Timeliness-oriented rush repair optimization of workforce scheduling and routing for logically complex systems under uncertainty

The optimization of workforce scheduling and routing problems (WSRPs) with a focus on timeliness-oriented rush repair in logically complex systems has received extensive attention. As there are many characteristics of complex system rush-repair, such as limited repair time and complex correlations, conventional approaches exhibit shortcomings in terms of timeliness (i.e., a comprehensive indicator that integrates both timely and mission reliability). To improve the overall emergency response efficiency under uncertain disaster scenarios, we develop a general decision-making framework for rush-repair WSRP. An optimization model is then formulated based on integer linear programming to maximize the overall timeliness while satisfying the skill-matching requirements and system-level recovery constraints. Furthermore, a novel matheuristic algorithm is proposed as a general solution approach for rush-repair decision-making. Finally, diverse benchmark groups, including different scales and sparsity levels, are simulated to conduct computational experiments. Compared to several representative algorithms, the results show that the proposed method has strong robustness and high efficiency.