Issues on simulation of the railway rolling stock operation process – a system and literature review

Railway traffic simulation, taking into account operation and maintenance conditions, is not a new issue in the literature. External effects in such networks (eg. level crossings) were not taken into account in studies. The used models do not take into account sufficiently the process of degradation and recovery of the network. From the technical side, currently carried out simulations are made using similar approaches and techniques as in the initial stage of research. Well-established work in this area could be the basis for evaluation of new solutions. However, the progress in simulation tools during the last years, especially in performance and programming architecture, attempt to create a modern simulation tool. In the paper were presented the main assumptions for the evaluated event-based simulation method, with application to stiff-track transportation network

Crew Scheduling Considering both Crew Duty Time Difference and Cost on Urban Rail System

Urban rail crew scheduling problem is to allocate train services to crews based on a given train timetable while satisfying all the operational and contractual requirements. In this paper, we present a new mathematical programming model with the aim of minimizing both the related costs of crew duty and the variance of duty time spreads. In addition to iincorporating the commonly encountered crew scheduling constraints, it also takes into consideration the constraint of arranging crews having a meal in the specific meal period of one day rather than after a minimum continual service time. The proposed model is solved by an ant colony algorithm which is built based on the construction of ant travel network and the design of ant travel path choosing strategy. The performances of the model and the algorithm are evaluated by conducting case study on Changsha urban rail. The results indicate that the proposed method can obtain a satisfactory crew schedule for urban rails with a relatively small computational time

The crew scheduling problem of an interurban public transport bus company

Una planificación de los conductores adecuada impacta en el coste operacional de las empresas de transporte público. La dificultad de esta tarea se debe principalmente a dos aspectos (Esclapés 2001, Bonrostro, Yusta 2003, Ernst et al. 2004, Van den Bergh et al. 2013, Ibarra-Rojas et al. 2015, Li et al. 2015): por un lado, la planificación de los conductores es parte de un problema mayor, la planificación de los vehículos y conductores. Por otro lado, las diferencias entre las características de las redes de transporte, los recursos de las empresas, las restricciones reglamentarias o los acuerdos laborales hacen que las soluciones sean particulares para cada empresa. El objetivo principal de esta investigación es desarrollar un algoritmo eficiente que minimice en un tiempo de ejecución aceptable el problema de la planificación de los conductores de una compañía de autobuses de transporte de pasajeros público interurbano, permitiendo relevos ilimitados en cualquier parada de la red, es decir, al principio, final o cualquier otra parada intermedia de una línea. De esta manera, haciendo uso de la herramienta en una empresa real, se han examinado dos lagunas de investigación encontradas en el análisis de la literatura. Por un lado, el impacto de permitir relevos ilimitados al principio, al final o en cualquier otra parada intermedia de una línea. Por otro lado, el impacto del proceso de planificación cuando las restricciones a cumplir varían según el tipo de servicio que se incluye en las jornadas. Se han analizado dos procesos: el dividir el problema en problemas independientes según las características de los servicios, o el llevar a cabo una planificación global bajo las restricciones más restrictivas. Con respecto a la metodología de investigación, se han seguido los siete pasos de la Investigación Operativa (Winston, Goldberg 2004): (1) formular el problema, (2) observar el sistema, (3) formular un modelo del problema, (4) verificar el modelo y usarlo para la predicción, (5) seleccionar una alternativa adecuada, (6) presentar los resultados y conclusiones del estudio e (7) implementar y evaluar las recomendaciones. Los resultados muestran que en ocasiones vale la pena considerar los factores investigados.Gidarien lanaren plangintza egoki batek zuzenki eragiten du garraio publikoko enpresen kostu operatiboan. Tripulazioaren plangintzaren zailtasuna bi arrazoiengatik ematen da bereziki (Esclapés 2001, Bonrostro, Yusta 2003, Ernst et al. 2004, Van den Bergh et al. 2013, Ibarra-Rojas et al. 2015, Li et al. 2015): alde batetik, gidarien plangintza beste arazo handiago baten parte da, ibilgailu eta gidarien plangintzaren arazoaren parte. Bestalde, garraio sareen arteko ezberdintasunek, enpresen baliabideen arteko ezberdintasunek edota arautegi edo lan-akordioen arteko ezberdintasunek, enpresa bakoitzarentzako soluzio partikular bat garatzea behartzen dute. Ikerketa honen helburu nagusia "algoritmo eraginkor bat garatzea da, zeinek exekuzio denbora apropos baten, eta lehen, azken edo beste edozein bitarteko geldialditan errelebua baimenduz, hiriarteko sare baten diharduen garraio publikoko autobus konpainia batek behar duen tripulazioa minimizatzen duen". Horrela, eta konpainia erreal baten tripulazioaren planifikazioa oinarritzat hartuta, literaturan aurkitutako bi ikerketa-hutsune aztertu dira. Alde batetik, zenbatetan mugatu ezak eta lehen, azken edo beste edozein bitarteko geldialditan errelebuak baimentzeak daukan inpaktua aztertuko da. Bestalde, planifikatzerakoan ezaugarri ezberdinak dituzten zerbitzuek errestrikzio ezberdinak kontsideratzea behartzen dutenean, planifikazio prozesua aztertu da. Bi prozedura aztertu dira: arazoa zerbitzuen ezaugarrien araberako planifikazio independentetan banatzea edo errestrikzio gogorrenak kontsideratuta, planifikazio bakar bat osatzea. Ikerketaren metodologiari dagokionez, Eragiketen Ikerketako (Winston, Goldberg 2004) zazpi urratsak jarraitu dira: (1) arazoa formulatzea, (2) sistemaren behaketa, (3) arazoaren eredua formulatu, (4) eredua egiaztatzea eta aurreikuspenerako erabiltzea, (5) aukera egokia aukeratzea, (6) azterketaren emaitzak eta ondorioak aurkeztea eta (7) gomendioak ezartzea eta ebaluatzea. Emaitzen arabera, kasu batzuetan ikertu diren bi faktoreek emaitza hobeagoak dakartzatela baieztatu da.A proper crew scheduling impacts on the operational cost of public transport companies. The difficulty of the crew scheduling is due to two main aspects (Esclapés 2001, Bonrostro, Yusta 2003, Ernst et al. 2004, Van den Bergh et al. 2013, Ibarra-Rojas et al. 2015, Li et al. 2015): first, it is part of a larger problem, the Vehicle and Crew Scheduling Problem. Second, the differences among network features, resources of companies, regulatory restrictions or labour agreements make the solutions particular to each company. The main objective of the present research work is “to develop an efficient algorithm which minimizes in an acceptable execution time the Crew Scheduling Problem of an interurban passenger public transport bus company, allowing unlimited drivers’ reliefs that can occur at first, last or any other intermediate stop of a line”. So, using this tool on a real company’s crew scheduling problem, two research gaps found in the analysis of the literature have been examined. On one hand, the impact of allowing unlimited drivers’ reliefs that can occur at first, last or any other intermediate stop of a line. On the other hand, the impact of the scheduling procedure when restrictions vary depending on the type of service that is included in the duty. Two procedures have been studied: dividing the problem into independent problems or scheduling globally under the most limited restrictions. Concerning the research methodology, the seven steps of Operations Research (Winston, Goldberg 2004) have been followed: (1) formulate the problem, (2) observe the system, (3) formulate a model of the problem, (4) verify the model and use the model for prediction, (5) select a suitable alternative, (6) present the results and conclusion of the study and (7) implement and evaluate the recommendations. The results show that occasionally it is worthy to consider both investigated factors

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Abstract The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown

A Genetic Algorithm-Based Column Generation Approach to the Passenger Rail Crew Scheduling Problem

The goal of this thesis was to develop and apply a genetic algorithm-based column generation heuristic to solve a passenger rail crew scheduling problem. The crew scheduling problem minimized the total cost of payment to crew members based on the hours on-board, hours away from a crew base, number of nights of lodging, and number of on-board and away meals. Payment regulations also dictated an overtime payment and a guaranteed salary per week. Additional problem constraints included restrictions on the maximum number of continuous working hours, maximum number of days worked per week, and minimum hours of rest. The proposed heuristic produced solutions with improvements of total cost ranging from 3.0 percent to 27.9 percent

Scalable randomized patrolling for securing rapid transit networks

Mass Rapid Transit using rail is a popular mode of transport employed by millions of people in many urban cities across the world. Typically, these networks are massive, used by many and thus, can be a soft target for criminals. In this paper, we consider the problem of scheduling randomised patrols for improving security of such rail networks. Similar to existing work in randomised patrols for protecting critical infrastructure, we also employ Stackelberg Games to represent the problem. In solving the Stackelberg games for massive rail networks, we make two key contributions. Firstly, we provide an approach called RaPtoR for computing randomized strategies in patrol teams, which guarantees (i) Strong Stackelberg equilibrium (SSE); and (ii) Optimality in terms of distance traveled by the patrol teams for specific constraints on schedules. Secondly, we demonstrate RaPtoR on a real world data set corresponding to the rail network in Singapore. Furthermore, we also show that the algorithm scales easily to large rail networks while providing SSE randomized strategies

Evolutionary algorithms for scheduling operations

While business process automation is proliferating through industries and processes, operations such as job and crew scheduling are still performed manually in the majority of workplaces. The linear programming techniques are not capable of automated production of a job or crew schedule within a reasonable computation time due to the massive sizes of real-life scheduling problems. For this reason, AI solutions are becoming increasingly popular, specifically Evolutionary Algorithms (EAs). However, there are three key limitations of previous studies researching application of EAs for the solution of the scheduling problems. First of all, there is no justification for the selection of a particular genetic operator and conclusion about their effectiveness. Secondly, the practical efficiency of such algorithms is unknown due to the lack of comparison with manually produced schedules. Finally, the implications of real-life implementation of the algorithm are rarely considered. This research aims at addressing all three limitations. Collaborations with DBSchenker,the rail freight carrier, and Garnett-Dickinson, the printing company,have been established. Multi-disciplinary research methods including document analysis, focus group evaluations, and interviews with managers from different levels have been carried out. A standard EA has been enhanced with developed within research intelligent operators to efficiently solve the problems. Assessment of the developed algorithm in the context of real life crew scheduling problem showed that the automated schedule outperformed the manual one by 3.7% in terms of its operating efficiency. In addition, the automatically produced schedule required less staff to complete all the jobs and might provide an additional revenue opportunity of £500 000. The research has also revealed a positive attitude expressed by the operational and IT managers towards the developed system. Investment analysis demonstrated a 41% return rate on investment in the automated scheduling system, while the strategic analysis suggests that this system can enable attainment of strategic priorities. The end users of the system, on the other hand, expressed some degree of scepticism and would prefer manual methods

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes