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Aug 12, 2020
Optimum Infrastructure Maintenance Scheduling Model for Urban Rail Transit Considering Possession Conflict Resolution
Authors: Gehui Liu [email protected], Shaokuan Chen [email protected], Xinchen Ran [email protected], Hua Jin [email protected], and Qianfei Chen [email protected]Author Affiliations
Publication: CICTP 2020
ABSTRACT
The infrastructure systems of urban rail transit is critically important for the safety and stability of train operations. Efficient maintenance on systems requires appropriate allocation of possession times and resources within a limited duration and complicated constraints. An integer programming model is proposed for long-term maintenance scheduling on multi-major systems. Maintenance activities are classified into routine tasks and projects due to their execution cycle, possession duration, and influence range. A possession duration indicator is introduced as constraint for the resolution of the time and space possession conflicts. The proposed model aims to minimize the total maintenance cost and penalty cost by arranging maintenance activities on each segment in discrete time periods. The clustered maintenance is applied for the reduction of cost. The results from case study verify the practicality and availability of the proposed model, which is able to provide decision support for long-term maintenance scheduling in urban rail transit.
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© 2020 American Society of Civil Engineers.
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Published online: Aug 12, 2020
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1MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong Univ., Beijing 100044, China. Email: [email protected]
2MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong Univ., Beijing 100044, China. Email: [email protected]
3MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong Univ., Beijing 100044, China. Email: [email protected]
4MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong Univ., Beijing 100044, China. Email: [email protected]
5MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong Univ., Beijing 100044, China. Email: [email protected]
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