Scheduling preventive railway maintenance activities

A railway system needs a substantial amount of maintenance. To prevent unexpected breakdowns as much as possible, preventive maintenance is required. In this paper we discuss the Preventive Maintenance Scheduling Problem (PMSP), where (short) routine activities and (long) unique projects have to be scheduled in a certain period. To reduce costs and inconvenience for the travellers and operators, these activities have to be scheduled as much as possible together. We present a mathematical formulation for this problem and some greedy heuristics to solve it fast. Moreover, we compare the performance of these heuristics with the optimal solution using some randomly generated instances

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

On a novel optimisation model and solution method for tactical railway maintenance planning

Within the ACEM-Rail project of the European Seventh Framework Programme new measurement and inspection techniques for monitoring the track condition are developed. By means of these new techniques the prediction of future track condition will be highly improved. To our knowledge mid-term maintenance planning is done for projects and preventive tasks, but predictions of the track condition are not incorporated into the planning process up to now. To efficiently utilise this new kind of information one task within the ACEM-Rail project is the development of methods for planning predictive maintenance tasks along with preventive and corrective ones in a mid-term planning horizon. The scope of the mid-term or tactical maintenance planning is the selection and combination of tasks and the allocation of tasks to time intervals where they will be executed. Thereby a coarse maintenance plan is determined that defines which tasks are combined together to form greater tasks as well as the time intervals for executing the selected tasks. This tactical plan serves as the base for booking future track possessions and for scheduling the maintenance tasks in detail. In this paper an algorithmic approach is presented which is able to react on dynamic and uncertain changes due to any track prediction updating. To this end optimisation algorithms are implemented within a rolling planning process, so it is possible to respond to updated information on track condition by adapting the tactical plan. A novel optimisation method is developed to generate cost effective and robust solutions by looking ahead into the future and evaluating different solutions in several scenario

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

Framework for Railway Phase-based Planning

In the railway field, planning the maintenance and renewal strategy from Life Cycle Cost (LCC) perspective gets more and more attentions recent years. The new approach looks at all the costs through the infrastructure life span and use the annuity (continuing payment with a fixed total annual spending) to evaluate the project alternatives. The comparison result can identify the most cost-efficient solution in a long run and therefore reduce the overall costs. This article defines a phase-based framework to guide the railway maintenance and renewal project planning at strategic level. The framework evaluates the project options from a larger LCC scope: The costs from Train Operation Companies (TOCs) and passengers, together with the maintenance and renewal costs from Infrastructure Managers are included in the calculation. The framework simplifies the planning processes and the LCC calculation into 7 phases. By going through the phases, the project’s key evaluation indicators such as track quality and life time, the LCC annuity, Cash flow and Cumulated NPV curve over years, can be visualized into charts, so that the alternative proposals can be easily illustrated and compared. A case study is introduced in the article to demonstrate how the framework works to compare timber sleepers and concrete sleepers from strategic planning level. Two Life Cycle Cost oriented policies are discussed to illustrate: high quality track is necessity to improve the cost efficiency of railway maintenance and renewals

A Mathematical Model for Ballast Tamping Decision Making in Railway Tracks

Ballast tamping is considered as an important maintenance process for railway infrastructures and has a large influence on the capacity of any railway networks. But optimizing the plan of that process is a complex problem with a high cost. This paper discusses optimizing tamping operations on ballasted tracks to improve the track geometry and reduce the total maintenance cost. A mathematical model for this problem in the literature is improved here by including the restriction on the resources (tools, workers and budget) in the model and including constant/variable values for track possession cost and available resources. The optimal solutions obtained for all instances are found by using the global optimization. Besides, a numerical study is presented to test and evaluate the model performance. The results show that the proposed model can be adopted by the infrastructure manager (IM) to make suitable tamping scheduling decisions under normal or private conditions; however, the private conditions lead to an increase of the final cost compared to that of the normal ones. Doi: 10.28991/cej-2020-03091601 Full Text: PD

Optimal maintenance of multi-component systems: a review

In this article we give an overview of the literature on multi-component maintenance optimization. We focus on work appearing since the 1991 survey "A survey of maintenance models for multi-unit systems" by Cho and Parlar. This paper builds forth on the review article by Dekker et al. (1996), which focusses on economic dependence, and the survey of maintenance policies by Wang (2002), in which some group maintenance and some opportunistic maintenance policies are considered. Our classification scheme is primarily based on the dependence between components (stochastic, structural or economic). Next, we also classify the papers on the basis of the planning aspect (short-term vs long-term), the grouping of maintenance activities (either grouping preventive or corrective maintenance, or opportunistic grouping) and the optimization approach used (heuristic, policy classes or exact algorithms). Finally, we pay attention to the applications of the models.literature review;economic dependence;failure interaction;maintenance policies;grouping maintenance;multi-component systems;opportunistic maintenance;maintencance optimization;structural dependence

Genetic and memetic algorithms for scheduling railway maintenance activities

Nowadays railway companies are confronted with high infrastructure maintenance costs. Therefore good strategies are needed to carry out these maintenance activities in a most cost effective way. In this paper we solve the preventive maintenance scheduling problem (PMSP) using genetic algorithms, memetic algorithms and a two-phase heuristic based on opportunities. The aim of the PMSP is to schedule the (short) routine activities and (long) unique projects for one link in the rail network for a certain planning period such that the overall cost is minimized. To reduce costs and inconvenience for the travellers and operators, these maintenance works are clustered as much as possible in the same time period. The performance of the algorithms presented in this paper are compared with the performance of the methods from an earlier work, Budai et al. (2006), using some randomly generated instances.genetic algorithm;heuristics;opportunities;maintenance optimization;memetic algorithm

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

Comparison of deep learning and analytic image processing methods for autonomous inspection of railway bolts and clips

In this work, different methods are proposed and compared for autonomous inspection of railway bolts and clips. A prototype of an autonomous data acquisition system was developed to automatically obtain information of the state of the railway track using LiDAR and camera sensors. This system was employed in a testing railway track installed in the facilities of the University of Vigo to obtain the images used in this work. Then, the images were further processed using analytic image segmentation algorithms as well as a neural network to detect the bolts and clips. Once these elements are detected, their relative position is computed to evaluate if there is any missing component. Finally, the orientation of the clips is computed to ensure that all the bolts are correctly placed. Four different methods were implemented, and their performance was evaluated using the segmentations provided by the analytical methods and the neural network.Ministerio de Universidades | Ref. FPU21/01176Ministerio de Ciencia e Innovación | Ref. PLEC2021-007940Recovery, Transformation and Resilience Plan of the European Union – NextGenerationEU (University of Vigo) | Ref. 58550