2,523 research outputs found

    A solution approach for dynamic vehicle and crew scheduling

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    In this paper, we discuss the dynamic vehicle and crew schedulingproblem and we propose a solution approach consisting of solving asequence of optimization problems. Furthermore, we explain why itis useful to consider such a dynamic approach and compare it witha static one. Moreover, we perform a sensitivity analysis on ourmain assumption that the travel times of the trips are knownexactly a certain amount of time before actual operation.We provide extensive computational results on some real-world datainstances of a large public transport company in the Netherlands.Due to the complexity of the vehicle and crew scheduling problem,we solve only small and medium-sized instances with such a dynamicapproach. We show that the results are good in the case of asingle depot. However, in the multiple-depot case, the dynamicapproach does not perform so well. We investigate why this is thecase and conclude that the fact that the instance has to be splitin several smaller ones, has a negative effect on the performance.transportation;vehicle and crew scheduling;large-scale optimization;dynamic planning

    Vehicle and crew scheduling: solving large real-world instances with an integrated approach

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    In this paper we discuss several methods to solve large real-worldinstances of the vehicle and crew scheduling problem. Although,there has been an increased attention to integrated approaches forsolving such problems in the literature, currently only small ormedium-sized instances can be solved by such approaches.Therefore, large instances should be split into several smallerones, which can be solved by an integrated approach, or thesequential approach, i.e. first vehicle scheduling and afterwardscrew scheduling, is applied.In this paper we compare both approaches, where we considerdifferent ways of splitting an instance varying from very simplerules to more sophisticated ones. Those ways are extensivelytested by computational experiments on real-world data provided bythe largest Dutch bus company.

    Comparison of heuristic approaches for the multiple depot vehicle scheduling problem

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    Given a set of timetabled tasks, the multi-depot vehicle scheduling problemis a well-known problem that consists of determining least-cost schedulesfor vehicles assigned to several depots such that each task is accomplishedexactly once by a vehicle. In this paper, we propose to compare theperformance of five different heuristic approaches for this problem,namely, a heuristic \\mip solver, a Lagrangian heuristic, a columngeneration heuristic, a large neighborhood search heuristic using columngeneration for neighborhood evaluation, and a tabu search heuristic. Thefirst three methods are adaptations of existing methods, while the last twoare novel approaches for this problem. Computational results on randomlygenerated instances show that the column generation heuristic performs thebest when enough computational time is available and stability is required,while the large neighborhood search method is the best alternative whenlooking for a compromise between computational time and solution quality.tabu search;column generation;vehicle scheduling;heuristics;Lagrangian heuristic;large neighborhood search;multiple depot

    The crew-scheduling module in the GIST system

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    The public transportation is gaining importance every year basically due the population growth, environmental policies and, route and street congestion. Too able an efficient management of all the resources related to public transportation, several techniques from different areas are being applied and several projects in Transportation Planning Systems, in different countries, are being developed. In this work, we present the GIST Planning Transportation Systems, a Portuguese project involving two universities and six public transportation companies. We describe in detail one of the most relevant modules of this project, the crew-scheduling module. The crew-scheduling module is based on the application of meta-heuristics, in particular GRASP, tabu search and genetic algorithm to solve the bus-driver-scheduling problem. The metaheuristics have been successfully incorporated in the GIST Planning Transportation Systems and are actually used by several companies.Integrated transportation systems, crew scheduling, metaheuristics

    Essays on urban bus transport optimization

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    Nesta tese, nós apresentamos uma compilação de três artigos de otimização aplicados no contexto de transporte urbano de ônibus. O principal objetivo foi estudar e implementar heurísticas com base em Pesquisa Operacional para otimizar problemas de (re)escalonamento de veículos off-line e on-line considerando várias garagens e frota heterogênea. No primeiro artigo, foi proposta uma abordagem heurística para o problema de escalonamento de veículos múltiplas garagens. Acreditamos que as principais contribuições são o método de geração de colunas para grandes instâncias e as técnicas de redução do espaço de estados para acelerar as soluções. No segundo artigo, adicionamos complexidade ao considerar a frota heterogênea, denotada como multiple depot vehicle type scheduling problem (MDVTSP). Embora a importância e a aplicabilidade do MDVTSP, formulações matemáticas e métodos de solução para isso ainda sejam relativamente inexplorados. A principal contribuição desse trabalho foi o método de geração de colunas para o problema com frota heterogênea, já que nenhuma outra proposta na literatura foi identificada no momento pelos autores. Na terceira parte desta tese, no entanto, nos concentramos no reescalonamento em tempo real para o caso de quebras definitivas de veículos. A principal contribuição é a abordagem eficiente do reescalonamento sob uma quebra. A abordagem com redução de espaço de estados, solução inicial e método de geração de colunas possibilitou uma ação realmente em tempo real. Em menos de cinco minutos, reescalonando todas as viagens restantes.In this dissetation we presented a three articles compilation in urban bus transportation optimization. The main objective was to study and implement heuristic solutions method based on Operations Research to optimizing offline and online vehicle (re)scheduling problems considering multiple depots and heterogeneous fleet. In the first paper, a fast heuristic approach to deal with the multiple depot vehicle scheduling problem was proposed. We think the main contributions are the column generation framework for large instances and the state-space reduction techniques for accelerating the solutions. In the second paper, we added complexity when considering the heterogeneous fleet, denoted as "the multiple-depot vehicle-type scheduling problem" (MDVTSP). Although the MDVTSP importance and applicability, mathematical formulations and solution methods for it are still relatively unexplored. We think the main contribution is the column generation framework for instances with heterogeneous fleet since no other proposal in the literature has been identified at moment by the authors. In the third part of this dissertation, however, we focused on the real-time schedule recovery for the case of serious vehicle failures. Such vehicle breakdowns require that the remaining passengers from the disabled vehicle, and those expected to become part of the trip, to be picked up. In addition, since the disabled vehicle may have future trips assigned to it, the given schedule may be deteriorated to the extent where the fleet plan may need to be adjusted in real-time depending on the current state of what is certainly a dynamic system. Usually, without the help of a rescheduling algorithm, the dispatcher either cancels the trips that are initially scheduled to be implemented by the disabled vehicle (when there are upcoming future trips planned that could soon serve the expected demand for the canceled trips), or simply dispatches an available vehicle from a depot. In both cases, there may be considerable delays introduced. This manual approach may result in a poor solution. The implementation of new technologies (e.g., automatic vehicle locators, the global positioning system, geographical information systems, and wireless communication) in public transit systems makes it possible to implement real-time vehicle rescheduling algorithms at low cost. The main contribution is the efficient approach to rescheduling under a disruption. The approach with integrated state-space reduction, initial solution, and column generation framework enable a really real-time action. In less than five minutes rescheduling all trips remaining

    Application oriented variable fixing methods for the multiple depot vehicle scheduling problem

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    In this article, we present heuristic methods for the vehicle scheduling problem that solve it by reducing the problem size using different variable fixing approaches. These methods are constructed in a way that takes some basic driver requirements into consideration as well. We show the efficiency of the methods on real-life and random data instances too. We also give an improved way of generating random input for the vehicle scheduling problem

    Essays on urban bus transport optimization

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    Nesta tese, nós apresentamos uma compilação de três artigos de otimização aplicados no contexto de transporte urbano de ônibus. O principal objetivo foi estudar e implementar heurísticas com base em Pesquisa Operacional para otimizar problemas de (re)escalonamento de veículos off-line e on-line considerando várias garagens e frota heterogênea. No primeiro artigo, foi proposta uma abordagem heurística para o problema de escalonamento de veículos múltiplas garagens. Acreditamos que as principais contribuições são o método de geração de colunas para grandes instâncias e as técnicas de redução do espaço de estados para acelerar as soluções. No segundo artigo, adicionamos complexidade ao considerar a frota heterogênea, denotada como multiple depot vehicle type scheduling problem (MDVTSP). Embora a importância e a aplicabilidade do MDVTSP, formulações matemáticas e métodos de solução para isso ainda sejam relativamente inexplorados. A principal contribuição desse trabalho foi o método de geração de colunas para o problema com frota heterogênea, já que nenhuma outra proposta na literatura foi identificada no momento pelos autores. Na terceira parte desta tese, no entanto, nos concentramos no reescalonamento em tempo real para o caso de quebras definitivas de veículos. A principal contribuição é a abordagem eficiente do reescalonamento sob uma quebra. A abordagem com redução de espaço de estados, solução inicial e método de geração de colunas possibilitou uma ação realmente em tempo real. Em menos de cinco minutos, reescalonando todas as viagens restantes.In this dissetation we presented a three articles compilation in urban bus transportation optimization. The main objective was to study and implement heuristic solutions method based on Operations Research to optimizing offline and online vehicle (re)scheduling problems considering multiple depots and heterogeneous fleet. In the first paper, a fast heuristic approach to deal with the multiple depot vehicle scheduling problem was proposed. We think the main contributions are the column generation framework for large instances and the state-space reduction techniques for accelerating the solutions. In the second paper, we added complexity when considering the heterogeneous fleet, denoted as "the multiple-depot vehicle-type scheduling problem" (MDVTSP). Although the MDVTSP importance and applicability, mathematical formulations and solution methods for it are still relatively unexplored. We think the main contribution is the column generation framework for instances with heterogeneous fleet since no other proposal in the literature has been identified at moment by the authors. In the third part of this dissertation, however, we focused on the real-time schedule recovery for the case of serious vehicle failures. Such vehicle breakdowns require that the remaining passengers from the disabled vehicle, and those expected to become part of the trip, to be picked up. In addition, since the disabled vehicle may have future trips assigned to it, the given schedule may be deteriorated to the extent where the fleet plan may need to be adjusted in real-time depending on the current state of what is certainly a dynamic system. Usually, without the help of a rescheduling algorithm, the dispatcher either cancels the trips that are initially scheduled to be implemented by the disabled vehicle (when there are upcoming future trips planned that could soon serve the expected demand for the canceled trips), or simply dispatches an available vehicle from a depot. In both cases, there may be considerable delays introduced. This manual approach may result in a poor solution. The implementation of new technologies (e.g., automatic vehicle locators, the global positioning system, geographical information systems, and wireless communication) in public transit systems makes it possible to implement real-time vehicle rescheduling algorithms at low cost. The main contribution is the efficient approach to rescheduling under a disruption. The approach with integrated state-space reduction, initial solution, and column generation framework enable a really real-time action. In less than five minutes rescheduling all trips remaining

    Electromobility in Public Transport: Scheduling of Electric Vehicles and Location Planning of the Charging Infrastructure

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    In recent years, considerable efforts have been made to make public transport more environmentally friendly. This should primarily be achieved by reducing greenhouse gas emissions. Electromobility is considered to be a key technology as electric vehicles create a variety of benefits. However, the use of electric vehicles involves a number of challenges. Modern battery electric vehicles have only a fractional part of the ranges of combustion engine vehicles. Thus, a major challenge is charging the vehicles at specific charging stations to compensate for this disadvantage. Technological aspects of electric vehicles are also of importance and have to be considered. Planning tasks of public transport companies are affected by these challanges, especially vehicle scheduling. Vehicle scheduling is a well-studied optimization problem. The objective is to cover a given set of timetabled service trips by a set of vehicles at minimum costs. An issue strongly related to vehicle scheduling is location planning of the charging infrastructure. For an effcient use of electric vehicles, charging stations must be located at suitable locations in order to minimize operational costs. Location planning of charging stations is a long-term planning task whereas vehicle scheduling is a more short-term planning task in public transport. This thesis examines optimization methods for scheduling electric vehicles in public transport and location planning of the charging infrastructure. Electric vehicles' technological aspects are particularly considered. Case studies based on real-world data are used for evaluation of the artifacts developed. An exact optimization method addresses scheduling of mixed vehicles fleets consisting of electric vehicles and vehicles without range limitations. It is examined whether traditional solution methods for vehicle scheduling are able to cope with the challenges imposed by electric vehicles. The results show, that solution methods for vehicle scheduling are able to deal with the additional challenges to a certain degree. However, novel methods are required to fully deal with the requirements of electric vehicles. A heuristic solution method for scheduling electric vehicles and models for the charging process of batteries are developed. The impact of the detail level of electric vehicles' technological aspects on resulting solutions is analyzed. A computational study reveales major discrepancies between model assumptions and real charging behaviours. A metaheuristic solution method for the simultaneous optimization of location planning of charging stations and scheduling electric vehicles is designed to connect the optimization problems and to open up synergy effects. In comparison to a sequential planning, the simultaneous problem solving is necessary because a sequential planning generally leads to either infeasible solutions or to significant increases in costs.In den letzten Jahren wurden erhebliche Anstrengungen unternommen, um den öffentlichen Personennahverkehr (ÖPNV) umweltfreundlicher zu gestalten. Dabei sollen insbesondere Treibhausgasemissionen reduziert werden. Elektromobilität wird dabei auf Grund der zahlreichen Vorteile von Elektrofahrzeugen als Schlüsseltechnologie angesehen. Der Einsatz von Elektrofahrzeugen ist jedoch mit Herausforderungen verbunden, da diese über weitaus geringere Reichweiten im Vergleich zu Fahrzeugen mit Verbrennungsmotoren verfügen, weshalb ein Nachladen der Fahrzeugbatterien während des Betriebs notwendig ist. Zudem müssen technische Aspekte von Elektrofahrzeugen, wie beispielsweise Batteriealterungsprozesse, berücksichtigt werden. Die Fahrzeugeinsatzplanung als Teil des Planungsprozesses von Verkehrsunternehmen im ÖPNV ist besonders von diesen Herausforderungen betroffen. Diese legt den Fahrzeugeinsatz für die Bedienung der angebotenen Fahrplanfahrten bei Minimierung der Gesamtkosten fest. Die Standortplanung der Ladeinfrastruktur ist eng mit dieser Aufgabe verbunden, da für einen effizienten Einsatz der Fahrzeuge Ladestationen an geeigneten Orten errichtet werden müssen, um Betriebskosten zu minimieren. Die Planung der Ladeinfrastruktur ist ein langfristiges Planungsproblem, wohingegen die Fahrzeugeinsatzplanung eine eher kurzfristige Planungsaufgabe darstellt. Diese Dissertation befasst sich mit Optimierungsmethoden für die Fahrzeugeinsatzplanung mit Elektrofahrzeugen und mit der Standortplanung der Ladeinfrastruktur. Technische Aspekte von Elektrofahrzeugen werden dabei berücksichtigt. Die entwickelten Artefakte werden mit Hilfe von realen Datensätzen evaluiert. Durch eine exakte Optimierungsmethode für die Fahrzeugeinsatzplanung mit gemischten Fahrzeugflotten bestehend aus Fahrzeugen mit und ohne Reichweiterestriktionen wird die Anwendbarkeit von Optimierungsmethoden ohne Berücksichtigung von Reichweitebeschränkungen auf die Herausforderungen von Elektrofahrzeugen untersucht. Die Ergebnisse zeigen, dass herkömmliche Optimierungsmethoden für die neuen Herausforderungen bis zu einem gewissen Grad geeignet sind, es jedoch neuartige Lösungsmethoden erfordert, um den Anforderungen von Elektrofahrzeugen vollständig gerecht zu werden. Mit Hilfe einer heuristischen Lösungsmethode für die Fahrzeugeinsatzplanung mit Elektrofahrzeugen und Modellen für den Ladeprozess von Batterien wird untersucht, inwiefern sich der Detailgrad bei der Abbildung von Ladeprozessen auf resultierende Lösungen auswirkt. Erhebliche Unterschiede zwischen Modellannahmen und realen Gegebenheiten von Ladeprozessen werden herausgearbeitet. Durch ein metaheuristisches Lösungsverfahren für die simultane Optimierung der Standortplanung der Ladeinfrastruktur und der Fahrzeugeinsatzplanung werden beide Problemstellungen miteinander verbunden, um Synergieeffekte offenzulegen. Im Vergleich zu einer sequentiellen Planung ist ein simultanes Lösen notwendig, da ein sequentielles Lösen entweder zu unzulässigen Ergebnissen oder zu erheblichen Kostensteigerungen führt
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