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Most of the existing path planning methods of automated guided vehicles (AGVs) are static. This paper proposes a new methodology for the path planning of a fleet of AGVs to improve the flexibility, robustness, and scalability of the AGV system. We mathematically describe the transport process as a dynamical system using an ad hoc mixed logical dynamical (MLD) model. Based on our MLD model, model predictive control is proposed to determine the collision paths dynamically, and the corresponding optimization problem is formulated as 0-1 integer linear programming. An alternating direction method of multipliers (ADMM)-based decomposition technique is then developed to coordinate the AGVs and reduce the computational burden, aiming for real-time decisions. The proposed methodology is tested on industrial scenarios, and results from numerical experiments show that the proposed method can obtain high transport productivity of the multi-AGV system at a low computational burden and deal with uncertainties resulting from the industrial environment., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Transport Engineering and Logistics

Operations of Automated Guided Vehicles (AGVs) are desired to be more energy-efficient while maintaining high transport productivity, motivated by the green production requirements. This paper investigates a new energy-efficient planning problem for determining conflict-free paths of the AGVs in its transport roadmap. In this problem, the vehicle path and transport time in the roadmap are jointly optimized, based on a flexible time–space network (FTSN). We provide the mathematical problem formulation of the energy-efficient path planning problem. The resulting optimization problem is proved to be a non-convex mixed-integer nonlinear programming which is computationally intractable. We further propose a hybrid metaheuristic that integrates the genetic algorithm and estimation of the distribution algorithm to improve its computational efficiency. Numerical results show the effectiveness of the developed algorithm based on the FTSN framework, compared to the existing metaheuristics, the conventional path planning method, and a commercial solver. The proposed method has a wide application in improving energy use of material handling, providing a guiding significance on promoting cleaner production of flexible manufacturing systems., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Transport Engineering and Logistics

Energy consumption is expected to be reduced while maintaining high productivity for container handling. This paper investigates a new energy-efficient scheduling problem of automated container terminals, in which quay cranes (QCs) and lift automated guided vehicles (AGVs) cooperate to handle inbound and outbound containers. In our scheduling problem, operation times and task sequences are both to be determined. The underlying optimization problem is mixed-integer nonlinear programming (MINLP). To deal with its computational intractability, a customized and efficient genetic algorithm (GA) is developed to solve the studied MINLP problem, and lexicographic and weighted-sum strategies are further considered. An $\epsilon $ -constraint algorithm is also developed to analyze the Pareto frontiers. Comprehensive experiments are tested on a container handling benchmark system, and the results show the effectiveness of the proposed lexicographic GA, compared to results obtained with two commonly-used metaheuristics, a commercial MINLP solver, and two state-of-the-art methods., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Transport Engineering and Logistics

Subway lines connecting different urban functional zones in large cities have direction-dependent and time-variant passenger demand, namely, asymmetry in passenger demand. Most existing studies adopt a symmetric strategy to design operations in both directions and sequentially optimize the different problems associated with operations, thereby failing to meet the asymmetry in passenger demand. This study formulates an asymmetric operation strategy as an integrated mixed-integer non-linear model to optimize the entire operational process of rolling stock from the perspective of service quality and operating costs. Based on the proposed model, an exact algorithm is proposed with speed-up techniques to quickly generate an optimal solution. To this end, the original model is decomposed into several sub-problems that can be exactly solved by using a forward dynamic programming algorithm. Based on actual data from the Beijing subway's Yizhuang line, numerical experiments are conducted to investigate the effectiveness of the asymmetric operation strategy, to identify managerial insights on the integrated optimization, and to evaluate the performance of the proposed methodology.

During railway operations, unexpected events may influence normal traffic flows. This paper focuses on a train rescheduling problem for handling large disruptions, such as a rolling stock breakdown leading to a cancelled train service, where passenger reassignment strategies have to be considered. A novel mixed-integer linear programming formulation is established with consideration of train retiming, reordering, rerouting, and reservicing (addition of extra stops). The proposed mathematical formulation considers planning extra stops for non-canceled trains in order to transport the disrupted passengers, which were supposed to travel on the canceled train, to their pre-planned destination stations. Other constraints deal with limited seat capacity and track capacity, and mapping train rescheduling with passenger reassignment. A bi-objective function is optimized by a weighted-sum method to maximize the number of disrupted passengers reaching their destination stations and to minimize the weighted total train delay for all non-canceled trains at their destinations. A series of numerical experiments based on a part of the Beijing-Shanghai high-speed railway line is carried out to verify the effectiveness and efficiency of the proposed model and to perform a sensitivity analysis of various performance factors. The results show that an optimal reassignment plan of disrupted passengers is important to achieve real-time efficiency of traffic and re-ticketing. The impact of passenger reassignment on train rescheduling is influenced by the weights for objectives, duration of disruption, allowed additional dwell and running times, and relationship between passenger demand and total available train capacity.

During railway operations unexpected events may occur, influencing normal traffic flows. This paper focuses on a train rescheduling problem in a railway system with seat-reserved mechanism during large disruptions, such as a rolling stock breakdown leading to some canceled services, where passenger reassignment strategies have also to be considered. A novel mixed-integer linear programming formulation is established with consideration of train retiming, reordering, and reservicing. Based on a time–space modeling framework, a big-M approach is adopted to formulate the track occupancy and extra train stops. The formulation aims to maximize the passenger accessibility measured by the amount of the transported passengers subject to canceled services and to minimize the weighted total train delay for all trains at their destinations. The proposed mathematical formulation also considers planning extra stops for non-canceled trains to transport the disrupted passengers, which were supposed to travel on the canceled services, to their pre-planned destinations. Other constraints deal with seat capacity limitation, track capacity, and some robustness measures under uncertainty of disruption durations. We propose different approaches to compute advanced train dispatching decisions under a dynamic and stochastic optimization environment. A series of numerical experiments based on a part of ‘‘Beijing–Shanghai’’ high-speed railway line is carried out to verify the effectiveness and efficiency of the proposed model and methods.

This paper describes a new approach to develop a real-world automated scheduler applicable for Australian sugarcane industry. In Australia, the transport sector plays a critical role in raw sugarcane harvest and accounts for over 35% of the total cost of raw sugar production. The generation of an optimised schedule can bring the following practical benefits: eliminate bin supply delays to harvesters, minimise the number of locomotives/bins, reduce the locomotive shifts, control the sugarcane age/quality, etc. To generate such a scheduler, a new optimisation approach is developed based on job shop scheduling techniques using constraint programming and mixed integer programming. The proposed approach can produce solutions for small-scale and large-scale cases in agriculture/crops transport systems in a reasonable time. Mixed integer programming focuses on objective function using linear relaxation to prune suboptimal solutions, while constraint programming focuses on the model using filtering algorithms to eliminate infeasible candidate solutions. The applicability of the developed scheduler has been validated by a real-world case study for Kalamia Mill in Queensland, Australia. Following from the validation and discussion, it is concluded that the automated scheduler would be a valuable optimisation tool for transport modellers in Australian sugarcane industry.

The research activity of the PhD project in Veterinary Science aimed at determining the presence of environmental pollutants in aquatic organisms to improve the current knowledge on animal biomonitoring and provide the toxicological risk assessment connected, concerning the link between pollution and human and animal health. Many industrial pollutants, especially micropollutants, have adverse effects at very low concentrations. It is therefore important to early detect the presence of these compounds directly or through particular molecular biomarkers found in living organism. As a matter of fact, the search of these substances in the natural environment, is not always easy as they are often present below the detection threshold. In aquatic ecosystems, the use of invertebrate species seems to be an appropriate way of monitoring the environmental quality, due to their general capacity to accumulate pollutants, either from sediments and surrounding water or from food sources. The research activity wes carried out on the following aquatic organisms: • The common octopus (Octopus vulgaris) is a mollusc belonging to the class Cephalopoda. It is considered a cosmopolitan species as its range extends from the eastern Atlantic to the Mediterranean Sea and at least to Senegal in Africa. Octopus vulgaris constitutes an important marine resource suitable for human consumption; however, they can represent a source of chemical contaminants intake particularly of heavy metals. The aims of this study were to determine (i) the levels of heavy metals (Pb, Cd and Hg) in the muscle and digestive gland of octopus caught from two different locations along Campania coast, as well as (ii) to estimate their weekly human intake derived from the ingestion of octopus. The analysis of 38 samples showed a higher concentration of Pb in the muscle of octopus in one of the sampling sites. Differences were observed between the two tissue types, with a higher level of Cd and Pb observed in the diges

Reducing pollutant emissions and promoting sustainable mobility solutions, including Public Transport (PT), are increasingly becoming key objectives for policymakers worldwide. In this work we develop an optimal vehicle scheduling approach for next generation PT systems, considering the instance of mixed electric / hybrid fleet. Our objective is that of investigating to what extent electrification, coupled with optimal fleet management, can yield operational cost savings for PT operators. We propose a Mixed In- teger Linear Program (MILP) to address the problem of optimal scheduling of a mixed fleet of electric and hybrid / non-electric buses, coupled with an ad-hoc decomposition scheme aimed at enhancing the scalability of the proposed MILP. Two case studies arising from the PT network of the city of Luxem- bourg are employed in order to validate the model; sensitivity analysis to fleet design parameters is performed, specifically in terms of fleet size and fleet composition. Conclusions point to the fact that careful modelling and handling of mixed-fleet conditions are necessary to achieve operational savings, and that marginal savings gradually reduce as more conventional buses are replaced by their electric counterparts. We believe the methodology proposed may be a key part of advanced decision support systems for policymakers and operators that are dealing with the on-going transition from conventional bus fleets towards greener transport solutions.

Reducing pollutant emissions and promoting sustainable mobility solutions, including Public Transport (PT), are increasingly becoming key objectives for policymakers worldwide. In this work we develop an optimal vehicle scheduling approach for next generation PT systems, considering the instance of mixed electric / hybrid fleet. Our objective is that of investigating to what extent electrification, coupled with optimal fleet management, can yield operational cost savings for PT operators. We propose a Mixed In- teger Linear Program (MILP) to address the problem of optimal scheduling of a mixed fleet of electric and hybrid / non-electric buses, coupled with an ad-hoc decomposition scheme aimed at enhancing the scalability of the proposed MILP. Two case studies arising from the PT network of the city of Luxem- bourg are employed in order to validate the model; sensitivity analysis to fleet design parameters is performed, specifically in terms of fleet size and fleet composition. Conclusions point to the fact that careful modelling and handling of mixed-fleet conditions are necessary to achieve operational savings, and that marginal savings gradually reduce as more conventional buses are replaced by their electric counterparts. We believe the methodology proposed may be a key part of advanced decision support systems for policymakers and operators that are dealing with the on-going transition from conventional bus fleets towards greener transport solutions.

The research activity of the PhD project in Veterinary Science aimed at determining the presence of environmental pollutants in aquatic organisms to improve the current knowledge on animal biomonitoring and provide the toxicological risk assessment connected, concerning the link between pollution and human and animal health. Many industrial pollutants, especially micropollutants, have adverse effects at very low concentrations. It is therefore important to early detect the presence of these compounds directly or through particular molecular biomarkers found in living organism. As a matter of fact, the search of these substances in the natural environment, is not always easy as they are often present below the detection threshold. In aquatic ecosystems, the use of invertebrate species seems to be an appropriate way of monitoring the environmental quality, due to their general capacity to accumulate pollutants, either from sediments and surrounding water or from food sources. The research activity wes carried out on the following aquatic organisms: • The common octopus (Octopus vulgaris) is a mollusc belonging to the class Cephalopoda. It is considered a cosmopolitan species as its range extends from the eastern Atlantic to the Mediterranean Sea and at least to Senegal in Africa. Octopus vulgaris constitutes an important marine resource suitable for human consumption; however, they can represent a source of chemical contaminants intake particularly of heavy metals. The aims of this study were to determine (i) the levels of heavy metals (Pb, Cd and Hg) in the muscle and digestive gland of octopus caught from two different locations along Campania coast, as well as (ii) to estimate their weekly human intake derived from the ingestion of octopus. The analysis of 38 samples showed a higher concentration of Pb in the muscle of octopus in one of the sampling sites. Differences were observed between the two tissue types, with a higher level of Cd and Pb observed in the diges

Aviation or air transportation refers to the activities surrounding mechanical flights in the airlines and the aircraft industries. In this paper, we present a recent literature survey on aviation management. The literature review is classified into the following main categories: Airline Capacity Analysis; Air Traffic Flow Management; Airline Fleet Assignment; Tail Assignment with Aircraft Maintenance Routing; Airline Crew Pairing; Airline Recovery and Rescheduling; Airline Revenue Management; Collaborative Decision Making; Aircraft Scheduling. This classification aims to motivate the researchers and practitioners in aviation management to develop more applicable, realistic and wide-ranging optimization methodologies for meeting the current needs of aviation industry.

—Reducing pollutant emissions and promoting sustainable mobility solutions, including Public Transport, are increasingly becoming key objectives for policymakers worldwide. In order to jointly achieve these goals, careful consideration should be put on the operational cost and management of PT services, in order to promote the adoption of green mobility solutions and advanced management techniques by operators. In this work we develop a dynamic fleet management approach for next generation Public Transportation systems, considering the instance of mixed electric / hybrid fleet. Our objective is that of investigating to what extent electrification, coupled with optimal fleet management, can yield operational cost savings for PT operators, explicitly considering real-time disturbances, including delays, service disruptions etc. We propose a Mixed Integer Linear Program to address the problem of optimal scheduling of a mixed fleet of electric and hybrid / non-electric buses, and employ it as predictor in a Model Predictive Control approach. Test results based upon a real-life scenario showcase how the proposed approach is indeed capable of yielding a sizable reduction in operational costs, even when considerable disturbances arise from the underlying system.

—Reducing pollutant emissions and promoting sustainable mobility solutions, including Public Transport, are increasingly becoming key objectives for policymakers worldwide. In order to jointly achieve these goals, careful consideration should be put on the operational cost and management of PT services, in order to promote the adoption of green mobility solutions and advanced management techniques by operators. In this work we develop a dynamic fleet management approach for next generation Public Transportation systems, considering the instance of mixed electric / hybrid fleet. Our objective is that of investigating to what extent electrification, coupled with optimal fleet management, can yield operational cost savings for PT operators, explicitly considering real-time disturbances, including delays, service disruptions etc. We propose a Mixed Integer Linear Program to address the problem of optimal scheduling of a mixed fleet of electric and hybrid / non-electric buses, and employ it as predictor in a Model Predictive Control approach. Test results based upon a real-life scenario showcase how the proposed approach is indeed capable of yielding a sizable reduction in operational costs, even when considerable disturbances arise from the underlying system.

We consider the problem of optimally determining the sequence of electric and hybrid buses departing from a multi-line bus terminal, considering both service constraints (schedule adherence) and energy constraints (electric bus charging status, bus recharging scheduling in capacitated facilities). The problem is formulated as a Mixed Integer Linear Program, with the objective of minimizing the total operational cost for the bus lines in question. System dynamics are captured by practical sets of constraints, ranging from scheduling adherence to discharge- recharge dynamics. Individual operational costs at the bus level and at the trip level are fully parametrized, allowing for extensive sensitivity analysis. We investigate a real-life case study based on the city of Luxembourg, where two charging stations have been installed in the central station’s bus terminal. Experimental results show that, while full electrification is indeed possible, the required fleet size depends strongly on the given line(s) timetable.

We are a team of engineers working on a concrete project of Mobility in Luxembourg. We want to solve the problem of optimally determining the sequence of electric and hybrid electric buses, considering both service constraints (schedule adherence) and energy constraints (electric bus charging status, bus recharging scheduling in capacitated facilities) and at the same time ensure a high level of quality of service for the user satisfaction. The problem is formulated as a Mixed Integer Linear Program, with the objective of minimizing the total operational cost for the bus lines in question. System dynamics are captured by twenty sets of constraints, ranging from scheduling adherence to discharge-recharge dynamics. Individual operational costs at the bus level (cost of running an electric / non-electric bus per km, cost of recharging) and at the trip level (penalty due to failed schedule adherence) are fully parametrised, allowing for extensive sensitivity analysis. We investigate a real-life case study based in the city of Luxembourg, where the objective is to reach the all-electric mode for principal urban buses network. Through the model we investigate: the minimum amount of electric buses necessary to perform a day’s schedule for two currently partially electrified lines, without resorting to conventional internal combustion alternatives; the impact of electrifying two additional lines, specifically considering the trade-offs related to either adding new buses or new charging stations at the bus terminal. Finally, we studied how to best decompose the overall problem in several smaller problems, to be able to solve also realistic scenarios and using large real data sets from the Mobility Data owner of Luxembourg. We analysed and compared two kinds of decomposition: a bus line-based decomposition, and a time-based decomposition.

We consider the problem of optimally determining the sequence of electric and conventional internal combustion buses departing from a multi-line bus terminal, considering both service constraints (schedule adherence) and energy constraints (electric bus charging status, bus recharging scheduling in capacitated facilities). The problem is formulated as a Mixed Integer Linear Program, with the objective of minimizing the total operational cost for the bus lines in question. System dynamics are captured by twenty sets of constraints, ranging from scheduling adherence to discharge-recharge dynamics. Individual operational costs at the bus level (cost of running an electric / non electric bus per km, cost of recharging) and at the trip level (penalty due to failed schedule adherence) are fully parametrised, allowing for extensive sensitivity analysis. We investigate a real-life case study based in the city of Luxembourg, where two charging stations have been installed in the central station’s bus terminal. Through the model we investigate: i) the minimum amount of electric buses necessary to perform a day’s schedule for two currently partially electrified lines, without resorting to conventional internal combustion alternatives; ii) the impact of electrifying two additional lines, specifically considering the trade-offs related to either adding new buses or new charging stations at the bus terminal.

We consider the problem of optimally determining the sequence of electric and hybrid buses departing from a multi-line bus terminal, considering both service constraints (schedule adherence) and energy constraints (electric bus charging status, bus recharging scheduling in capacitated facilities). The problem is formulated as a Mixed Integer Linear Program, with the objective of minimizing the total operational cost for the bus lines in question. System dynamics are captured by practical sets of constraints, ranging from scheduling adherence to discharge- recharge dynamics. Individual operational costs at the bus level and at the trip level are fully parametrized, allowing for extensive sensitivity analysis. We investigate a real-life case study based on the city of Luxembourg, where two charging stations have been installed in the central station’s bus terminal. Experimental results show that, while full electrification is indeed possible, the required fleet size depends strongly on the given line(s) timetable.

We are a team of engineers working on a concrete project of Mobility in Luxembourg. We want to solve the problem of optimally determining the sequence of electric and hybrid electric buses, considering both service constraints (schedule adherence) and energy constraints (electric bus charging status, bus recharging scheduling in capacitated facilities) and at the same time ensure a high level of quality of service for the user satisfaction. The problem is formulated as a Mixed Integer Linear Program, with the objective of minimizing the total operational cost for the bus lines in question. System dynamics are captured by twenty sets of constraints, ranging from scheduling adherence to discharge-recharge dynamics. Individual operational costs at the bus level (cost of running an electric / non-electric bus per km, cost of recharging) and at the trip level (penalty due to failed schedule adherence) are fully parametrised, allowing for extensive sensitivity analysis. We investigate a real-life case study based in the city of Luxembourg, where the objective is to reach the all-electric mode for principal urban buses network. Through the model we investigate: the minimum amount of electric buses necessary to perform a day’s schedule for two currently partially electrified lines, without resorting to conventional internal combustion alternatives; the impact of electrifying two additional lines, specifically considering the trade-offs related to either adding new buses or new charging stations at the bus terminal. Finally, we studied how to best decompose the overall problem in several smaller problems, to be able to solve also realistic scenarios and using large real data sets from the Mobility Data owner of Luxembourg. We analysed and compared two kinds of decomposition: a bus line-based decomposition, and a time-based decomposition.

This work addresses a tactical railway traffic management problem focused on the opti-mization of train dispatching decisions and timing decisions related to short-term main-tenance works in a railway network subject to disturbed process times. This is modeled as a mixed-integer programming formulation in which the traffic flow and track mainten-ance variables, constraints and objectives are integrated under a stochastic environment. The resulting bi-objective optimization problem is to minimize the deviation from a sche-duled plan and to maximize the number of aggregated maintenance works. The two ob-jectives require to schedule competitive train operations versus maintenance works on the same infrastructure elements. Numerical experiments are performed on a realistic railway network. We measure the quality of the integrated solutions in terms of their robustness to random perturbations of the train travel times and of the maintenance works. Pareto op-timal methods are compared for the bi-objective problem. We also evaluate the impact of introducing routing stability constraints in order to force the trains to keep the same route among the different stochastic disturbed scenarios. The experiments show that forcing the routing stability reduces the routing flexibility and the ability to optimize the two perfor-mance indicators when dealing with stochastic disturbances of process times., Transport Engineering and Logistics

This paper addresses the real-time problem of coordinating aircraft ground and air operations in an airport area. At a congested airport, airborne decisions are related to take-off and landing operations, while ground (taxiway) decisions consist of scheduling aircraft movements between the gates and the runways. Since the runways are the initial/terminal points of both decisions, coordinated actions have a great potential to improve the overall performance. However, in the traffic control practice the different decisions are taken by different controllers, at least in large airports. Weak coordination may result in long queues at the runways, with increasing aircraft delays and energy consumption. This paper investigates models, methods and policies for improving the coordination between taxiway scheduling and airborne scheduling. The performance of a solution is measured in terms of delay and travel time, the latter being related to the energy consumption of an aircraft. A microscopic mathematical formulation is adopted to achieve reliable solutions. Exact and heuristic methods have been analysed in combination with the different policies, based on practical-size instances from Amsterdam Schiphol airport, in the Netherlands. Computational experience shows that good quality solutions can be found within limited time, compatible with real-time operations., Transport Engineering and Logistics

This work addresses a tactical railway traffic management problem focused on the opti-mization of train dispatching decisions and timing decisions related to short-term main-tenance works in a railway network subject to disturbed process times. This is modeled as a mixed-integer programming formulation in which the traffic flow and track mainten-ance variables, constraints and objectives are integrated under a stochastic environment. The resulting bi-objective optimization problem is to minimize the deviation from a sche-duled plan and to maximize the number of aggregated maintenance works. The two ob-jectives require to schedule competitive train operations versus maintenance works on the same infrastructure elements. Numerical experiments are performed on a realistic railway network. We measure the quality of the integrated solutions in terms of their robustness to random perturbations of the train travel times and of the maintenance works. Pareto op-timal methods are compared for the bi-objective problem. We also evaluate the impact of introducing routing stability constraints in order to force the trains to keep the same route among the different stochastic disturbed scenarios. The experiments show that forcing the routing stability reduces the routing flexibility and the ability to optimize the two perfor-mance indicators when dealing with stochastic disturbances of process times., Transport Engineering and Logistics

This paper addresses the real-time problem of coordinating aircraft ground and air operations in an airport area. At a congested airport, airborne decisions are related to take-off and landing operations, while ground (taxiway) decisions consist of scheduling aircraft movements between the gates and the runways. Since the runways are the initial/terminal points of both decisions, coordinated actions have a great potential to improve the overall performance. However, in the traffic control practice the different decisions are taken by different controllers, at least in large airports. Weak coordination may result in long queues at the runways, with increasing aircraft delays and energy consumption. This paper investigates models, methods and policies for improving the coordination between taxiway scheduling and airborne scheduling. The performance of a solution is measured in terms of delay and travel time, the latter being related to the energy consumption of an aircraft. A microscopic mathematical formulation is adopted to achieve reliable solutions. Exact and heuristic methods have been analysed in combination with the different policies, based on practical-size instances from Amsterdam Schiphol airport, in the Netherlands. Computational experience shows that good quality solutions can be found within limited time, compatible with real-time operations., Transport Engineering and Logistics

This paper describes an optimization framework for railway rolling stock rostering and maintenance scheduling. A key problem in railway rostering planning requires covering a given set of services and maintenance works with limited rolling stock units. The problem is solved via a two-step approach that combines the scheduling tasks related to train services, short-term maintenance operations and empty runs. A commercial MIP solver is used for the development of a real-time decision support tool. A campaign of experiments on real world scenarios from Trenitalia (Italian train operating company) illustrates the improvement achievable by the approach when compared to the practical solutions

This paper describes an optimization framework for railway rolling stock rostering and maintenance scheduling. A key problem in railway rostering planning requires covering a given set of services and maintenance works with limited rolling stock units. The problem is solved via a two-step approach that combines the scheduling tasks related to train services, short-term maintenance operations and empty runs. A commercial MIP solver is used for the development of a real-time decision support tool. A campaign of experiments on real world scenarios from Trenitalia (Italian train operating company) illustrates the improvement achievable by the approach when compared to the practical solutions

This work focuses on the stochastic evaluation of train schedules computed by a microscopic scheduler of railway operations based on deterministic information. The research question is to assess the degree of sensitivity of various rescheduling algorithms to variations in process times (running and dwell times). In fact, the objective of railway traffic management is to reduce delay propagation and to increase disturbance robustness of train schedules at a network scale. We present a quantitative study of traffic disturbances and their effects on the schedules computed by simple and advanced rescheduling algorithms. Computational results are based on a complex and densely occupied Dutch railway area; train delays are computed based on accepted statistical distributions, and dwell and running times of trains are subject to additional stochastic variations. From the results obtained on a real case study, an advanced branch and bound algorithm, on average, outperforms a First In First Out scheduling rule both in deterministic and stochastic traffic scenarios. However, the characteristic of the stochastic processes and the way a stochastic instance is handled turn out to have a serious impact on the scheduler performance. © 2013 Springer Science+Business Media New York.

Railway dispatchers reschedule trains in real-time in order to limit the propagation of disturbances and to regulate traffic in their respective dispatching areas by minimizing the deviation from the off-line timetable. However, the decisions taken in one area may influence the quality and even the feasibility of train schedules in the other areas. Regional control centers coordinate the dispatchers' work for multiple areas in order to regulate traffic at the global level and to avoid situations of global infeasibility. Differently from the dispatcher problem, the coordination activity of regional control centers is still underinvestigated, even if this activity is a key factor for effective traffic management. This paper studies the problem of coordinating several dispatchers with the objective of driving their behavior towards globally optimal solutions. With our model, a coordinator may impose constraints at the border of each dispatching area. Each dispatcher must then schedule trains in its area by producing a locally feasible solution compliant with the border constraints imposed by the coordinator. The problem faced by the coordinator is therefore a bilevel programming problem in which the variables controlled by the coordinator are the border constraints. We demonstrate that the coordinator problem can be solved to optimality with a branch and bound procedure. The coordination algorithm has been tested on a large real railway network in the Netherlands with busy traffic conditions. Our experimental results show that a proven optimal solution is frequently found for various network divisions within computation times compatible with real-time operations.

Railway dispatchers reschedule trains in real-time in order to limit the propagation of disturbances and to regulate traffic in their respective dispatching areas by minimizing the deviation from the off-line timetable. However, the decisions taken in one area may influence the quality and even the feasibility of train schedules in the other areas. Regional control centers coordinate the dispatchers' work for multiple areas in order to regulate traffic at the global level and to avoid situations of global infeasibility. Differently from the dispatcher problem, the coordination activity of regional control centers is still underinvestigated, even if this activity is a key factor for effective traffic management. This paper studies the problem of coordinating several dispatchers with the objective of driving their behavior towards globally optimal solutions. With our model, a coordinator may impose constraints at the border of each dispatching area. Each dispatcher must then schedule trains in its area by producing a locally feasible solution compliant with the border constraints imposed by the coordinator. The problem faced by the coordinator is therefore a bilevel programming problem in which the variables controlled by the coordinator are the border constraints. We demonstrate that the coordinator problem can be solved to optimality with a branch and bound procedure. The coordination algorithm has been tested on a large real railway network in the Netherlands with busy traffic conditions. Our experimental results show that a proven optimal solution is frequently found for various network divisions within computation times compatible with real-time operations.