Preventive maintenance planning of railway infrastructure by reduced variable neighborhood programming
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Preventive maintenance planning of railway infrastructure by reduced variable neighborhood programming Souhir Elleuch1 · Bassem Jarboui2 · Nenad Mladenovic3 Received: 30 March 2020 / Accepted: 4 November 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Nowadays more and more complex railway systems can operate efficiently only if we have tools for planning their maintenance. Train accidents are mainly caused by infrastructure problems, or more specifically by track geometry failures.In this paper, we present a support decision system for forecasting the deterioration of track geometry. Two types of defects can be identified for each railway track segment. If a defect belongs to the first type, it must be repaired immediately; otherwise, the defect can be fixed after a specific time period. For resolving this problem, we first decompose the problem into two stages: prediction and classification. Both phases contain a learning phase and a testing phase. The solution technique for both stages are based on automatic programming field and its recently proposed heuristic, called variable neighborhood programming. Our new method is tested on real world problems. The results show that the proposed approach is a good and reliable tool for the preventive maintenance planning of the railway infrastructure. Keywords Railway infrastructure maintenance · Machine learning · Automatic programming · Variable neighborhood programming
1 Introduction Railway transportation is becoming more and more important due to the environmental reasons, e.g., CO2 emission, and low energy consumption requirements. To ensure that a railway system operates efficiently, the maintenance of the infrastructure must be
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Souhir Elleuch [email protected]
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Department of Management Systems and Production Management, College of Business and Economics, Qassim University, Buraidah, Saudi Arabia
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Higher colleges of technology, Abu Dhabi, United Arab Emirates
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Research Center on Digital Supply Chain and Operations Management, Department of Industrial and Systems Engineering, Khalifa University, Abu Dhabi, United Arab Emirates
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carefully performed. Over the last decades, the number of railway travel lines has been increased. As a result, optimization of the maintenance planning is getting more complex and more important activity [1]. Railroad companies run an inspection for a determined period and record the characteristics of found defects in rail tracks. These defects can be classified into two classes, red and yellow. The red class includes the defects which violate Federal Railroad Administration (FRA) standards and the yellow class includes the defects which meet FRA standards, but violate railroad’s own standards. If a defect belongs to the red type, it must be repaired immediately; otherwise, the defect can be fixed after a specific time period. Each defect is characterized by many features, where the most important is the amplitude value that gives information about the status of the defect
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