Hacia la integración de la resincronización en el problema de programación por tipo de vehículo

In this paper, we propose an integer linear programming (ILP) aiming at optimizing timetabling generation and the Vehicle Type Scheduling Problem (VTSP), based on a time-space network (TSN). The model was defined as Vehicle Type Scheduling Problem with Sequential Changes of timetable (VTSP- SCT). Additionally, we developed a new methodology to insert time window to the proposed problem based on small changes on the TSN structure, with easy computational implementation and optimal solution at low computation run-times. By including small changes to the timetable and/or including time windows for timetabling trips, we introduced flexibility levels in the departure times of trips, resulting in operational advantages for the service provider. Since we use a very short time window interval, the current timetable is only slightly modified, minimally changing the passenger routines. The developed approaches were tested using random instances based on a Brazilian city. The VTSP-SCT with and without time windows have resulted in relevant savings in the daily operations of the public transportation service, reducing the required number of scheduled vehicles to carry out the historic demand.En este documento, proponemos una programación lineal entera (ILP) con el objetivo de optimizar la generación de horarios y el problema de programación de tipo de vehículo (VTSP), basado en una red de tiempo y espacio (TSN). El modelo se definió como un problema de programación de tipo de vehículo con cambios secuenciales de horario (VTSP-SCT). Además, desarrollamos una nueva metodología para insertar una ventana de tiempo al problema propuesto en base a pequeños cambios en la estructura TSN, con una implementación computacional sencilla y una solución óptima en tiempos de ejecución de computación bajos. Al incluir pequeños cambios en el cronograma y/o incluir ventanas de tiempo para programar viajes, introdujimos niveles de flexibilidad en los horarios de salida de los viajes, lo que resulta en ventajas operacionales para el proveedor del servicio. Dado que usamos un intervalo de ventana de tiempo muy corto, el horario actual solo se modifica ligeramente, cambiando mínimamente las rutinas de los pasajeros. Los enfoques desarrollados fueron probados usando instancias aleatorias basadas en una ciudad brasileña. El VTSP-SCT con y sin ventanas de tiempo ha resultado en ahorros relevantes en las operaciones diarias del servicio de transporte público, reduciendo el número requerido de vehículos programados para llevar a cabo la demanda histórica

The Vehicle Rescheduling Problem with Retiming

When a vehicle breaks down during operation in a public transportation system, the remaining vehicles can be rescheduled to minimize the impact of the breakdown. In this paper, we discuss the vehicle rescheduling problem with retiming (VRSPRT). The idea of retiming is that scheduling flexibility is increased, such that previously inevitable cancellations can be avoided. To incorporate delays, we expand the underlying recovery network with retiming possibilities. This leads to a problem formulation that can be solved using Lagrangian relaxation. As the network gets too large, we propose an iterative neighborhood exploration heuristic to solve the VRSPRT. This heuristic allows retiming for a subset of trips, and adds promising trips to this subset as the al

Cargo Consolidation and Distribution Through a Terminals-Network: A Branch-And-Price Approach

Less-than-truckload is a transport modality that includes many practical variations to convey a number of transportation-requests from the origin locations to their destinations by using the possibility of goods-transshipments on the carrier?s terminals-network. In this way logistics companies are required to consolidate shipments from different suppliers in the outbound vehicles at a terminal of the network. We present a methodology for finding near-optimal solutions to a less-than-truckload shipping modality used for cargo consolidation and distribution through a terminals-network. The methodology uses column generation combined with an incomplete branch-and-price procedure.Fil: Dondo, Rodolfo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentin

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

Solution Approaches for Vehicle and Crew Scheduling with Electric Buses

The use of electric buses is expected to rise due to its environmental benefits. However, electric vehicles are less exible than conventional diesel buses due to their limited driving range and longer recharging times. Therefore, scheduling electric vehicles adds further operational dificulties. Additionally, various labor regulations challenge public transport companies to find a cost-effcient crew schedule. Vehicle and crew scheduling problems essentially define the cost of operations. In practice, these two problems are often solved sequentially. In this paper, we introduce the integrated electric vehicle and crew scheduling problem (E-VCSP). Given a set of timetabled trips and recharging stations, the E-VCSP is concerned with finding vehicle and crew schedules that cover the timetabled trips and satisfy operational constraints, such as limited driving range of electric vehicles and labor regulations for the crew while minimizing total operational cost. An adaptive large neighborhood search that utilizes branch-and-price heuristics is proposed to tackle the E-VCSP. The proposed method is tested on real-life instances from public transport companies in Denmark and Sweden that contain up to 1,109 timetabled trips. The heuristic approach provides evidence of improving efficiency of transport systems when the electric vehicle and crew scheduling aspects are considered simultaneously. By comparing to the traditional sequential approach, the heuristic finds improvements in the range of 1.17-4.37% on average. A sensitivity analysis of the electric bus technology is carried out to indicate its implications for the crew schedule and the total operational cost. The analysis shows that the operational cost decreases with increasing driving range (120 to 250 kilometers) of electric vehicles

A Matheuristic for Integrated Timetabling and Vehicle Scheduling

Planning a public transportation system is a complex process, which is usually broken down in several phases, performed in sequence. Most often, the trips required to cover a service with the desired frequency (headway) are decided early on, while the vehicles needed to cover these trips are determined at a later stage. This potentially leads to requiring a larger number of vehicles (and, therefore, drivers) that would be possible if the two decisions were performed simultaneously. We propose a multicommodity-flow type model for integrated timetabling and vehicle scheduling. Since the model is large-scale and cannot be solved by off-the-shelf tools with the efficiency required by planners, we propose a diving-type matheuristic approach for the problem. We report on the efficiency and effectiveness of two variants of the proposed approach, differing on how the continuous relaxation of the problem is solved, to tackle real-world instances of bus transport planning problem originating from customers of M.A.I.O.R., a leading company providing services and advanced decision-support systems to public transport authorities and operators. The results show that the approach can be used to aid even experienced planners in either obtaining better solutions, or obtaining them faster and with less effort, or both

Integrating Timetabling and Crew

We investigate to what degree we can integrate a Train Timetabling / Engine Scheduling Problem with a Crew Scheduling Problem. In the Timetabling Problem we design a timetable for the desired lines by fixing the departure and arrival times. Also, we allocate time-slots in the network to secure a feasible timetable. Next, we assign engines in the Engine Scheduling Problem to the lines in accordance with the timetable. The overall integration is achieved by obtaining an optimal solution for the Timetabling / Engine Scheduling Problem. We exploit the fact that numerous optimal, and near optimal solutions exists. We consider all solutions that can be obtained from the optimal engine schedule by altering the timetable, while keeping the order of demands in the schedules intact. The Crew Scheduling model is allowed to re-time the service of demands if the additional cost is outweighed by the crew savings. This information is implemented in a mathematical model for the Crew Scheduling Problem. The model is solved using a column generation scheme. Hereby it is possible for the Crew Scheduling algorithm to adjust the timetable and achieve a better overall solution. We perform computational experiments based on a case at a freight railway operator, DB Schenker Rail Scandinavia, and show that significant cost savings can be achieved

Data-driven optimization of bus schedules under uncertainties

Plusieurs sous-problèmes d’optimisation se posent lors de la planification des transports publics. Le problème d’itinéraires de véhicule (PIV) est l’un d’entre eux et consiste à minimiser les coûts opérationnels tout en assignant exactement un autobus par trajet planifié de sorte que le nombre d’autobus entreposé par dépôt ne dépasse pas la capacité maximale disponible. Bien que les transports publics soient sujets à plusieurs sources d’incertitude (à la fois endogènes et exogènes) pouvant engendrer des variations des temps de trajet et de la consommation d’énergie, le PIV et ses variantes sont la plupart du temps résolus de façon déterministe pour des raisons de résolubilité. Toutefois, cette hypothèse peut compromettre le respect de l’horaire établi lorsque les temps des trajets considérés sont fixes (c.-à-d. déterministes) et peut produire des solutions impliquant des politiques de gestion des batteries inadéquates lorsque la consommation d’énergie est aussi considérée comme fixe. Dans cette thèse, nous proposons une méthodologie pour mesurer la fiabilité (ou le respect de l’horaire établi) d’un service de transport public ainsi que des modèles mathématiques stochastiques et orientés données et des algorithmes de branch-and-price pour deux variantes de ce problème, à savoir le problème d’itinéraires de véhicule avec dépôts multiples (PIVDM) et le problème d’itinéraires de véhicule électrique (PIV-E). Afin d’évaluer la fiabilité, c.-à-d. la tolérance aux délais, de certains itinéraires de véhicule, nous prédisons d’abord la distribution des temps de trajet des autobus. Pour ce faire, nous comparons plusieurs modèles probabilistes selon leur capacité à prédire correctement la fonction de densité des temps de trajet des autobus sur le long terme. Ensuite, nous estimons à l'aide d'une simulation de Monte-Carlo la fiabilité des horaires d’autobus en générant des temps de trajet aléatoires à chaque itération. Nous intégrons alors le modèle probabiliste le plus approprié, celui qui est capable de prédire avec précision à la fois la véritable fonction de densité conditionnelle des temps de trajet et les retards secondaires espérés, dans nos modèles d'optimisation basés sur les données. Deuxièmement, nous introduisons un modèle pour PIVDM fiable avec des temps de trajet stochastiques. Ce problème d’optimisation bi-objectif vise à minimiser les coûts opérationnels et les pénalités associées aux retards. Un algorithme heuristique basé sur la génération de colonnes avec des sous-problèmes stochastiques est proposé pour résoudre ce problème. Cet algorithme calcule de manière dynamique les retards secondaires espérés à mesure que de nouvelles colonnes sont générées. Troisièmement, nous proposons un nouveau programme stochastique à deux étapes avec recours pour le PIVDM électrique avec des temps de trajet et des consommations d’énergie stochastiques. La politique de recours est conçue pour rétablir la faisabilité énergétique lorsque les itinéraires de véhicule produits a priori se révèlent non réalisables. Toutefois, cette flexibilité vient au prix de potentiels retards induits. Une adaptation d’un algorithme de branch-and-price est développé pour évaluer la pertinence de cette approche pour deux types d'autobus électriques à batterie disponibles sur le marché. Enfin, nous présentons un premier modèle stochastique pour le PIV-E avec dégradation de la batterie. Le modèle sous contrainte en probabilité proposé tient compte de l’incertitude de la consommation d’énergie, permettant ainsi un contrôle efficace de la dégradation de la batterie grâce au contrôle effectif de l’état de charge (EdC) moyen et l’écart de EdC. Ce modèle, combiné à l’algorithme de branch-and-price, sert d’outil pour balancer les coûts opérationnels et la dégradation de la batterie.The vehicle scheduling problem (VSP) is one of the sub-problems of public transport planning. It aims to minimize operational costs while assigning exactly one bus per timetabled trip and respecting the capacity of each depot. Even thought public transport planning is subject to various endogenous and exogenous causes of uncertainty, notably affecting travel time and energy consumption, the VSP and its variants are usually solved deterministically to address tractability issues. However, considering deterministic travel time in the VSP can compromise schedule adherence, whereas considering deterministic energy consumption in the electric VSP (E-VSP) may result in solutions with inadequate battery management. In this thesis, we propose a methodology for measuring the reliability (or schedule adherence) of public transport, along with stochastic and data-driven mathematical models and branch-and-price algorithms for two variations of this problem, namely the multi-depot vehicle scheduling problem (MDVSP) and the E-VSP. To assess the reliability of vehicle schedules in terms of their tolerance to delays, we first predict the distribution of bus travel times. We compare numerous probabilistic models for the long-term prediction of bus travel time density. Using a Monte Carlo simulation, we then estimate the reliability of bus schedules by generating random travel times at each iteration. Subsequently, we integrate the most suitable probabilistic model, capable of accurately predicting both the true conditional density function of the travel time and the expected secondary delays, into the data-driven optimization models. Second, we introduce a model for the reliable MDVSP with stochastic travel time minimizing both the operational costs and penalties associated with delays. To effectively tackle this problem, we propose a heuristic column generation-based algorithm, which incorporates stochastic pricing problems. This algorithm dynamically computes the expected secondary delays as new columns are generated. Third, we propose a new two-stage stochastic program with recourse for the electric MDVSP with stochastic travel time and energy consumption. The recourse policy aims to restore energy feasibility when a priori vehicle schedules are unfeasible, which may lead to delays. An adapted algorithm based on column generation is developed to assess the relevance of this approach for two types of commercially available battery electric buses. Finally, we present the first stochastic model for the E-VSP with battery degradation. The proposed chance-constraint model incorporates energy consumption uncertainty, allowing for effective control of battery degradation by regulating the average state-of-charge (SOC) and SoC deviation in each discharging and charging cycle. This model, in combination with a tailored branch-and-price algorithm, serves as a tool to strike a balance between operational costs and battery degradation

Solving transportation scheduling problems : models and algorithms

Given the increasing load on current logistics and transportation systems, it is crucial to improve resource utilization and reduce operational costs. This can be achieved by developing better models and algorithms for transportation planning and scheduling. The main challenges include the mathematical modeling of operational rules, uncertainties in operations, and large-scale problem size. This dissertation addresses crew scheduling in freight railways and vehicle routing problems (VRP) for mail processing and distribution centers (P&DCs). Our goal is to develop models and algorithms that improve efficiency and reduce operating costs. In Chapter 2, we propose an optimization model to support real-time freight railway crew assignment decisions. Due to workload balance requirements and operating regulations, the optimization model is difficult to solve for realistic instances. Hence, we propose model improvements and develop effective solution techniques to find optimal or near-optimal solutions very quickly. Chapter 3 extends the freight rail crew scheduling problem by incorporating uncertainty in train arrival and departure times. We propose a stochastic programming model, but this model is solvable only the number of scenarios is small. As a consequence, we develop heuristics that use an analytical model to calculate the expected total cost of a given choice of crew deadheads. Using this cost evaluator, we develop four local search based heuristic algorithms to sequentially improve crew scheduling decisions under uncertainty. In Chapter 4, we first cluster the pickup and drop off points in mail P&DCs into zones and then minimize the number of vehicles required and the total distance traveled to meet daily transport demand. The clustering is performed with a greedy randomized adaptive search procedure, and two heuristics are developed to find solutions to the VRP, which proved intractable for realistic instances. The heuristics are optimization-based within a rolling horizon framework. An extensive analysis is undertaken to evaluate the relative performance of the two heuristics. The contributions of this dissertation include modeling, algorithmic development, computational testing, and validation using real and randomly generated data.Mechanical Engineerin