An overview on vehicle scheduling models

Bunte, Stefan; Kliewer, Natalia

doi:10.1007/s12469-010-0018-5

Cited by 215 publications

(102 citation statements)

References 40 publications

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“…Various papers give overviews of the models and solution approaches that are adopted (see Bodin and Golden (1981), Daduna and Paixão (1995), Bunte and Kliewer (2009) and Wren et al (2003)). Further considerations such as robustness and the integration of vehicle and crew scheduling are addressed by papers such as Békési et al (2009) and Mesquita et al (2009).…”

Section: Literature Reviewmentioning

confidence: 99%

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A GRASP with efficient neighborhood search for the integrated maintenance and bus scheduling problem

Hama

Yagiura

2018

JAMDSM

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The overall planning process undertaken by a bus company is traditionally composed of five sub-processes: timetabling, vehicle scheduling, maintenance scheduling, crew scheduling, and crew rostering. Solving the full optimization problem is believed to be computationally intractable, and therefore in practice the five sub-processes are usually optimized in sequence. In this paper, we present a model that integrates the problems of vehicle scheduling and maintenance scheduling. The objective is to minimize the differences in mileage between buses, the total distance traveled, and the daily differences in the number of maintenance tasks. We propose a heuristic algorithm using the framework of greedy randomized adaptive search procedure (GRASP), and we improve the neighborhood search procedure by using an ordered list of possible trips. We compare the neighborhood search procedure with and without this mechanism and show that the ordered list reduces the number of neighbors to be checked by more than 95%, and it reduces the time to obtain solutions of the same or better quality by an average of 70%. Through computational experiments on instances generated from real-world data, we show that the proposed algorithm finds solutions that are as good as or better than those obtained by a commercial solver in less than 5% of the time required by the latter.

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Section: Literature Reviewmentioning

confidence: 99%

“…The single depot vehicle scheduling problem (SDVSP) is known to be solvable in polynomial time (Bunte and Kliewer, 2009). The minimum fleet size for the single depot case was first solved by Saha (1970); however, the model does not consider the operational costs.…”

Section: Literature Reviewmentioning

confidence: 99%

A GRASP with efficient neighborhood search for the integrated maintenance and bus scheduling problem

Hama

Yagiura

2018

JAMDSM

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“…An overview of different vehicle scheduling models can be found in (Bunte and Kliewer, 2009). For heuristic solution approaches for the MDVSP we refer to Pepin et al (2006).…”

Section: Sequential Vehicle and Crew Schedulingmentioning

confidence: 99%

Computing strong lower and upper bounds for the integrated multiple-depot vehicle and crew scheduling problem with branch-and-price

Horváth

Kis

2017

Cent Eur J Oper Res

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In the problem of the title, vehicle and crew schedules are to be determined simultaneously in order to satisfy a given set of trips over time. The vehicles and the crew are assigned to depots, and a number of rules have to be observed in the course of constructing feasible schedules. The main contribution of the paper is a novel mathematical programming formulation which combines ideas from known models, and an exact solution procedure based on branch-and-price.The method is tested on benchmark instances from the literature and it provides suboptimal schedules using limited computational resources.Keywords vehicle and crew scheduling · branch-and-price · exact methods · integer programming

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“…Each selected vehicle starts and ends a trip in the depot with the travel time and stations fixed and previously defined. Different approaches to model the VSP have been developed, as well as solution methods and extensions for a better reality representation, e.g., the inclusion of multiple-depots and heterogeneous fleet [2]. The latter, however, has received little attention in the public transportation literature.…”

Section: Introductionmentioning

confidence: 99%

Towards Integrating Retiming in Vehicle Type Scheduling Problem

Sigua

Campoverde²,

Borenstein

2017

REP

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ResumenEn este documento, proponemos una programación lineal entera (ILP) con el objetivo de optimizar la generación de horarios y el problema de programación de tipo de vehículo (VTSP), basado en una red de tiempo y espacio (TSN). El modelo se definió como un problema de programación de tipo de vehículo con cambios secuenciales de horario (VTSP-SCT). Además, desarrollamos una nueva metodología para insertar una ventana de tiempo al problema propuesto en base a pequeños cambios en la estructura TSN, con una implementación computacional sencilla y una solución óptima en tiempos de ejecución de computación bajos. Al incluir pequeños cambios en el cronograma y/o incluir ventanas de tiempo para programar viajes, introdujimos niveles de flexibilidad en los horarios de salida de los viajes, lo que resulta en ventajas operacionales para el proveedor del servicio. Dado que usamos un intervalo de ventana de tiempo muy corto, el horario actual solo se modifica ligeramente, cambiando mínimamente las rutinas de los pasajeros. Los enfoques desarrollados fueron probados usando instancias aleatorias basadas en una ciudad brasileña. El VTSP-SCT con y sin ventanas de tiempo ha resultado en ahorros relevantes en las operaciones diarias del servicio de transporte público, reduciendo el número requerido de vehículos programados para llevar a cabo la demanda histórica. Palabras clave: programación de vehículos, logística, transporte público, investigación de operaciones, programación de horarios AbstractIn this paper, we propose an integer linear programming (ILP) aiming at optimizing timetabling generation and the Vehicle Type Scheduling Problem (VTSP), based on a time-space network (TSN). The model was defined as Vehicle Type Scheduling Problem with Sequential Changes of timetable (VTSP-SCT). Additionally, we developed a new methodology to insert time window to the proposed problem based on small changes on the TSN structure, with easy computational implementation and optimal solution at low computation run-times. By including small changes to the timetable and/or including time windows for timetabling trips, we introduced flexibility levels in the departure times of trips, resulting in operational advantages for the service provider. Since we use a very short time window interval, the current timetable is only slightly modified, minimally changing the passenger routines. The developed approaches were tested using random instances based on a Brazilian city. The VTSP-SCT with and without time windows have resulted in relevant savings in the daily operations of the public transportation service, reducing the required number of scheduled vehicles to carry out the historic demand.

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An overview on vehicle scheduling models

Cited by 215 publications

References 40 publications

A GRASP with efficient neighborhood search for the integrated maintenance and bus scheduling problem

A GRASP with efficient neighborhood search for the integrated maintenance and bus scheduling problem

Computing strong lower and upper bounds for the integrated multiple-depot vehicle and crew scheduling problem with branch-and-price

Towards Integrating Retiming in Vehicle Type Scheduling Problem

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