Risk-Based Optimal Scheduling for the Predictive Maintenance of Railway Infrastructure

In this thesis a risk-based decision support system to schedule the predictive maintenance activities, is proposed. The model deals with the maintenance planning of a railway infrastructure in which the due-dates are defined via failure risk analysis.The novelty of the approach consists of the risk concept introduction in railway maintenance scheduling, according to ISO 55000 guidelines, thus implying that the maintenance priorities are based on asset criticality, determined taking into account the relevant failure probability, related to asset degradation conditions, and the consequent damages

A Modal Choice Model for Evaluating the Impact of Increasing Automation in Container Terminals

The aim of this paper is to define a model for the modal choice between road and rail transport taking into account the increase of rail attractiveness resulting from the increasing of the number of container terminals equipped with automated handling systems. The considered automated handling system is the automated multilevel handling system developed within the RCMS EU project, that is, a multistory storage building, equipped with electric AGVs, remote controlled elevators and remote controlled ceiling cranes. This automated system makes possible to access to a specific container without the necessity of reshuffling and to load/unload containers to/from trucks and trains directly under the storage structure, allowing a significant reduction of the loading/unloading time. In order to define the modal choice model, the systematic utility and the perceived utility are provided and the flows of freight delivered via rail or via road are determined with a binomial Logit model. Moreover, the threshold distance between seaport and inland terminals beyond which automation has a significant impact on modal split is evaluated. As a case study, a European port hinterland network is considered and some scenarios are analyzed, assuming that an increasing number of terminals introduces automation. The paper shows that the introduction of automation in container terminals has significant consequences on modal split. In particular, as the number of automated terminals increases, the rail mode becomes more competitive and the threshold distance between seaport and inland terminals, at which the modal split is equally distributed between road and rail modes, significantly decreases

A Rolling-Horizon Approach for Predictive Maintenance Planning to Reduce the Risk of Rail Service Disruptions

This article proposes a model for the risk-based scheduling of predictive maintenance activities on a railway line to intervene when a track segment has reached a certain state of degradation, thus preventing faults and possible failures. With the aim of taking into account the stochastic nature of real environments, the rail-track degradation process is represented as a stochastic process, and the failure probability is evaluated as the probability of reaching a degradation threshold. Moreover, a rolling-horizon framework is introduced to manage newly available real-time information and unpredicted faults or maintenance activity delays. Whereas the traditional scheduling models are offline models that cover the long-term horizon but neglect operational disturbances, the presented model allows for dynamic day-to-day planning and adaptation of the maintenance plan to real-time information, thereby responding to the increasing understanding of real-world processes. The optimization problem on maintenance scheduling is formulated as a mixed-integer linear programming problem based on risk minimization, in adherence to ISO 55 000 guidelines. Finally, the application of the approach to a real rail network is reported and discussed, with a focus on the planning of tamping activities at the operational level

Dynamic Cost Model to Evaluate the Impact of Increasing Automation in Container Terminals on Transport Chain Cost

Nowadays many intermodal terminals are moving towards more automated solutions in order to improve the efficiency of freight transport chain. These automation solutions concern: yard management, quay scheduling, land-side loading/unloading, stowage planning and sequencing, automated recognition systems, etc.. Hence, the automation of intermodal terminals has effects/impacts on the inland transportation costs. Usually, the models developed in freight transport cost research are static (Feo-Valero et al. (2011), Liu et al. (2009), Liu et al. (2012)) and not consider the variation of costs over time. In this regard, Ferrari (2014) has made an important contribution. He introduced a dynamic model based on a dynamic cost function. This function considers the variation over time of costs due to technological and organizational changes in transport modes. Nevertheless, the models identified in literature review analyze transport cost on a particular inland corridor, whereas the variation of transport costs at a port hinterland network level seems to be neglected. Conca et al. (2016) introduces a dynamic model to evaluate the modal shift (road/rail) in a port hinterland network considering the level of automation of intermodal nodes. Therefore, starting from the assumptions of Conca et al. (2016), this paper has the objective to define a dynamic transport cost model for road and rail that considers the role of automation developments at a port hinterland network level

Stochastic scheduling approach for predictive risk-based railway maintenance

This paper presents a stochastic model for scheduling predictive and risk-based maintenance activities in rail sector. The novelty of the approach consists of the risk concept introduction in railway maintenance scheduling, thus implying that the maintenance priorities are based on criticality of assets, determined by the relevant failure probability, related to asset degradation conditions, and by the consequent direct and indirect damages. This approach belongs to the framework of 'predictive maintenance' which aims at intervening when an asset has reached a certain degradation state, being the future track conditions forecasted by appropriate models. In particular, this work explicitly considers the stochastic nature of risk and of the real-world maintenance operations, introducing stochastic deadlines. In doing so, it is worth noting that, the adaptive rescheduling models only partially solve this issue, since they consider deterministic sub-problems of the overall problem and they cannot vary continuously the stochastic input variables. Therefore, to cope with this problem, in this paper, the risk-based maintenance planning problem is formulated in term of stochastic programming. After providing a formal methodology description, some experimental results are reported and some indications about its future developments are given

A modular model to schedule predictive railway maintenance operations

This paper presents a modular model for the optimal railway maintenance scheduling problem. In particular, an innovative approach to predictive railway maintenance scheduling is applied to track maintenance, also taking into account the risk assessment, according to the ISO 55000 guidelines, and the real-time track conditions. The novelty of this approach consists of the introduction of the concept of risk in railway maintenance scheduling, thus implying that the maintenance activity priorities are based on asset criticalities, such as track degradation conditions and repair costs, and the users' unmet demand due to traffic disturbances caused by asset faults. In the paper, after a general framework description, the relevant literature is analyzed. Then, the formal problem description is given, and some experimental results are discussed, together with some indications about the future model developments

Design of collaborative multimodal strategies for disruption management in urban railway systems

In order to increase the attractiveness and the resilience of multimodal transport systems, each transport mode should be exploited according to its peculiar characteristics in an integrated way with the other modes. In this regard, it is possible to imagine a \u201csynchromodal framework\u201d relying on a \u201crail backbone\u201d, where the synchronization of the different modes is driven by rail transport. On the other hand, the limited number of path alternatives that characterizes the rail network makes it vulnerable to service disruptions and may result in a significant Level of Service loss. In this context, this paper proposes a methodology for urban public service providers that allows to design a multimodal service able to react, in a quasi-real-time framework, to unexpected events by offering multimodal transport solution to users

On applying artificial intelligence techniques to maximise passengers comfort and infrastructure reliability in urban railway systems

Railway infrastructure reliability and availability are key to guarantee an efficient rail service. In particular, track maintenance has a significant impact on the ride quality and train speed, since the reduction of speed limits is imposed in case of poor track condition. This implies consequence for the passengers comfort and delays of travel times. In this context, this research is aimed at developing an integrated model for the identification of track defects and the optimal ordering of maintenance interventions that can reduce operational costs for the infrastructure manager, improving capacity, efficiency as well as passengers’ satisfaction. The application of the proposed approach to a real urban railway line is described, showing its effectiveness

Risk-based optimal scheduling of maintenance activities in a railway network

In a railway system, maintenance activities need to be continuously performed to ensure safety and continued rail operations. In this framework, while on one hand unplanned corrective maintenance activities performed when a fault is occurred are expensive and would cause low service quality, on the other hand preventive maintenance that does not consider the actual asset condition is often unnecessary and turns out to generate avoidable costs. To deal with this issue, in this paper, a risk-based decision support system to schedule the predictive maintenance activities is proposed. In such a framework, the interventions are planned by taking into account the forecast degradation state of railway assets and performed when a given threshold is reached, thus minimizing the probability of both sudden and unnecessary operations. With the end of finding the optimal scheduling of predictive maintenance, in this paper also the space-distributed aspect of railway infrastructure is considered, defining the best path and the activities assignment for each maintenance team. The scheduling model is formulated as a Mixed Integer Linear Programming (MILP) problem aiming at based on the risk minimization, according to the ISO 55000 guidelines. A matheuristic solution approach is proposed and applied to a real rail network. The relevant results show how the proposed scheduling model can use the outputs of predictive tools and degradation models, based on data from field, to mitigate the sudden failure risk by means of a costeffective maintenance plan at a network level