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

No abstract

No abstract

Network design, in general is a critical concept due to its effect on efficiency, cost and other significant factors. In recent years, RFID is widely applied for RNP Network design. The large -area network design process requires a significant number of interrogating antennas based on the reader-tag range communication. RFID technology uses a huge number of tags communicate with a small number of the reader, from thus point of view, the challenges of large scale RNP problems include high computational cost due to the time consumption of RFID readers placement error, as a result, these challenges reduce the effectiveness of the RFID system In this study, a model of multi-objective function for RFID reader placement was conducted on various large -area condition to evaluate the impact of network design expansion. A comparative analysis was performed with (GBCS algorithm) Gradient-Based Cuckoo Search. The dataset was performed for the area of 80m x 80m and 150m x 150m. Simulation results exhibited that the performance of (GBCS) Gradient-Based Cuckoo presented a minimum number of deployed readers with maximum RFID tags coverage and was superior in solving large scale RFID-NP problems. Simulation results not only explained that the present algorithm is strong and workable but also showed excellent approximation abilities even in the high scale-area. Consequently, the authors recommend if the area bigger than (80m2), there is a need to divide the area into separate regions and deal with each region separately to be sure that the objective function can work efficiently.