A flexible system for scheduling drivers

A substantial part of the operating costs of public transport is attributable to drivers, whose efficient use therefore is important. The compilation of optimal work packages is difficult, being NP-hard. In practice, algorithmic advances and enhanced computing power have led to significant progress in achieving better schedules. However, differences in labor practices among modes of transport and operating companies make production of a truly general system with acceptable performance a difficult proposition. TRACS II has overcome these difficulties, being used with success by a substantial number of bus and train operators. Many theoretical aspects of the system have been published previously. This paper shows for the first time how theory and practice have been brought together, explaining the many features which have been added to the algorithmic kernel to provide a user-friendly and adaptable system designed to provide maximum flexibility in practice. We discuss the extent to which users have been involved in system development, leading to many practical successes, and we summarize some recent achievements

Solving Challenging Real-World Scheduling Problems

This work contains a series of studies on the optimization of three real-world scheduling problems, school timetabling, sports scheduling and staff scheduling. These challenging problems are solved to customer satisfaction using the proposed PEAST algorithm. The customer satisfaction refers to the fact that implementations of the algorithm are in industry use. The PEAST algorithm is a product of long-term research and development. The first version of it was introduced in 1998. This thesis is a result of a five-year development of the algorithm. One of the most valuable characteristics of the algorithm has proven to be the ability to solve a wide range of scheduling problems. It is likely that it can be tuned to tackle also a range of other combinatorial problems. The algorithm uses features from numerous different metaheuristics which is the main reason for its success. In addition, the implementation of the algorithm is fast enough for real-world use.Siirretty Doriast

Modelo de asignación de turnos para la programación de conductores en la operación de sistemas de transporte masivo

La operación de los sistemas de transporte público con autobuses, es puesta en marcha después de un proceso de planeación secuencial de cuatro etapas que finaliza con la programación y asignación del recurso humano a la operación de los vehículos, para cumplir con las rutas y servicios requeridos. Los conductores de los autobuses representan en todos los sistemas una buena parte del costo operacional, siendo por tanto de alto interés el tener una óptima programación de este personal. En este trabajo de grado se plantea la solución en varias etapas secuenciales con técnicas heurísticas y metaheurísticas para la generación de turnos de conductores. La primera etapa hace una reparación de posibles problemas que puedan tenerse desde la programación de buses, la segunda divide la programación de autobuses reparada en bloques de 2 a 6 horas continuas y la tercera hace una combinación de bloques para formar los turnos. Por último se presentan los resultados de aplicación del modelo en el caso de estudio del Sistema Integrado de Transporte Masivo (SITM) Megabús. El documento presenta los antecedentes de la problemática del problema de programación de turnos de conductores de buses conocido como BDSP (Bus Driver Scheduling Problem) para lo cual presenta el análisis de artículos relevantes que constituyen el estado del arte del problema. Posteriormente se presenta la formulación y justificación del problema y los objetivos que se plantean en este trabajo. Se incluye una descripción general del SITM Megabús sobre el cual se da el caso de aplicación. Se plantea el enfoque metodológico del trabajo en donde se presenta el planteamiento del problema y propuesta de solución para la generación de turnos de conductores de buses. Por último se presentan los resultados obtenidos de aplicación del modelo sobre rutas alimentadoras de la cuenca de Dosquebradas del SITM Megabús, conclusiones y recomendaciones

A multi-agent system for a bus crew rescheduling system

This 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)

Railway crew rescheduling for disruption management

Unforeseen events from external influences such as major weather events, and internal causes such as infrastructure failures disrupt daily dense train operations. Such disruptions can quickly spread over the network and cause planned crew schedules to become infeasible to follow. Being one of the important steps in recovery of the railway service following a disruption, if crew rescheduling is not properly considered, it can jeopardise the return to stable service. This thesis mainly focuses on railway crew rescheduling for disruption management. This thesis studies real-time railway crew rescheduling in theory and practice. By carefully examining the current literature on railway crew rescheduling, this thesis presents a detailed analysis and comparison of the current methods. This thesis provides models and methods for the crew rescheduling problems caused by two distinct types of disruptions: minor disruptions and significant disruptions. Sensitivity tests are conducted on several parameters to explore the impact on solutions. Meanwhile, this thesis considers that optimisation tools for solving the railway crew rescheduling problem cannot be a standalone optimisation tool for controllers to use. If no solution and no further information is given by an optimisation tool, time will be wasted. If no feedback is given by an optimisation tool, there will be no resolution. When such situations occur, controllers usually do not know what has happened inside an optimisation tool and how to get potential solutions. A feedback mechanism is proposed to output the reasons for not producing solutions and to adjust parameter values used in the crew rescheduling problems to give a good chance of generating results with revised values. A timetable rescheduling model is proposed to model the impact on train services of a disruption and predict the recovery period. The recovery period measures how quickly a timetable can return to its normal level. A disruption neighbourhood is introduced as an idea, which is used to identify the drivers that should be considered in the crew rescheduling model for significant disruptions. It is characterised by the drivers who are included in the model and the recovery period. Algorithms are proposed to find the drivers that should be considered in a disruption neighbourhood to obtain good solutions. Several mathematical techniques and methods are proposed to speed up the solution time for the crew rescheduling problem for significant disruptions. Further, the integrated rolling stock and crew rescheduling problem is still an immature research area. This thesis presents detailed formulations to model the problem and explores this problem with retiming possibilities. It can provide mutually feasible rescheduling solutions between rolling stock rescheduling and crew rescheduling. Several goals that relate to rolling stock and crew during disruption management are considered, analysed and further grouped into different objectives. Two kinds of multicriteria decision making (MCDM) methods are used to produce a set of optimal solutions for the integrated problem

A multi-agent system for a bus crew rescheduling system

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.EThOS - Electronic Theses Online ServiceUniversiti Teknikal Malaysia Melaka (UTeM)GBUnited Kingdo

Passenger train unit scheduling optimisation

This thesis deals with optimisation approaches for the train unit scheduling problem (TUSP). Given a train operator’s fixed timetables and a fleet of train units of different types, the TUSP aims at determining an assignment plan such that each train trip in the timetable is appropriately covered by a single or coupled units, with certain objectives achieved and certain constraints respected. From the perspective of a train unit, scheduling assigns a sequence of trains to it as its daily workload. The TUSP also includes some auxiliary activities such as empty-running generation, coupling/decoupling control, platform assignment, platform/siding/depot capacity control, re-platforming, reverse, shunting movements from/to sidings or depots and unit blockage resolution. It is also relevant with activities like unit overnight balance, maintenance provision and unit rostering. In general, it is a very complex planning process involving various aspects. Current literature on optimisation methods for the TUSP is very scarce, and for those existing ones they are generally unsuitable for the UK railway industry, either due to different problem settings and operational regulations or simplifications on some critical factors in practice. Moreover, there is no known successful commercial software for automatically optimising train unit scheduling in the world as far as the author is aware, in contrast with bus vehicle scheduling, crew scheduling and flight scheduling. This research aims at taking an initial step for filling the above gaps. A two-level framework for solving the TUSP has been proposed based on the connection-arc graph representation. The network-level as an integer multicommodity flow model captures the essence of the rail network and allocates the optimum amount of train unit resources to each train globally to ensure the overall optimality, and the station-level process (post-processing) resolves the remaining local issues like unit blockage. Several ILP formulations are presented to solve the network-level model. A local convex hull method is particularly used to realise difficult requirements and tighten LP relaxation and some further discussions over this method is also given. Dantzig-Wolfe decomposition is used to convert an arc formulation to a path formulation. A customised branch-and-price solver is designed to solve the path formulation. Extensive computational experiments have been conducted based on real-world problem instances from ScotRail. The results are satisfied by rail practitioners from ScotRail and are generally competitive or better than the manual ones. Experiments for fine-tuning the branch-and-price solver, solution quality analysis, demand estimation and post-processing have also been carried out and the results are reported. This research has laid a promising foundation leading to a continuation EPSRC funded project (EP/M007243/1) in collaboration with FirstGroup and Tracsis plc