Maintenance plan optimization for a train fleet

Doganay, Kivanc; Bohlin, Markus

doi:10.2495/cr100331

Cited by 21 publications

(17 citation statements)

References 14 publications

(26 reference statements)

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“…The main issues in railway maintenance are the lack of efficient and inexpensive technology for detecting problems and a lack of proper maintenance procedures [6]. Therefore, an effective railway maintenance strategy is needed to reduce overall costs while preserving the highest maintenance level [7].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Large-Scale Rail Transit Systems Based on an Analytic Hierarchy Process/Gradient-Based Cuckoo Search Algorithm (GBCS) Scheme

et al. 2020

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Condition monitoring is used as a tool for maintenance management and function as input to decision support. Thus the key parameters in preventing severe damage to railway assets can be determined by automatic real-time monitoring. The technique of radio-frequency identification (RFID) is increasingly applied for the automatic real-time monitoring and control of railway assets, which employs radio waves without the use of physical contact. In this work, a 243-km 2 area of Kuala Lumpur was selected. Because of its large size, determining the locations in which to install the RFID readers for monitoring the bogie components in the Kuala Lumpur railway system is a very complex task. The task involved three challenges: first, finding an optimal evolutionary method for railway network planning in order to deploy the RFID system in a large-area; second, identifying the large area that involved functional features; third, determining which station or stations should be given priority in applying the RFID system to achieve the most effective monitoring of the trains. The first challenge was solved by using a gradientbase cuckoo search algorithm for RFID system deployment. The second challenge was solved by determining all necessary information using geographic information system (GIS) resources. Because of the huge volume of data collected from GIS, it was found that the best method for eliminating data was to develop a new clustering model to separate the useful from the unuseful data and to identify the most suitable stations. Finally, the data set was reduced by developing a specific filter, and the information collected was tested by an analytic hierarchy process as a technique to determine the best stations for system monitoring and control. The results showed the success of the proposed method in solving the significant challenge of large-scale area conditions correlated with multi-objective RFID functions. The method provides high reliability in working with complex and dynamic data. Keywords Geographic information system (GIS) Á Analytic hierarchy process (AHP) Á Radio-frequency identification (RFID) Á Gradient-based cuckoo search algorithm (GBCS) Á Condition monitoring Á Bogie system Á Railway maintenance

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Section: Introductionmentioning

confidence: 99%

Monitoring Large-Scale Rail Transit Systems Based on an Analytic Hierarchy Process/Gradient-Based Cuckoo Search Algorithm (GBCS) Scheme

et al. 2020

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“…These additional blocks consist of railcars of types (1, 2, 3), (4,5,6) and so on. We also introduce enough single-car blocks to enforce the following priority order of railcar types in the first iteration of the WLBH: 1, 2, 3, .…”

Section: Propertymentioning

confidence: 99%

“…Because a block of consecutive railcars can be retrieved together, only the cost of the first railcar of the block is taken into account. Finally, we define the domains of the decision variables in (5) and (6).…”

Section: Formal Description Of the Rrtmentioning

confidence: 99%

“…Let N = 10, T = 2 (two types), the demand vector be (4,6), and the positioning of railcars at tracks be as shown in Figure 5. If LBH breaks ties by selecting a block with the lowest number of its first railcar, then the LBH constructs a solution with 6 blocks {−2, 3, ..., 6 − 8 − 10 − 12 − 14 − 16−}.…”

Section: The Largest Block Heuristicmentioning

confidence: 99%

“…We refer the interested reader to, e.g., Doganay and Bohlin [6] and Budai et al [3] and to the general reviews of Kobbacy and Murthy [15] and Lidén [16]. In the following, we set up the problem and analyze its complexity in Sections 2 and 3, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Shunting operations at flat yards: retrieving freight railcars from storage tracks

2017

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In this paper, we study the railcar retrieval problem (RRT) where specified numbers of certain types of railcars have to be withdrawn from the storage tracks of a flat yard. This task arises in the daily operations of workshop yards for railcar maintenance. The objective is to minimize the total cost of shunting via methods such as minimizing the usage of shunting engines.We describe the RRT formally, present a mixed-integer program formulation, and prove the general case to be NP-hard. For some special cases, exact algorithms with polynomial runtimes are proposed. We also analyze several intuitive heuristic solution approaches motivated by observed realworld planning routines. We evaluate their average performances in simulations with different scenarios and provide their worst-case performance guarantee. We show that although the analyzed heuristics result in much better solutions than the naive planning approach, they are still on average 30%-50% from the optimal objective value and may result in up to 14 times higher costs in the worst case. Therefore, we conclude that optimization should be implemented in practice in order to save valuable resources. Furthermore, we analyze the impacts of yard layout and the widespread organizational routine of presorting on the railcar retrieval cost.

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A Genetic Algorithm Approach for Scheduling Trains Maintenance Under Uncertainty

Lam

2019

Advanced Computational Methods for Knowledge Engineering

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This paper investigates the overhaul maintenance scheduling problem in which the maintenance duration is uncertain at the time of planning. This problem involves specifying the dates of trains' arrival at the maintenance centre while taking into consideration the due windows, the desired number of trains in service, and the capacity of the maintenance centre. The cycle time of each type of trains is random with a known probability distribution. The objective is to minimise a weighted sum of two components: (i) the deviation of the assigned arrival dates from the due windows and (ii) the penalty for violating the resources' constraints. A combined genetic algorithm with sample average approximation solution approach is developed to solve this problem. The solution approach consists of a genetic algorithm for global search and an exact method to determine the arrival dates of train-sets when a sequence of train-sets is known. The results with data provided by one of the leading Australian maintenance center show that the proposed method can produce good solution within acceptable computation time.

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Maintenance plan optimization for a train fleet

Cited by 21 publications

References 14 publications

Monitoring Large-Scale Rail Transit Systems Based on an Analytic Hierarchy Process/Gradient-Based Cuckoo Search Algorithm (GBCS) Scheme

Monitoring Large-Scale Rail Transit Systems Based on an Analytic Hierarchy Process/Gradient-Based Cuckoo Search Algorithm (GBCS) Scheme

Shunting operations at flat yards: retrieving freight railcars from storage tracks

A Genetic Algorithm Approach for Scheduling Trains Maintenance Under Uncertainty

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