1,401,053 research outputs found
A multi-agent system for a bus crew rescheduling system
No full textThis thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Unpredictable events (UE) are major factors that cause disruption to everyday bus operation. In the occurrence of UE, the main resources - crews and vehicles - are affected, and this leads to crew schedule disruption. One way to deal with the problem is crew rescheduling. Most of the current approaches are based on static schedules do not support rescheduling in a real-time scenario. They have the ability to reschedule but a new complete schedule is produced without concerning the real time situation. The mathematical approaches which are used by most scheduling packages have the ability to search for optimum or near optimum schedules but they are usually slow to produce results in real-time because they are computationally intensive when faced with complex situations. In practice, crew or bus rescheduling is managed manually, based on the supervisor's capabilities and experience in managing UE. However, manual rescheduling is complex, prone to error and not optimum, especially when dealing with many UE at the same time. This research proposes the CRSMAS (Crew Rescheduling System with Multi Agent System) approach as an alternative that may help supervisors to make quick rescheduling decisions by automating the crew rescheduling process. A Multi Agent System (MAS) is considered suitable to support this rescheduling because agents can dynamically adapt their behaviour to changing environments and they can find solutions quickly via negotiations and cooperation between them. To evaluate the CRSMAS, two types of experiment are carried out: Single Event and Multiple Events. The Single Event experiment is used to find characteristics of crew schedules that influence the crew rescheduling process while the Multiple Events experiment is used to test the capability of CRSMAS in dealing with numerous events that occur randomly. A wide range of simulation results, based on real-world data, are reported and analysed. Based on the experiment it is concluded that CRSMAS is suitable for automating the crew rescheduling process and capable of quick rescheduling whether facing single events or multiple events at the same time, the success of rescheduling is not only dependant on the tool but also to other factors such as the characteristics of crew schedules and the period of the UE, and one limitation of CRSMAS that was discovered is it cannot simulate different type of events at the same time. This limitation is because in different events there are different rules but, in Virtual World, agents can only negotiate with one set of rules at a time.Financial support was obtained from the Universiti Teknikal Malaysia Melaka (UTeM)
Algorithm optimization for solving crew scheduling problems
No full textIn this paper, we are proposing a methodology to determine the most efficient and least costly way of crew pairing optimization. We are developing a methodology based on algorithm optimization on Eclipse opensource IDE using the Java programming language to solve the crew scheduling problems.En este trabajo proponemos una metodología para determinar la manera más eficiente y menos costosa de la optimización de emparejamiento de la tripulación. Estamos desarrollando una metodología basada en el algoritmo de optimización en Eclipse IDE de código abierto utilizando el lenguaje de programación Java para resolver los problemas de programación de la tripulación.En aquest treball proposem una metodologia per determinar la manera més eficient i menys costosa de l'optimització d'aparellament de la tripulació. Estem desenvolupant una metodologia basada en l'algoritme d'optimització en Eclipse IDE de codi obert utilitzant el llenguatge de programació Java per resoldre els problemes de programació de la tripulació
Dynamic Modeling of Crew Performance for Long-Duration Space Missions
No full textCrew time is one of the most valuable and limited resources during long-duration space missions. Crew time requirements fluctuate depending on variations in crew performance. The limited number of crewmembers, resources, and the myriad of tasks to be performed leave a tight schedule for crewmembers during long-duration space missions. This schedule needs to account for potential interventions (stress events) that may alter predicted performance and thus scheduling. A dynamic crew model using a stochastic Auto Regressive Integrated Moving Average (ARIMA) model of interrupted time series was developed to account for the effects of potential stress events on crew performance. This model aids in estimating crew time requirements for varying mission scenarios and for evaluating stress event effects on crew performance
Railway Crew Rescheduling with Retiming
No full textRailway operations are disrupted frequently, e.g. the Dutch railway network experiences about three large disruptions per day on average. In such a disrupted situation railway operators need to quickly adjust their resource schedules. Nowadays, the timetable, the rolling stock and the crew schedule are recovered in a sequential way. In this paper, we model and solve the crew rescheduling problem with retiming. This problem extends the crew rescheduling problem by the possibility to delay the departure of some trains. In this way we partly integrate timetable adjustment and crew rescheduling. The algorithm is based on column generation techniques combined with Lagrangian heuristics. In order to prevent a large increase in computational time, retiming is allowed only for a limited number of trains where it seems very promising. Computational experiments with real-life disruption data show that, compared to the classical approach, it is possible to find better solutions by using crew rescheduling with retiming.
Solving large scale crew scheduling problems by using iterative partitioning
No full textThis paper deals with large-scale crew scheduling problems arisingat the Dutch railway operator, Netherlands Railways (NS). NSoperates about 30,000 trains a week. All these trains need a driverand a certain number of conductors. No available crew schedulingalgorithm can solve such huge instances at once. A common approachto deal with these huge weekly instances, is to split them intoseveral daily instances and solve those separately. However, wefound out that this can be rather inefficient.In this paper, we discuss several methods to partition hugeinstances into several smaller ones. These smaller instances arethen solved with the commercially available crew schedulingalgorithm TURNI. We compare these partitioning methods with eachother, and we report several results where we applied differentpartitioning methods after each other. The results show that allmethods significantly improve the solution. With the best approach,we were able to cut down crew costs with about 2\\% (about 6 millioneuro per year).crew scheduling;large-scale optimization;partitioning methods
GEF CReW+ República Dominicana : proyecto de saneamiento de Sabana Yegua
No full textEl video muestra el proyecto de rehabilitación de la Planta de Tratamiento de Aguas Residuales de Sabana Yegua, liderado por el Ministerio de Medio Ambiente y Recursos Naturales y financiado por el Fondo para el Medio Ambiente Mundial (GEF) en el marco del Proyecto GEF CReW+. El GEF CReW+ es una iniciativa de cooperación financiada por el GEF e implementada conjuntamente por el Banco Interamericano de Desarrollo (BID) y el Programa de las Naciones Unidas para el Medio Ambiente (PNUMA) en 18 países de la Región del Gran Caribe, como continuación de la exitosa fase previa “El Fondo Regional del Caribe para la Gestión de Aguas Residuales (CReW)” (2011-2017). El proyecto es ejecutado por Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH, la Organización de los Estados Americanos (OEA) y la Secretaría del Convenio de Cartagena (CAR/RCU), en representación del BID y el PNUMA, respectivamente, e involucra a los siguientes países: Barbados, Belice, Colombia, Costa Rica, Cuba, República Dominicana, Granada, Guatemala, Guyana, Honduras, Jamaica, México, Panamá, Saint Kitts y Nevis, Santa Lucía, San Vicente y las Granadinas, Surinam y Trinidad y Tobago
Bus crew scheduling using mathematical programming
No full textThis thesis describes a bus crew scheduling system, IMPACS, which has been demonstrated to be successful for a wide variety of scheduling conditions, and is at present in regular use by three British bus companies including the largest, London Buses Ltd.
The background to the bus crew scheduling problem 1S described and the existing literature on methods for solution is reviewed.
In IMPACS, the crew scheduling problem is formulated as an integer linear programme using a formulation which is an extension of set covering; a very large set of possible duties is generated, from which the duties forming the schedule are selected in such a way as to minimise the total cost. The variables of the set covering problem correspond to the duties generated and the constraints to the pieces of work in the bus schedule.
For realistic schedules, it is impossible to generate all legal duties, and there are often too many pieces of work to allow each one to give rise to a constraint. IMPACS contains several heuristic methods which reduce the set covering problem to a manageable size, while still allowing good quality schedules to be compiled.
Techniques for speeding up the solution of the set covering problem have been investigated, and in particular a branching strategy which exploits features of the crew scheduling problem has been developed
A solution approach for dynamic vehicle and crew scheduling
No full textIn 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
GEF CReW+ República Dominicana : proyecto de saneamiento de la Universidad Autónoma de Santo Domingo
No full textEl video muestra el proyecto de rehabilitación de la Planta de Tratamiento de Aguas Residuales de la Universidad Autónoma de Santo Domingo (Recinto Santiago), liderado por el Ministerio de Medio Ambiente y Recursos Naturales y financiado por el Fondo para el Medio Ambiente Mundial (GEF) en el marco del Proyecto GEF CReW+. El GEF CReW+ es una iniciativa de cooperación financiada por el GEF e implementada conjuntamente por el Banco Interamericano de Desarrollo (BID) y el Programa de las Naciones Unidas para el Medio Ambiente (PNUMA) en 18 países de la Región del Gran Caribe, como continuación de la exitosa fase previa “El Fondo Regional del Caribe para la Gestión de Aguas Residuales (CReW)” (2011-2017). El proyecto es ejecutado por Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH, la Organización de los Estados Americanos (OEA) y la Secretaría del Convenio de Cartagena (CAR/RCU), en representación del BID y el PNUMA, respectivamente, e involucra a los siguientes países: Barbados, Belice, Colombia, Costa Rica, Cuba, República Dominicana, Granada, Guatemala, Guyana, Honduras, Jamaica, México, Panamá, Saint Kitts y Nevis, Santa Lucía, San Vicente y las Granadinas, Surinam y Trinidad y Tobago
Crew Rostering for the High Speed Train
No full textAt the time of writing we entered the final stage of implementing the crew rostering system Harmony CDR to facilitate the planning of catering crews on board of the Thalys, the High Speed Train connecting Paris, Cologne, Brussels, Amsterdam, and Geneva. Harmony CDR optimally supports the creation of crew rosters in two ways. Firstly, Harmony CDR contains a powerful algorithm to automatically generate a set of rosters, which is especially developed for this specific situation. As the user has some control over the objectives of the algorithm, several scenarios can be studied before a set of rosters is adopted. An important feature of the automatic roster generator is that it respects requirements, directives, and requests stemming from legal, union, and/or company regulations and/or from individual crew. Secondly, Harmony CDR provides user-interface data manipulation at various levels of detail. The user interface enables the planner to easily obtain many different views on the planning data and to manipulate the planning manually. So again, the planner gets optimal support from the system while he or she is still in control. Also, violating a requirement, directive, or request is detected and displayed, but can be accepted by the planner. In this paper we describe the crew rostering problem for the catering crews of the High Speed Train and the Harmony CDR solution in more detail.decision support systems;railways;crew rostering
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