An Integrated Approach to Ex-Urban Crew and Vehicle Scheduling

Nonato, Maddalena

doi:10.1007/978-3-642-85970-0_6

Cited by 27 publications

(22 citation statements)

References 29 publications

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“…Carraresi et al (1995) proposed a column generation approach that starts with a feasible set of runs and iteratively replaces some runs to get better solution, and the solution uses a Lagrangean relaxation method. Gaffi and Nonato (1997) proposed an integrated approach for ex-urban vehicle and crew scheduling. They use the column generation method to solve the set covering or set partitioning continuous relaxation embedded in branch and bound framework to get an integer solution.…”

Section: Matching Algorithmsmentioning

confidence: 99%

Algorithm for Crew-Scheduling Problem with Bin-Packing Features

Qiao

Hamedi

Haghani

2010

Transportation Research Record

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This thesis proposes a new approach for solving the traditional crew scheduling problem. The crew scheduling problem is solved with a bin packing approach in polynomial time. Based on the extensive research on the bin packing problem during the past 40 years, an algorithm that is proved to be the most efficient for solving most bin packing problems is selected and modified for application in the crew scheduling problem. A Modified Best-Fit-Decreasing Algorithm is proposed and discussed in this study. A case study is conducted using the proposed algorithm and the results are discussed.

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Section: Matching Algorithmsmentioning

confidence: 99%

Algorithm for Crew-Scheduling Problem with Bin-Packing Features

Qiao

Hamedi

Haghani

2010

Transportation Research Record

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“…Exact optimization methods, based on branch-and-bound, branch-and-cut, column generation, or lagrangian relaxation are presented by Friberg and Haase (1999), Gaffi and Nonato (1999) Approaches based on heuristics and metaheuristics are presented in Ball et al (1983), Falkner andRyan (1992), Patrikalakis and Xerocostas (1992), Wren and Gualda (1999), Valouxis and Housos (2002), Rodrigues et al (2006), Laurent and Hao (2007), Steinzen et al (2007 and Bartodziej et al (2009). However, the reported heuristic techniques are not easy to generalize and most of them have been developed for very specific problems, with results reported for a small number of instances.…”

Section: Introductionmentioning

confidence: 99%

“…Exact optimization methods, based on branch-and-bound, branch-and-cut, column generation, or lagrangian relaxation are presented by Friberg and Haase (1999), Gaffi and Nonato (1999), Haase et al (2001), Freling et al (1999), Freling et al (2001), Fischetti et al (2001), Freling et al (2003), Huisman et al (2005), Groot and Huisman (2008) and Paias (2008, 2009).…”

Section: Introductionmentioning

confidence: 99%

A multiobjective metaheuristic approach for the integrated vehicle and crew scheduling

Prata

2016

J. Transp. Lit.

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Article Info AbstractKeywords: mass transit combinatorial optimization evolutionary algorithmsThe integrated vehicle and crew scheduling problem is a hard Combinatorial Optimization problem widely studied over the years. Taking into consideration the range of variables related to the planning process of vehicles and drivers, there are several practical characteristics of the problem that are not reflected in the solutions generated computationally. Among these characteristics, that were not found in the consulted literature, the most important is the existence of multiple objectives. This paper aims at presenting a multiobjective approach for the integrated vehicle and crew scheduling problem based on Genetic Algorithms. A case study in Portimão (Portugal) is presented and discussed. IntroductionIn what concerns buses, urban transportation planning can be divided in several steps, often considered as follows: network design, timetable creation, vehicle scheduling, crew scheduling and crew rostering. Vehicle scheduling and crew scheduling have been a subject of numerous research activities, due to the significant gains that may result from their optimization (Daduna and Paixão, 1995; Wren and Rousseau, 1999).Several researchers have reported the strong interaction between the vehicle scheduling problem and the crew scheduling problem, emphasizing the gains that can be obtained by considering them together (Freling et al., 2003). The integrated problem is usually known as the Vehicle and Crew Scheduling Problem (VCSP).Traditionally, vehicle scheduling and crew scheduling are done separately and in a sequential manner. The Vehicle Scheduling Problem (VSP) is performed in a first stage, being followed by the Crew Scheduling Problem (CSP). Usually, the CSP is more complex than the VSP, and therefore, in general, it is not a suitable strategy to schedule the vehicles without taking into account the requirements for the crews, since these may form the bottleneck of the whole process.In many public transport companies, particularly in developing countries, the costs of crews and drivers are the most significant. Thus, efforts should be focused in minimizing such costs, even when vehicle scheduling is being performed.For the resolution of the VCSP, several approaches have been reported in the literature. Exact optimization methods, based on branch-and-bound, branch-and-cut, column generation, or lagrangian relaxation are presented by Friberg and Haase (1999), Gaffi and Nonato (1999) Approaches based on heuristics and metaheuristics are presented in Ball et al. (1983), Falkner andRyan (1992), Patrikalakis and Xerocostas (1992), Wren and Gualda (1999), Valouxis and Housos (2002), Rodrigues et al. (2006), Laurent and Hao (2007), Steinzen et al. (2007 and Bartodziej et al. (2009). However, the reported heuristic techniques are not easy to generalize and most of them have been developed for very specific problems, with results reported for a small number of instances.Taking into consideration the range of variables related t...

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“…This integrated management of both resources demonstrated its efficiency in [6] where relief locations, i.e. places where a driver can be relieved by a colleague, are spatially distant.…”

Section: Introductionmentioning

confidence: 99%

“…Gaffi and Nonato in [6] were the first to address the integrated VCSP in the multiple-depot case. Like Freling and coauthors, they make use of Lagrangian relaxation and column generation.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Vehicle and Crew Scheduling for Extra Urban Transports

Laurent

Hao

New Frontiers in Applied Artificial Intelligence

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Abstract. We present a simultaneous approach to solve the integrated vehicle and crew scheduling problem in an extra urban context. We consider the single depot case with a heterogeneous fleet of vehicles. We propose a constraint based model which is subsequently solved by a Greedy Randomized Adaptive Search Procedure. The construction phase of each initial solution relies on constraint programming techniques, while the local search phase exploits a powerful neighborhood exploration mechanism. The computational experiments conducted on real-world instances show the effectiveness and the flexibility of the approach compared with the classical sequential vehicle and crew scheduling.

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An Integrated Approach to Ex-Urban Crew and Vehicle Scheduling

Cited by 27 publications

References 29 publications

Algorithm for Crew-Scheduling Problem with Bin-Packing Features

Algorithm for Crew-Scheduling Problem with Bin-Packing Features

A multiobjective metaheuristic approach for the integrated vehicle and crew scheduling

Simultaneous Vehicle and Crew Scheduling for Extra Urban Transports

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