A hybrid genetic tabu search algorithm for metro crew scheduling based on a space-time-state network - Xue, Feng; Liang, Peng; Yang, Ying; Wang, Jincheng (Applied soft computing, 2025)
The crew scheduling problem is highly important for the operation and management of urban rail transit. It is essential to reasonably design an approach for optimizing the crew schedule within the constraints of a provided train diagram so that the schedule is highly versatile and can meet the actual operational demand. Additionally, better results can be achieved by using an optimization method, which can reduce operating costs and satisfy crew members’ working preferences to the greatest extent possible to achieve a more rational distribution of tasks. Unlike traditional space-time networks that merely describe spatiotemporal movement trajectories, this study innovatively introduces state attributes to ensure solution feasibility during search. Using these attributes, we establish a space-time-state network for crew scheduling modeling. This model has the objective of reducing task connection time and personnel costs. To solve the provided model, a hybrid genetic tabu search (HGTS) algorithm is created by considering the distinctive characteristics of two methods: tabu search (TS) and genetic algorithm (GA), where TS handles local search and GA performs global optimization. The HGTS algorithm can efficiently address the complex metro crew scheduling problem and obtain an improved crew scheduling plan. The proposed method is validated against data from Chengdu Metro Line 5. Results demonstrate that our constructed methodology can effectively reduce the personnel costs and connection time of crew scheduling over the manual scheduling plan: a total of 148 crew duties were obtained, with an optimization rate of 10.30 % and a total connection time of 198 h 44 min 49 s, with an optimization rate of 7.71 %. Furthermore, the proposed method has a higher computational speed and enhanced stability than the shortest-path faster algorithm based on the greedy approach (G-SPFA) method, especially for large-scale data. Additionally, as a hybrid algorithm, HGTS delivers superior solutions compared to standalone GA and TS. This advantage is evidenced by key metrics: HGTS achieved a total duty duration of 725 h 31 min 51 s versus GA's 778 h 38 min 10 s and TS's 749 h 11 min 31 s, while also demonstrating tighter crew efficiency with standard deviations of 0.067, 0.077, and 0.085 for HGTS, GA, and TS respectively. •HGTS customized for urban rail crew scheduling with strict cumulative constraints.•Novel space-time-state network framework ensures global feasibility of crew duties, overcoming traditional model limits.•Benchmarking vs GA, TS, G-SPFA shows HGTS superiority in crew efficiency, schedule stability, & computational speed.•Method shows good optimization performance and fast computational speed under large-scale data.