An AI-Based Planning and Control Platform for Public Transport Maintenance

Maintenance planning and control refers to the coordinated activities for determining maintenance needs, scheduling maintenance work, and ensuring that maintenance execution aligns with operational requirements. In public transport sectors, maintenance planning and control is critical for efficient operations, yet their maintenance workshops still use traditional computerized maintenance management software and lack advanced decision support tools. In this regards, this paper proposes an artificial intelligence based maintenance planning and control (AI-MPC) platform that aims to intelligently optimize maintenance workflows in maintenance workshops. The proposed platform considers a holistic approach and integrates maintenance planning with maintenance control in a unified system. As a systematic methodology, focus group workshop with industry and academic experts is conducted to gather the requirements and an expert view on the development of the platform. The focus group was organized around four key dimensions i.e., practicality and usability, integration and adaptability, functional and structural feasibility, and framework completeness. The expert feedbacks were properly analyzed and based on that a comprehensive multi-layer platform architecture is developed that highlight the important aspects of integration, intelligence, and user interface, etc., as suggested by the experts. According to our knowledge, there is no such solution available and that, our proposed AI-MPC is a novel comprehensive maintenance platform that can enable real-time, data-driven maintenance planning and control, reducing unplanned downtime and improving resource utilization in maintenance facilities.