Emergency Scheduling Research Articles

Research on the water-ground multimodal transport emergency scheduling model and decision-making method considering the actual road network inundation situation.

As urbanization continues to accelerate worldwide, urban flooding is becoming increasingly destructive, making it important to improve emergency scheduling capabilities. Compared to other scheduling problems, the urban flood emergency rescue scheduling problem is more complicated. Considering the impact of a disaster on the road network passability, a single type of vehicle cannot complete all rescue tasks. A reasonable combination of multiple vehicle types for cooperative rescue can improve the efficiency of rescue tasks. This study focuses on the urban flood emergency rescue scheduling problem considering the actual road network inundation situation. First, the progress and shortcomings of related research are analyzed. Then, a four-level emergency transportation network based on the collaborative water-ground multimodal transport transshipment mode is established. It is shown that the transshipment points have random locations and quantities according to the actual inundation situation. Subsequently, an interactive model based on hierarchical optimization is constructed considering the travel length, travel time, and waiting time as hierarchical optimization objectives. Next, an improved A* algorithm based on the quantity of specific extension nodes is proposed, and a scheduling scheme decision-making algorithm is proposed based on the improved A* and greedy algorithms. Finally, the proposed decision-making algorithm is applied in a practical example for solving and comparative analysis, and the results show that the improved A* algorithm is faster and more accurate. The results also verify the effectiveness of the scheduling model and decision-making algorithm. Finally, a scheduling scheme with the shortest travel time for the proposed emergency scheduling problem is obtained.

Resource-Constrained Emergency Scheduling for Forest Fires via Artificial Bee Colony and Variable Neighborhood Search Combined Algorithm

Resource-Constrained Emergency Scheduling for Forest Fires via Artificial Bee Colony and Variable Neighborhood Search Combined Algorithm

Emergency scheduling based on event triggering and multi-hierarchical planning for space surveillance network

Space Surveillance Network (SSN) task scheduling plays a crucial role in maintaining the catalog of Resident Space Objects (RSO). However, various emergencies, such as RSO maneuvering, collisions, or rocket launch, may disrupt the original scheduled scheme. Therefore, it is essential to rapidly regenerating emergency schemes while minimizing disturbance to the initial scheduling scheme. This paper introduces an Emergency Task Scheduling model, referred to as MM-ETS, which aims to Maximize observation profits and Minimize disturbance. This model incorporates constraints related to observability, tasks, and resources, which are derived from practical applications. Additionally, a Hierarchical Distributed Dynamic Emergency Scheduling algorithm, encompassing Task assignment, Conflict resolution, Resource negotiation, and Center collaboration (HD-TCRC-DES), is proposed. The presented algorithm is activated by emergencies and a Rolling Horizon Strategy (RHS) is employed to break down long-term, large-scale problems into short-term, small-scale problems, thus improving feasibility and emergency response capabilities. At each layer of the proposed algorithm, heuristic rules are utilized to solve the new scheme, which helps allocate the computational load to resource nodes, and quickly adjust the initial scheduling scheme. Experimental scenarios involving 15 ground observation resources and 1000 emergency tasks are constructed, and the simulation results demonstrate that the proposed method can enhance Comprehensive Benefits Indicators (CBI) by approximately 24.65%, 24.7%, 24.91%, and 30.5% compared to the baselines. Consequently, it is suitable for SSN emergency task scheduling.

Retracted: Emergency Scheduling Optimization Simulation of Cloud Computing Platform Network Public Resources

Retracted: Emergency Scheduling Optimization Simulation of Cloud Computing Platform Network Public Resources

Modeling and simulation of complex emergency dispatch based on BIPSO

In emergency task scheduling, this study proposes a complex model for emergency scheduling. It is based on the particle swarm algorithm and improves upon the traditional version. Additionally, the study recommends the use of the binary particle swarm optimization algorithm (PSO). The study proposes applying the multi-objective task scheduling-particle swarm optimization algorithm (MOTS-PSO) to the complex emergency scheduling model by combining it with the multi-objective function. Compared to other algorithms, the proposed improved algorithm exhibited the lowest average number of iterations, which consistently fell within the range of 130, and achieved a 100% success rate for optimization searches on the majority of functions. When compared with other models, the proposed research model demonstrated superior performance, exhibiting the lowest total scheduling cost, total execution time, and data transfer time of 280 and 900, respectively, for the task quantity of 5000. Furthermore, the proposed model exhibited the lowest maximum execution cost for a single node, which remained within the range of 0.45S. The outcomes of the experiments demonstrate that the proposed research model adequately satisfies the requirements for complex scheduling and its simulability has been confirmed.

Research on emergency scheduling based on improved genetic algorithm in harvester failure scenarios.

In response to the issue of harvesting machine failures affecting crop harvesting timing, this study develops an emergency scheduling model and proposes a hybrid optimization algorithm that combines a genetic algorithm and an ant colony algorithm. By enhancing the genetic algorithm's crossover and mutation methods and incorporating the ant colony algorithm, the proposed algorithm can prevent local optima, thus minimizing disruptions to the overall scheduling plan. Field data from Deyang, Sichuan Province, were utilized, and simulations on various harvesting machines experiencing random faults were conducted. Results indicated that the improved genetic algorithm reduced the optimal comprehensive scheduling cost during random fault occurrences by 47.49%, 19.60%, and 32.45% compared to the basic genetic algorithm and by 34.70%, 14.80%, and 24.40% compared to the ant colony algorithm. The improved algorithm showcases robust global optimization capabilities, high stability, and rapid convergence, offering effective emergency scheduling solutions in case of harvesting machine failures. Furthermore, a visual management system for agricultural machinery scheduling was developed to provide software support for optimizing agricultural machinery scheduling.

Integrated Emergency Vehicle Scheduling Model in Urban Emergency Rescue within a Time-Limited Period

Considering the uncertainty of urban emergency rescue time, it is difficult for emergency vehicles to maintain a 100% service level continuously. The biggest challenge is to minimize driving distance and costs when the number of vehicles available for deployment is uncertain. In this study, considering the limitation of emergency vehicles, a new integrated emergency vehicle scheduling model for large-scale urban emergency rescue was developed, which is able to tackle multiple emergency tasks at the same time, achieving the shortest path and the lowest cost. Then, the emergency scheduling was simulated in seven urban areas of H City in China to verify the applicability of the proposed model, during which a fast non-dominated sorting multi-objective genetic algorithm with elite strategy was used to solve this model. Moreover, the corresponding algorithm test was conducted to verify the feasibility of the Pareto solution and demonstrate its effectiveness. Moreover, the simulation results show that the used algorithm can reduce computing time by 14.27%, compared with the genetic algorithm. Finally, the optimal scheduling scheme was selected according to the decision-makers’ preferences. This study solved various emergency vehicle scheduling problems under uncertain emergency times, which was beneficial for multilateral cooperation emergency scheduling and provided decision support for emergency command departments for choosing the best emergency scheme.

Uncertainty analysis on flood routing of embankment dam breach due to overtopping failure

The breach risk of reservoir dam exists objectively while exerting benefits. There is great uncertainty in the overtopping breach process of embankment dam and the dam break flood routing which affects the accuracy of dam risk analysis and may cause unnecessary waste in emergency scheduling. However, the uncertainty analysis of the breach process and its consequence is currently inadequate. Therefore, a stratified sampling Monte Carlo method is proposed to simulate uncertain overtopping breach flood of embankment dams. The main sources of uncertainty are analyzed and determined as uncertain dam breach and flood routing processes. The uncertain breach process of dam is studied by presenting a sensitive study between 3 mechanism breach models and 5 parametric breach models. The random dam breach process is restored using HEC-RAS semi-empirical breach model by estimating breach characteristics through multiple common breach models. The random flood routing is carried out through 1D–2D coupled unsteady flow analysis in which the random breach process is adopted as an upper inflow boundary condition. According to the case study results, though parameters have been controlled in a limited range, the flood routing results in the early stage of dam overtopping failure present greater uncertainty. As the flood progresses further downstream, the uncertainty will gradually decrease. This study could serve as a reference for dam breach risk map making.

Enhanced Method for Emergency Scheduling of Natural Gas Pipeline Networks Based on Heuristic Optimization

Safety and disturbance issues in system engineering have garnered substantial attention. This study focuses on the analysis of the distinct characteristics of emergency dispatch problems in Natural Gas Pipeline Networks (NGPS). Graph theory serves as a tool to transform the NGPS topology and establish an optimization model for NGPS emergency dispatch. The model also integrates user weights, satisfaction, and reduction factors into the user modeling approach. Its objective is to maximize overall system satisfaction while considering factors such as demand-side requirements and operational constraints. To solve this optimization model, the Particle Swarm Optimization (PSO) method is employed. An in-depth exploration of four unique disturbance scenarios provides solid evidence of the effectiveness and practicality of the PSO method. Compared to other methods, the PSO method consistently boosts overall user satisfaction and aligns more fluidly with the real-time demands of emergency scheduling, regardless of reduced supply capacity, complete supply interruptions, sudden surges in user demand, or pipeline connection failures. The developed emergency scheduling optimization method presents two key advantages. Firstly, it proficiently mitigates potential losses stemming from decreased supply capacity at local or regional levels. By adeptly adjusting natural gas supply strategies, it minimizes economic and production losses while ensuring a steady supply to critical users. Secondly, the method is superior at swiftly reducing the affected area and managing the increased demand for natural gas, thus maintaining NGPS stability. This research underscores the importance of considering user characteristics and demands during emergencies and demonstrates the effectiveness of employing the PSO method to navigate emergency scheduling challenges. By strengthening the resilience of the pipeline network and ensuring a sustainable natural gas supply, this study constitutes a significant contribution to energy security, economic development, and the promotion of clean energy utilization, ultimately propelling the achievement of sustainable development goals.

Scheduling Optimization of Mobile Emergency Vehicles Considering Dual Uncertainties

Compared with the traditional operation mode of emergency vehicles, the mobile emergency vehicle is regarded as a new type of emergency facility carrier with the features of variable locations, flexible mobility, and intelligent decision-making. It can provide an effective solution to reasonably respond to the uncertain risks of sudden disasters. Focusing on meeting the maximum demand for materials and services in disaster areas, this paper proposes a scheduling model of mobile emergency vehicles with dual uncertainty of path and demand. The model, solved by an integer-coding hybrid genetic algorithm, aims to obtain minimum mobile emergency scheduling cost and time by transforming the multi-objective problem into a single-objective problem. The “5.12” Wenchuan earthquake is used as an example to validate the model and solving method. The results show that the model can reduce the impact of uncertain risks and improve the scientific logic of emergency strategies and deployments based on the actual crisis scenario. It benefits from introducing mobile emergency vehicles and optimizing their scheduling process.

Research on a Scheduling Model for Social Emergency Resource Sharing Based on Emergency Contribution Index

A large number of massive repair machines are urgently necessary for a post-disaster rescue. These machines also need to be operated by professionals, and the demands require the participation of different industries in the whole society since they cannot be met via the national emergency resource storage system. Therefore, the support of extensive emergency resources from different industries across the entire society is needed in the rescue process, that is, social emergency resource sharing. To achieve this sharing, an emergency resource scheduling model should have the ability to allocate resources from the whole society. However, traditional emergency scheduling models have not considered the suppliers’ willingness to take part in the scheduling activities and their abilities to supply the resources. To solve the above issues, this paper designs a scheduling model for social emergency resource sharing based on an emergency contribution index (SSERS). The emergency contribution index (ECI) can be used to find the enterprises that not only have the ability to provide efficient emergency resources on time but also have the willingness to participate in emergency rescue. The results show that our model effectively optimizes the basic models to some extent and achieves social emergency resource sharing.

An improved estimation of distribution algorithm for rescue task emergency scheduling considering stochastic deterioration of the injured

Efficient allocating and scheduling emergency rescue tasks are a primary issue for emergency management. This paper considers emergency scheduling of rescue tasks under stochastic deterioration of the injured. First, a mathematical model is established to minimize the average mathematical expectation of all tasks’ completion time and casualty loss. Second, an improved multi-objective estimation of distribution algorithm (IMEDA) is proposed to solve this problem. In the IMDEA, an effective initialization strategy is designed for obtaining a superior population. Then, three statistical models are constructed, which include two tasks existing in the same rescue team, the probability of first task being processed by a rescue team, and the adjacency between two tasks. Afterward, an improved sampling method based on referenced sequence is employed to efficiently generate offspring population. Three multi-objective local search methods are presented to improve the exploitation in promising areas around elite individuals. Furthermore, the parameter calibration and effectiveness of components of IMEDA are tested through experiments. Finally, the comprehensive comparison with state-of-the-art multi-objective algorithms demonstrates that IMEDA is a high-performing approach for the considered problem.

Research on task assignment algorithm of heterogeneous aircraft cooperative cluster in dynamic scene

With the aim of minimizing task completion time and reducing resource consumption, this paper investigates the dynamic task allocation problem of a heterogeneous aircraft cooperative cluster with multiple fire spots in the forest. Firstly, we establish the fire point propagation model and task assignment model to enhance task assignment accuracy. Based on this, we propose the Chaotic Cosine Fusing T-Distribution Variation and Quasi-Reflection Learning Grey Wolf Optimization algorithm (CTQGWO), which integrates distributed mutation and quasi-reflective learning. By incorporating chaos population initialization of the Tent map, adaptive control parameter adjustment strategy, distributed mutation, and quasi-reflective learning strategy, we can enhance the convergence speed and accuracy of the algorithm. Finally, taking into account the complexity of the forest and the instability of the aircraft, we develop a task reassignment algorithm that can quickly allocate and reduce the number of communications. Through experiments, our algorithm enables rapid decision-making for forest fire emergency scheduling, under the condition of limited forest fire resources, effectively reducing the total rescue time and minimizing the consumption of rescue resources.

Distributed algorithm without iterations for an integrated energy system

Existing energy management methods for integrated energy systems are mostly in distributed communication and computation now, need a large number of iterations, and each time of iteration needs lots of communication and computation. For this reason, on one hand, the iteration may cause energy-delay. On the other hand, iteration will significantly increase the communication and computation burden. The integrated energy systems contain a variety of devices and energy resources (including renewable energy resources), so the communication and computation burden is already very high. If the communication and computation cannot be solved very well, the cost functions of each device need to be much easier to ensure the operation of the system and their systematic error will be much larger. For this reason, the result of optimization will be much worse because of the accuracy of cost functions. The greatest challenge of this issue is to establish an algorithm without iteration. For handling this issue, first, we adopt the theoretical demonstration to prove that if all prices of all devices are the same, the optimization will be realized and the instantaneous price is the one-order derivative. (we assume the relationship between the operating cost and the energy flow of each device as the convex cost functions.) Second, we reshape all cost functions. Third, we change the function to the total of the foregoing functions in the directed annular path and adopt the total function of the hole system to solve the energy price. Last, we use the price to ensure their operating condition. Our theoretical demonstration has already proved the optimization, convergence, the plug and play performance, scalability, and the emergency scheduling performance of the annular partial differential algorithm (APDA).

Dynamic Scheduling Algorithm Based on Evolutionary Reinforcement Learning for Sudden Contaminant Events Under Uncertain Environment

For sudden drinking water pollution event, reasonable opening or closing valves and hydrants in a water distribution network (WDN), which ensures the isolation and discharge of contaminant as soon as possible, is considered as an effective emergency measure. In this paper, we propose an emergency scheduling algorithm based on evolutionary reinforcement learning (ERL), which can train a good scheduling policy by the combination of the evolutionary computation (EC) and reinforcement learning (RL). Then, the optimal scheduling policy can guide the operation of valves and hydrants in real time based on sensor information, and protect people from the risk of contaminated water. Experiments verify our algorithm can achieve good results and effectively reduce the impact of pollution events.

Proliferation of Novel Synthetic Opioids in Postmortem Investigations After Core-Structure Scheduling for Fentanyl-Related Substances.

New generations of novel synthetic opioids (NSOs) have emerged to fill a void in the illicit drug markets left by the decline in popularity of fentanyl analogs subsequent to core-structure scheduling of fentanyl-related substances in the United States and China. These new opioids include members of the 2-benzyl benzimidazole (eg, isotonitazene, metonitazene, N -pyrrolidino etonitazene, protonitazene, etodesnitazene), benzimidazolone (eg, brorphine), and cinnamylpiperazine (eg, AP-238, 2-methyl AP-237) subclasses. Novel synthetic opioids continue to be detected in opioid-related fatal overdoses, demonstrating the harms associated with exposure to these drugs. Between January 2020 and December 2021, 384 casework blood samples were reported by our laboratory to contain 1 or more of the prior listed 8 NSOs. Isotonitazene (n = 144), metonitazene (n = 122), and brorphine (n = 91) were the 3 most prevalent substances, with positivity for isotonitazene and brorphine peaking just before the announcement of emergency scheduling. These NSOs have been documented as significant drivers of drug mortality, and this case series described here highlights the challenges medical examiners and coroners face in staying current with emerging drugs. Challenges include regional differences, rapid turnover, short lifecycles, variable toxicology testing, and difficulty in assessing individual drug toxicity in polydrug cases.

Multi-objective scheduling of priority-based rescue vehicles to extinguish forest fires using a multi-objective discrete gravitational search algorithm

The implementation of emergency scheduling of priority-based rescue vehicles for extinguishing forest fires is a complex optimization problem facing with many challenges and difficulties to optimize the operational costs and to improve the efficiency to make robust decisions. The main challenges are to minimize the number of fire engines while minimizing firefighting time and firefighting delay time, simultaneously. The main difficulties to make these decisions are to consider the severity of each fire point with regards to the limited resources of vehicles. Hence, this paper motivates to develop a new multi-objective scheduling model for extinguishing the fire of forests considering rescue priority with the limited rescue resources. To make an efficient and robust solution for the proposed problem, another novelty is to propose a hybrid optimization algorithm which is a modified discrete gravitational search algorithm. To confirm the applicability of the proposed problem, an actual forest fire emergency scheduling in Heilongjiang Province, China, is simulated. The proposed hybrid optimization algorithm is tested to check the feasibility the Pareto solutions and it is compared with a set of well-known and recent algorithms to show its efficiency. Finally, after a comprehensive discussion, the results prove that this work provides an accurate and effective tool for performing the emergency scheduling for extinguishing the forest fire.

Emergency resources scheduling in distribution system: From cyber-physical-social system perspective

With the frequently happened aging electrical grid and extreme weather events, outage management and service restoration face the challenges of flexible scheduling multiple resources for different kinds of objectives. Previous research mainly focuses on the single restoration objective or single emergency resource management. This paper proposes a new emergency scheduling framework under the cyber-physical-social system (CPSS) perspective, which includes the scheduling of multiple components from CPSS, i.e., crew, mobile distributed generators, and resources. Under the framework, a novel co-scheduling model with the goal of minimizing time-consuming, cost, and shedding load are also built as a mixed-integer non-linear optimization model. Then we formulate this problem as advanced multiple salesmen traveling problem with a routing stage and a scheduling stage, and design a solution algorithm based on the genetic algorithm to solve this problem. In the solving algorithm, the optimal routing solutions are got at first, then the emergency scheduling solutions are optimized in the second stage based on the routing results. Finally, a numerical case study on the IEEE 123-bus network shows the effectiveness of the co-optimization process, which can provide a routing solution for each depot, and the corresponding optimization objective results will increase 25.75% and 73.92% when compared with time-consuming optimization and load-shedding optimization.

Research on Performance Optimization Method of UAV Communication Link in Emergency Communication Environment

Aiming at the problem of long average delay of traditional UAV emergency communication, a performance optimization method of UAV communication link based on emergency communication environment is proposed. The target tasks in the emergency communication channel are divided into different target task groups. According to the distance between the target tasks in the target task group, the symmetric matrix is constructed, all elements in the symmetric matrix are classified, and the classification results are calculated by ant colony algorithm to construct the emergency communication task scheduling model of UAV. Combined with the emergency communication task scheduling model and the demand, priority, time window, and communication ability of UAV emergency scheduling task, the time delay control of UAV emergency communication is realized with the goal of maximizing the efficiency of UAV task execution, maximizing the completion rate of UAV task execution and minimizing the risk of UAV delay. The experimental results show that the average delay of the three methods also increases. When the system load is 50%, the average delay of common methods is 23 ms, the average delay of traditional methods is 76 ms, the average delay of the method proposed in this paper is 26 ms, and the common average delay is the shortest. When the system load is 99%, the common methods cannot realize emergency communication scheduling due to system load overshoot, and the corresponding average delay cannot be considered, indicating that the common methods are unstable. The average delay of the method proposed in this paper is stable under different system loads and always remains below 40 ms. It is proved that compared with the traditional methods, the method proposed in this paper takes less time for UAV emergency communication and has less average delay under different system loads, so it has better application value.

Multi-objective deep reinforcement learning for emergency scheduling in a water distribution network

Multi-objective deep reinforcement learning for emergency scheduling in a water distribution network