# Multi-Objective Real-Time Planning of Evacuation Routes for Underground Mine Fires
## 6 Conclusions  
This study investigates the planning of emergency escape routes for underground personnel in the event of an exogenous fire in an underground mine. It considers real-time \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Respond** & **Priorities** & **Targets** & **Paths** & **Temporary Escape Time/s** & **Equal Distance/m** & **Time in Smoke/s** \\ \hline \multirow{4}{*}{300 s} & \multirow{2}{*}{1} & \multirow{2}{*}{Second shaft} & [446, 472, 467, 466, 385, 409, 422, 421, 389, 416, 415, 414, 382, 381, 32, 441] & 927 & 2395 & 379 \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._disaster development, personnel location, the protective capacity of self-rescuers, and the location of underground refuge chambers. Using alarm sensor data and [PERSON]'s algorithm, we predicted the arrival time of smoke flow at each underground location. We then established a bi-objective optimization model that aims to minimize both the shortest equivalent length of the emergency escape route and the time that personnel are affected by smoke flow, thereby ensuring the efficiency and safety of the escape routes. A dynamic programming-based labeling algorithm is employed to solve the model, facilitating the identification of optimal routes across all emergency escape options. This is achieved through the inclusion of two labeled elements in the model and the application of a stricter dominance rule. For multiple feasible emergency escape routes that consist of various exits and Pareto-optimal sets for the same exit, a path preference strategy is implemented. This strategy prioritizes surface exits and minimizes temporary escape time in accordance with safety requirements. Ultimately, the route with the shortest path length and highest safety is selected from all feasible options. This approach avoids the influence of decision makers' risk preferences, which can occur with traditional weighting methods. However, the prediction of fire development in this study is based on the premise that the fire development is in a stable stage and there is no airflow reversal in the ventilation system. How to integrate all the sensor data from underground to predict the trend of the disaster and the change in the ventilation system will be the key point to be solved in the future. Moreover, the reliability of sensor data, which this study assumes to be consistently accurate, presents another limitation. In real-world scenarios, sensor failures, inaccuracies, or delays in data transmission could significantly impact the model's effectiveness. Future work should address these issues by incorporating sensor reliability factors into the model, as well as developing strategies to mitigate the effects of potential data inaccuracies. In our future work, we will investigate how to predict fire development and changes in the state of the ventilation system based on sensor data at the time of the fire and to provide a more accurate assessment of the risk at various locations underground during a fire. We hope that our work will provide a new perspective to other scholars in the area of evacuation of people from exogenous fires in underground mines. Conceptualization, L.B. ; methodology, L.B. and D.Z. ; software, L.B. ; validation, L.B., Y.L. and D.Z. ; formal analysis, L.B., Y.L., D.Z. and L.W. ; investigation, L.B., Y.L., D.Z. and L.W. ; resources, L.B. and D.Z. ; data curation, L.B. ; writing--original draft preparation, L.B. and Y.L. ; writing--review and editing, L.B., Y.L., D.Z. and L.W. ; visualization, L.B., Y.L. and L.W. ; supervision, D.Z. ; project administration, L.B. ; funding acquisition, L.B. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Research and Development Program of China, grant number 2023 YFC2907305, and the State Key Laboratory for Fine Exploration and Intelligent Development of Coal Resources, CUMT, grant number SKLCRSM24 KF003. Not applicable.