Classification in vehicle routing and scheduling

Bodin, Lawrence; Golden, Bruce L.

doi:10.1002/net.3230110204

Cited by 280 publications

(92 citation statements)

References 32 publications

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“…However, the mathematical programming problems arising from scheduling applications are often difficult to solve and workable schedules must be obtained from heuristics [4,71J. The 31gorithm presented in this paper is also heuristic.…”

Section: Discussionmentioning

confidence: 99%

Aircraft and Crew Scheduling during Airlift Operations

Armstrong¹,

Charnes²,

Samn³

1981

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Section: Discussionmentioning

confidence: 99%

Aircraft and Crew Scheduling during Airlift Operations

Armstrong¹,

Charnes²,

Samn³

1981

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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%

“…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). Various practical extensions are also considered, such as multiple vehicles types (see Hassold and Ceder (2014)), which is NP-hard even for the single-depot case (see Lenstra and Rinnooy Kan (1981)), variable arrival and departure times (which is NP-hard even for one vehicle and one depot; see Savelsbergh (1985)), and route constraints (see Bodin and Golden (1981)). …”

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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“…Overviews of algorithms and applications for the singledepot VSP (SDVSP) and some of its extensions can be found in [1,5,10,12,18]. The SDVSP has been formulated as a linear assignment problem, a transportation problem, a minimum-cost flow problem, a quasi-assignment problem, and a matching problem in the literature.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, in our experiments, the backup trip candidates are generated based on distances and travel times. In order to simplify the experiments and to better compare the algorithms, we assumed that K, the number of backup trips, is such that K ∈ {2, 3,5,10,15,20,25,30,35, 40} for test cases with more than 300 remaining trips. For test cases with 100 and 300 remaining trips, K ∈ {2, 3, 5, 10} and K ∈ {2, 3, 5, 10, 15, 20}, respectively, since smaller-size problems have fewer backup vehicles.…”

Section: Experiments Configurationmentioning

confidence: 99%

The vehicle rescheduling problem: Model and algorithms

Mirchandani

Borenstein

2007

Networks

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When a vehicle on a scheduled trip breaks down, one or more vehicles need to be rescheduled to serve the customers on that trip with minimum operating and delay costs. The problem of reassigning vehicles in real-time to this cut trip as well as to other scheduled trips with given starting and ending times, is referred to as the vehicle rescheduling problem (VRSP). This paper considers modeling, algorithmic, and computational aspects of the single-depot VRSP. The paper formulates a model for this problem and develops several fast algorithms to solve it, including parallel synchronous auction algorithms. The concept of the common feasible network (CFN) is introduced to find a good set of initial "prices" for speeding up the auction algorithm. Computational experiments on randomly generated problems are described. Computational results show that, for small problems, all of the developed algorithms demonstrate very good computational performances. For large problems, parallel CFN-based auction algorithms provide the optimal solution with much smaller computation times.

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Classification in vehicle routing and scheduling

Cited by 280 publications

References 32 publications

Aircraft and Crew Scheduling during Airlift Operations

Aircraft and Crew Scheduling during Airlift Operations

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

The vehicle rescheduling problem: Model and algorithms

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