Spatiotemporal 3D GIS-integrated rule-based scheduling for high-rise maintenance optimisation: a Malaysian case study

Purpose High-rise residential buildings face persistent maintenance inefficiencies due to reactive workflows, poor spatiotemporal integration, and suboptimal resource allocation. Current approaches inadequately address the complexities of vertical structures and dynamic maintenance requirements, creating critical gaps in proactive management. This study aims to bridge these gaps by developing and validating a 3D GIS-integrated rule-based scheduling model, enhancing proactive decision-making, resource optimisation, and regulatory compliance. Design/methodology/approach A 3D GIS-integrated rule-based scheduling model was developed, combining spatial visualisation, temporal analytics and Unified Modeling Language (UML) logic. A dataset of 4,070 maintenance work orders was collected from three high-rise residential buildings managed by a Malaysian university Asset Management Department (2020–2024). Data were digitised, modeled in SketchUp, integrated via ArcGIS Pro and PostgreSQL/PostGIS, and validated against manual scheduling. Findings The model reduced delays in high-priority tasks by 17%, improved compliance with task urgency by 23%, and achieved 100% technician utilisation. It also supported spatiotemporal fault visualisation, trend detection, and workload balancing. Lean maintenance principles and life cycle cost analysis were embedded, ensuring alignment with Malaysia’s Strata Management Act 2013. Practical implications The model offers facility managers, developers, and Management Corporation a scalable, data-driven tool for improving scheduling efficiency and regulatory compliance in high-rise maintenance operations. Originality/value This study uniquely integrates 3D Geographic Information Systems, temporal analytics, and rule-based logic into a unified scheduling framework. It moves beyond conventional systems by enabling dynamic prioritisation, proactive planning, and sustainability-focused maintenance. The model serves as a foundation for further integration with Internet of Things monitoring and smart city systems.