Operational efficiency management of infrastructures: a task-driven framework based on time-varying networks

Purpose In the accelerating process of global urbanization, the operational efficiency of infrastructure has emerged as an important indicator of urban modernization. Existing research on operational efficiency management of infrastructure (OEMI) mainly focuses on outcome-oriented efficiency evaluation, neglecting the driving role of operational tasks, and thus failing to understand the causes of efficiency changes. This study aims to develop a task-driven framework for evaluating and analyzing operational efficiency from the perspective of operational task scheduling. Design/methodology/approach Firstly, a static socio-technical network of infrastructure (STNI) is constructed based on socio-technical network theory. Secondly, the dynamic changes of STNI are tracked through the time-varying network approach and task-information-node mapping relationship. Finally, the operational efficiency at both node and network levels is evaluated by combining load and cost indicators. The framework is applied to an urban wastewater engineering (UWE) in Nanjing, China. Findings During the case UWE operation, the node-level operational efficiency shows a polarized distributed. The network-level operational efficiency of the UWE can be characterized differently in different scenarios. For example, there is a “working day cycle” in the administrative scenario and a “two-stage decay” in the external shock scenario. The results highlight the heavy burden of administrative work on workers and the potential dilemma of node overload during emergency response work. Practical implications The framework is able to capture the dynamics of task-driven STNI operational efficiency, bridging the OEMI effort from a task scheduling perspective, especially where real-time adjustments and rapid decision-making are critical. Originality/value The framework effectively captures the dynamics of task-driven STNI operational efficiency, bridging the OEMI effort from a task scheduling perspective, especially where real-time adjustments and rapid decision-making are crucial.