A comparison of five heuristics for the multiple depot vehicle scheduling problem

Pepin, Ann-Sophie; Desaulniers, Guy; Hertz, Alain; Huisman, Dennis

doi:10.1007/s10951-008-0072-x

Cited by 115 publications

(106 citation statements)

References 29 publications

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“…Some exact algorithms, presented by Fischetti et al (1989), Forbes et al (1994) and Kliewer et al (2006), were able to solve instances with up to thousands of trips. Pepin et al (2009) presented and compared five representative heuristics: a tabu search, a large neighborhood search, a truncated column generation, a Lagrangian heuristic and a truncated branch-and-cut. The instances were composed of 1500 trips and up to 8 depots.…”

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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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%

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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“…Examples are the heuristic of Caprara et al (1999) for the set covering problem, and the large neighborhood search (LNS) heuristics of Ropke and Pisinger (2006) and Prescott-Gagnon et al (2007) for the vehicle routing problem with time windows and of Pepin et al (2008) for the multiple depot vehicle scheduling problem. The latter two papers have in common with this paper that they use heuristic column generation for neighborhood exploration.…”

Section: Mathematical Model and Solution Approach Overviewmentioning

confidence: 99%

Column Generation with Dynamic Duty Selection for Railway Crew Rescheduling

Potthoff

Huisman

Desaulniers

2010

Transportation Science

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The Dutch railway network experiences about three large disruptions per day on average. In this paper, we present an algorithm to reschedule the crews when such a disruption occurs. The algorithm is based on column generation techniques combined with Lagrangian heuristics. Since the number of duties is very large in practical instances, we first define a core problem of tractable size. If some tasks remain uncovered in the solution of the core problem, we perform a neighborhood exploration to improve the solution. Computational experiments with real-life instances show that our method is capable of producing good solutions within a couple of minutes of computation time.

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A comparison of five heuristics for the multiple depot vehicle scheduling problem

Cited by 115 publications

References 29 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

Column Generation with Dynamic Duty Selection for Railway Crew Rescheduling

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