Multi-objective model for optimizing railway infrastructure asset renewal

Trabalho inspirado num problema real da empresa Infraestruturas de Portugal, EP.A multi-objective model for managing railway infrastructure asset renewal is presented. The model aims to optimize three objectives, while respecting operational constraints: levelling investment throughout multiple years, minimizing total cost and minimizing work start postponements. Its output is an optimized intervention schedule. The model is based on a case study from a Portuguese infrastructure management company, which specified the objectives and constraints, and reflects management practice on railway infrastructure. The results show that investment levelling greatly influences the other objectives and that total cost fluctuations may range from insignificant to important, depending on the condition of the infrastructure. The results structure is argued to be general and suggests a practical methodology for analysing trade-offs and selecting a solution for implementation.info:eu-repo/semantics/publishedVersio

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

Large scale railway renewal planning with a multiobjective modeling approach

Conferência realizada na Filândia, Helsinquia, de 20-24 de agosto de 2018A multiobjective modeling approach for managing large scale railway infrastructure asset renewal is presented. An optimized intervention project schedule is obtained considering operational constraints in a three objectives model: evenly spreading investment throughout multiple years, minimizing total cost, minimizing work start postponements on higher priority railway sections. The MILP model was based on a real world case study; the objectives and constraints specified by an infrastructure management company. Results show that investment spreading greatly influences the other objectives and that total cost fluctuations depend on the overall condition of the railway infrastructure. The model can produce exact efficient solutions in reasonable time, even for very large-sized instances (a test network of similar size to the USA railway network, the largest in the world). The modeling approach is therefore a very useful, practical methodology, for generating optimized solutions and analyzing trade-offs among objectives, easing the task of ultimately selecting a solution and produce the works schedule for field implementation.info:eu-repo/semantics/publishedVersio

Overhaul Resource Planning for Rolling Stock Using MIP Models

In terms of maintenance, parts must be maintained to satisfy operating conditions. Although, maintenance is costly and unprofitable, it is indispensable. Thus, reducing maintenance costs without reducing maintenance is one of the critical issues. Since maintenance costs mainly come from resources, they should be properly managed to minimize the cost. Hence, the goal of this paper is to find the optimal number of resources for required maintenance activities. Two mixed-integer programming models are developed. The first model is used for a long-term plan to find a proper number of resources while the second one generates a maintenance schedule for a shorter time frame to verify feasibility of the plan

Discrete Event Simulation and Optimization Approaches for the Predictive Maintenance of Railway Infrastructure

This thesis is carried out within the PhD Course in Logistics and Transport at CIELI - Italian Centre of Excellence on Logistics, Transport and Infrastructures, University of Genoa. In this work, a discrete event simulation and optimization model is created to schedule the predictive maintenance activities. Nowadays, after a severe decrease of transport demand during the pandemic period, rail public transport is resuming a central role for both freight and passenger transport. To cope with this increase in demand, to maintain high safety standards and to avoid unnecessary costs, the idea is to switch to predictive maintenance strategy, intervening before an asset failure and when it has reached a certain state of degradation. The degradation and asset future conditions are predicted according to probabilistic models and maintenance deadlines are defined by applying a risk based approach. The problem is first formulated as a MILP (Mixed Integer Linear Programming) optimization problem and then transformed into a simulation-based optimization problem using the ExtendSim software. Different simulative models are created to take into account the stochastic nature of some variables in real processes. After the formal description of the models, some real-world applications are presented. Finally, considerations on the proposed approach are reported highlighting limits and challenges in predictive maintenance planning, such as lack of data and the stochastic and complex environment

Compliance of maintenance and operational needs for trains: a simulation analysis to evaluate the impact of a flexible scheduling on local transport by rail

If not properly managed neither planned on real needs, the maintenance of rolling stock may strongly affect rail operations in local public transport, risking to compromise the quality of service or generating an over sizing of the fleet. Therefore, an effective coordination is required between the Operation and Maintenance departments. Some flexibility in maintenance activities – i.e., preventive and on condition maintenance policies - has already been applied for some years in the regional rail transport with successful results; however, it has not been introduced yet in rail public transport, where a corrective maintenance is generally adopted. In this work, the proper scheduling of more flexible maintenance activities in the rail public transport context is addressed through the use of discrete event simulation. Real data sets provided by the Italian GTT-Gruppo Torinese Trasporti company have been used to test the proposed approach and to carry out a multi-scenarios campaign, aiming at analyzing the effectiveness of the maintenance process when certain operating conditions change or unexpected events occur. Some improvement proposals have also been analyzed with the proposed simulation method

New heuristics for the stochastic tactical railway maintenance problem

Efficient methods have been proposed in the literature for the management of a set of railway maintenance operations. However, these methods consider maintenance operations as deterministic and known a priori. In the stochastic tactical railway maintenance problem (STRMP), maintenance operations are not known in advance. In fact, since future track conditions can only be predicted, maintenance operations become stochastic. STRMP is based on a rolling horizon. For each month of the rolling horizon, an adaptive plan must be addressed. Each adaptive plan becomes deterministic, since it consists of a particular subproblem of the whole STRMP. Nevertheless, an exact resolution of each plan along the rolling horizon would be too time-consuming. Therefore, a heuristic approach that can provide efficient solutions within a reasonable computational time is required. Although STRMP has already been introduced in the literature, little work has been done in terms of solution methods and computational results. The main contributions of this paper include new methodology developments, a linear model for the deterministic subproblem, three efficient heuristics for the fast and effective resolution of each deterministic subproblem, and extensive computational results