by each line in order to optimise specific objective functions such as operators' and users' costs.Frequency Setting Problem: Determines the number of trips per hour needed to satisfy the passenger demand in each planning period whilst considering operational costs.Transit Network Timetabling: Defines the arrival and departure times for all trips at all stops along with the transit network, commonly to increase the level of service.Vehicle Scheduling Problem: Determines the trip-vehicle assignments to cover all the planned trips in the timetable while ensuring operational costs (in terms of vehicle usage) are minimised.Driver Scheduling Problem: Defines the daily duties that cover all the scheduled trips minimising the drivers' wages and satisfying specific labour regulations.
Bus driver scheduling enhancement: a derandomizing approach for uncertain bus trip times
Published in Transportmetrica B: Transport Dynamics, 2020
Liujiang Kang, Qiang Meng, Chuanbei Zhou
Although some optimization models and solution algorithms have been proposed recently to solve the driver scheduling problems, because of the size and complexity of the problem, these approaches are only applicable for small and medium problem instances or are only limited to find locally optimal solutions. It should be pointed that the models developed by this study can be applied to real applications and solved optimally. Specifically, we solve two practical problems. One is the bus driver scheduling problem, which deploys bus drivers to cover timetables for bus routes under drivers’ contractual working rules. Another one is the enhanced bus driver scheduling problem, which redeploys bus drivers to ensure the quality of services in case of bus trip delays based on historical data. The second problem can handle uncertainty in the daily operation of urban bus transit systems, and it differs from the existing studies in problem settings as well as model formulations. The contributions of this study are as follows. First, an integer linear programming model for the driver scheduling problem is developed, which can be optimally solved using existing optimization packages, such as CPLEX and GAMS. Second, considering the true nature of the problem that bus trip times are uncertain and stochastic in practice, we extend the driver scheduling problem to an enhanced driver scheduling problem. Third, a derandomizing approach is proposed to address the uncertainty of trip times. Finally, to assess the efficiency and applicability of the models, we conduct a real case study for the Singapore bus route #95 (consisting of 105 trips), including a sensitivity analysis of driver group size and driver workload involved in the problems.