Unified Crew Planning and Replanning Optimization in Multi-Line Metro Systems Considering Workforce Heterogeneity

Abstract--Metro crew planning is a key component of smart city development as it directly impacts the operational efficiency and service reliability of public transportation. With the rapid expansion of metro networks, effective multi-line scheduling and emergency management have become essential for large-scale seamless operations. However, current research focuses primarily on individual metro lines, with insufficient attention on cross-line coordination and rapid replanning during disruptions. Here, a unified optimization framework is presented for multi-line metro crew planning and replanning with heterogeneous workforce. Specifically, a hierarchical time-space network model is proposed to represent the unified crew action space, and computationally efficient constraints and formulations are derived for the crew's heterogeneous qualifications and preferences. Solution algorithms based on column generation and shortest path adjustment are further developed, utilizing the proposed network model. Experiments with real data from Shanghai and Beijing Metro demonstrate that the proposed methods outperform benchmark heuristics in both cost reduction and task completion, and achieve notable efficiency gains by incorporating cross-line operations, particularly for urgent tasks during disruptions. This work highlights the role of global optimization and cross-line coordination in multi-line metro system operations, providing insights into the efficient and reliable functioning of public transportation in smart cities. Metro systems are vital to urban transportation, offering high efficiency and large capacity to meet growing mobility demands. Within the context of metro operations, labor costs account for a significant share of expenses [1]. Consequently, metro crew planning plays a crucial factor in achieving smooth, cost-effective operations. As metro systems continue to expand rapidly, the need for optimized crew planning approaches has become increasingly critical to realize efficient and intelligent metro operations that support the broader goals of smart city development [2]. Existing research on metro crew planning primarily focuses on single-line operations [3], [4], [5], [6], [7], [8].