Emergency Materials Distribution Research Articles

A new two-stage emergency material distribution framework for urban rainstorm and flood disasters to promote the SDGs

Under the coupled effects of climate change and urbanization, urban rainstorm and flood has become more frequent and intense. The effect of the flood disasters emergency response has a direct impact on the success or failure of the rescue. Emergency material distribution is one of the most critical steps in the emergency response phase and is crucial to achieve the Sustainable Development Goals (SDGs). This study proposes a two-stage emergency material distribution framework for urban rainstorm and flood disasters. The entropy weight method and technique for order preference by similarity to ideal solution (EWM-TOPSIS) was adopted to incorporate the difference in relief-demand urgency into the emergency material distribution principle. The mathematical model was established by minimizing emergency travel time, inequity and social costs (logistic costs and deprivation costs) as objective functions. The gamultiobj algorithm was used to solve the model. The proposed framework is further applied in Xi'an city of China. The results showed that the affected sites were categorized into disaster risk levels I, II and III. The Pareto solution set was obtained by optimizing each conflicting objective. The emergency material distribution plan was selected from the Pareto solution set based on the actual situation. In the plan, the flood disasters emergency travel time totaled 6.48 h, the inequity totaled 3.69, and the total social costs reached 226,584 CNY. The proposed framework is more consistent with the needs of the actual rescue by comparing the four models generated plans. Decision-makers should not only consider minimizing dispatch time and social costs, but also the trade-off between equity and prioritization of distribution. This study helps to provide new ideas for sustainable urban development and emergency management. Urban rainstorm and flood disasters change with the changing environment dynamically, and it is difficult to collect dynamic disaster information on the affected sites. Thus our further study will focus on improving the prediction accuracy of dynamic urban rainstorm and flood disasters.

Research on Multi-UAV Task Assignment Based on a Multi-Objective, Improved Brainstorming Optimization Algorithm

In response to the practice of rescue channel blocking and a shortage of emergency materials in the event of sudden significant disasters, a multi-UAV collaborative distribution scheme was designed based on the demand for rapid and accurate distribution of materials. This paper constructed a multi-UAV collaborative task assignment and routing problem with simultaneous delivery and pick-up and time windows (MVTARPSDPTW), considering the factors of UAV load, energy consumption, cargo quality, and volume to minimize the total cost of UAV distribution and the full penalty of the task, as well as optimizing the balance of UAV efficiency. This paper proposes a multi-objective, improved brainstorming optimization algorithm based on Pareto dominance (MIBSO) to solve the MVTARPSDPTW problem. With DTLZ4, DTLZ5, and DTLZ6 benchmarks, this work tests the algorithm performance according to the characteristic attributes of the model sought, selecting the four indicators of GD, the Spacing metric, HV, and IGD, concerning convergence, solution distribution, and comprehensive performance. Case validation is based on a COVID-19 scenario in Changchun, China, and the results show that the model algorithm designed in this paper has good performance and feasibility in convergence and distribution of reconciliation. Finally, the multi-UAV emergency material distribution solution provides practical, theoretical support for rescue tasks in sudden significant disasters.

Optimisation of evacuation and emergency materials distribution under natural disaster

Optimisation of evacuation and emergency materials distribution under natural disaster

Robust Bi-Level Optimization for Maritime Emergency Materials Distribution in Uncertain Decision-Making Environments

Maritime emergency materials distribution is a key aspect of maritime emergency responses. To effectively deal with the challenges brought by the uncertainty of the maritime transport environment, the multi-agent joint decision-making location-routing problem of maritime emergency materials distribution (MEMD-LRP) under an uncertain decision-making environment is studied. First, two robust bi-level optimization models of MEMD-LRP are constructed based on the effect of the uncertainty of the ship’s sailing time and demand of emergency materials at the accident point, respectively, on the premise of considering the rescue time window and priority of emergency materials distribution. Secondly, with the help of robust optimization theory and duality theory, the robust optimization models are transformed into robust equivalent models that are easy to solve. Finally, a hybrid algorithm based on the ant colony and tabu search (ACO-TS) algorithm solves multiple sets of numerical cases based on the case design of the Bohai Sea area, and analyzes the influence of uncertain parameters on the decision making of MEMD-LRP. The study of MEMD-LRP under uncertain decision-making environments using bi-level programming and robust optimization methods can help decision makers at different levels of the maritime emergency logistics system formulate emergency material reserve locations and emergency material distribution schemes that can effectively deal with the uncertainty in maritime emergencies.

A Drone Scheduling Method for Emergency Power Material Transportation Based on Deep Reinforcement Learning Optimized PSO Algorithm

Stable material transportation is essential for quickly restoring the power system following a disaster. Drone-based material transportation can bypass ground transportation’s limitations and reduce transit times. However, the current drone flight trajectory distribution optimization model cannot meet the need for mountainous emergency relief material distribution following a disaster. A power emergency material distribution model with priority conditions is proposed in this paper, along with a two-layer dynamic task-solving framework that takes task dynamics into account. This research proposes an algorithm (TD3PSO) that combines the particle swarm algorithm (PSO) updating technique with the double-delay depth deterministic policy gradient algorithm (TD3) algorithm’s capacity to dynamically parameterize. The final task allocation experiment demonstrates that the modified TD3PSO significantly outperforms the conventional algorithm on the Solomon data set, with an improvement of 26.3% on average over the RLPSO algorithm and a 11.0% reduction in the volatility of the solving impact. When solving under realistic circumstances, the solution effect increases by 1.6% to 13.4%, and the redistribution experiment confirms the framework’s efficacy. As a result, the algorithm and architecture suggested in this paper may successfully address the issue of scheduling drones for power emergencies while enhancing transportation efficiency.

Optimal path planning and data simulation of emergency material distribution based on improved neural network algorithm.

Today, data storage technology is also gradually improving. Various industries can store massive amounts of data for analysis. The global climate change and the bad ecology led to frequent occurrence of natural disasters. Therefore, it is necessary to establish an effective emergency materials distribution system. The neural network model is used to calculate and the optimal emergency distribution route is analyzed according to the historical information and the data. Considering backpropagation, this paper further disposing a method to further improve the calculation of neural network algorithm. From the perspective of structural parameters of neural network algorithms, this paper uses genetic algorithms to construct predictions, and combines the actual purpose of material distribution after disasters. Considering the capacity constraints of distribution centers, time constraints, material needs of disaster relief points and different means of transportation, a dual-objective path planning with multiple distribution centers and multiple disaster relief points with the shortest overall delivery time and lowest overall delivery cost is constructed. By establishing an emergency material distribution system, it can maximize the prompt and accurate delivery after a natural disaster occurs, and solves the urgent needs of the people.

Research on emergency material demand based on urgency and satisfaction under public health emergencies.

In recent years, the frequent occurrence of public health emergencies has had a significant impact on people's life. The study of emergency logistics has also attracted scholars' attention. Therefore, matching emergency materials' supply and demand quickly, which meets urgency and satisfaction, is the purpose of this paper. This paper used the Metabolism Grey Model (1,1) (GM (1,1)) and the material demand prediction model to predict the number of infections and material demand. Besides, we established a bi-objective optimization model by constructing a profit and loss matrix and a comprehensive utility perception matrix. The results show that the method is helpful in matching the optimal supply and demand decision quickly on the basis of meeting urgency and satisfaction. The method is helpful in improving the fairness of emergency material distribution, which could better protect people's livelihoods.

Model of multi-period emergency material allocation for large-scale sudden natural disasters in humanitarian logistics: Efficiency, effectiveness and equity

Large-scale sudden natural disasters will lead to a large number of casualties and economic losses. The allocation of emergency materials is of vital significance for saving lives and property losses and alleviating human suffering after disasters. Different from the traditional allocation model with single or double optimization objectives in one period, we focus on the objectives of three dimensions in humanitarian logistics: efficiency, effectiveness and equity. Finally, an emergency material allocation model with multi-rescue sites, multi-affected sites and multi-periods is proposed. The model allows decision makers to make trade-offs among efficiency, effectiveness and equity. Numerical experiments and real cases show that the model can adapt to large-scale emergency material distribution. The solution efficiency is high, and the optimal solution can be obtained in a few minutes. This satisfies the timeliness of emergency rescue distribution and provides decision makers with a quick distribution plan.

Optimization of emergency supplies paths based on dynamic real-time split deliver

A multi-objective dynamic demand split delivery emergency material distribution model is developed to enhance the efficiency of emergency material distribution and facilitate the smooth progress of safety rescue operations during unconventional emergencies. This model incorporates the psychological view of those affected by disasters. The issue of dynamic demand may be transformed into a static demand problem by dividing the distribution time window into time domains of equal length. The optimization process is thereafter executed in real-time with the timed batch methodology. A refined ant colony method has been developed to address the model by integrating the attributes of the mathematical model, followed by doing an arithmetic case analysis. The findings indicate that the algorithm and mathematical model suggested in this study are efficacious in addressing the emergency material distribution issue, offering valuable decision-making advice and reference.

Integrated Design of Emergency Medical and Material Distribution Networks During the Epidemic Outbreak Period

During the epidemic outbreak period, the number of infected people increased explosively. In order to effectively contain the further spread of the epidemic and treat infected people, it is necessary to make reasonable planning for the treatment of infected people and the distribution of materials. In this paper, a multi-objective mixed-integer programming model for the integrated design of emergency medical and material distribution networks is developed, in which the location of medical points, the allocation of infected people, and the distribution of medical materials are addressed in the multi-stage planning. Then, the multi-objective model is solved by the augmented ε-constraint method. A realistic case study on Huangpu District of Shanghai in China is conducted to demonstrate the validity of the developed model. The results show that the integrated consideration of the emergency medical network and the material distribution network is more capable of reducing the total transportation distance and decreasing the total operating cost.

Optimization of Evacuation and Emergency Materials Distribution under Natural Disaster

Optimization of Evacuation and Emergency Materials Distribution under Natural Disaster

Optimization of regional emergency supplies distribution vehicle route with dynamic real-time demand.

Given the particular characteristics of a sudden outbreak of an epidemic on a regional scale and considering the possible existence of a latent period process, this paper takes the distribution of regional emergency supplies as the research object. Form the proposes a dynamic vehicle path problem from the perspective of real-time demand changes. First, when there is a sudden outbreak of a small-scale epidemic, there is uncertainty about demand in the epidemic area. The objective functions of minimizing the vehicle travel route cost of emergency vehicles, the late arrival penalty cost of emergency vehicles, and the fixed cost of emergency vehicles, as well as the objective function of minimizing the total distance traveled by vehicles, are established. Second, a mathematical model of the dynamic real-time demand vehicle route problem is built using the actual vehicle routing problem as a basis. The model is then solved using the SFSSA method. Finally, the computational results demonstrate that the SFSSA algorithm can effectively reduce transportation cost and distance when solving the constructed mathematical model problem, providing a solution to the problem of optimizing the route of emergency material distribution vehicles for a regional scale.

Bibliometric analysis and system review of vehicle routing optimization for emergency material distribution

Bibliometric analysis and system review of vehicle routing optimization for emergency material distribution

An Optimization Method for Distributing Emergency Materials Which Balances Multiple Decision Criteria

In emergency rescue operations, it is very important for emergency management to arrange emergency materials scientifically and reasonably according to emergency needs. However, the decision criteria for emergency material distribution are often diverse, coupled with the lack of information acquisition due to the suddenness and development complexity of disasters, which makes the scientific decisions about emergency material distribution face great challenges. This paper proposes a multi-criteria decision optimization model of emergency material distribution under uncertainty that combines the 4E criteria of performance evaluation (economic criterion, efficiency criterion, effectiveness criterion and equity criterion). Then, the solution method of the multiple decision criteria model is designed. Finally, a case study of emergency medical material distribution during COVID-19 in China was conducted for model verification. The results show that different decision criteria have an important impact on the distribution of emergency materials, and the proposed model has significant advantages in the allocation of large-scale disaster relief materials, which can provide a useful reference for emergency material distribution under uncertain information conditions.

Multi-period dynamic multi-objective emergency material distribution model under uncertain demand

This paper proposes a multi-period dynamic emergency material distribution (EMD) model under uncertain demand by considering the disaster degree and demand of different affected areas to improve the reasonability of disaster relief material distribution in post-disaster emergencies. The objective is to minimize the unsatisfied demand, materials distribution cost, and the risk of choosing the path to distribute materials. The model also focuses on the heterogeneity of emergency materials, the dynamics of demand, and the diversity of transportation tools. In addition, the demands and transportation times are described by fuzzy numbers to better fit with the real situation. To solve the problem we propose a hybrid multi-objective salp swarm algorithm and sine cosine algorithm (HMSSA-SCA). Furthermore, numerical examples of different scenarios and scales are solved and compared with the multi-objective salp swarm algorithm (MSSA), multi-objective evolutionary decomposition algorithm for emergency resource scheduling (MOEA/D-mdERS), non-dominated sorting genetic algorithm II (NSGA-II), and strength pareto evolutionary algorithm 2 (SPEA2) to verify the performance of the HMSSA-SCA algorithm. Finally, the floods and landslides disaster event in 2011 in Rio de Janeiro state, Brazil is used to verify the effectiveness and accuracy of the model and algorithm in the real case.

Evaluation optimization method for site selection of urban emergency medical material distribution center

In order to solve the problems of long distribution time and small distribution coverage of urban emergency medical materials distribution centers, this paper constructs an nondominated sorting genetic algorithm-Ⅱ-based (NSGA-Ⅱ-based) optimal location model for the urban emergency medical material distribution center. Aiming at minimizing the cost of transportation and maximizing the coverage of the population and medical resources of the emergency material collection and distribution center to the adjacent areas, the model introduces a compromise fuzzy decision-making method for the overall location plan and establishes an improved analytic hierarchy process (AHP)-fuzzy compromise decision evaluation model. The compromise fuzzy decision-making method is constructed to optimize the overall site selection decision-making. The feasibility of the model is verified by taking the location selection scheme of emergency medical materials collection and distribution in Gaocheng District of Shijiazhuang City, Hebei Province as an example. The results show that the construction of seven centralized distribution centers can meet the needs of epidemic prevention and control in Gaocheng District, Shijiazhuang City. The construction and transportation costs are reduced. The average transportation time of the optimal scheme is 18.9 minutes and the proportion of travel time over 30 minutes driving is 13.3%, which are reduced by 49.1% and 80.0% respectively compared with the previous scheme. The research results can provide reference for the location plan of emergency collection and distribution center under urban public health emergencies.

Optimization Analysis of Emergency Material Distribution Methods Based on Complex Network Model and Cluster Analysis Model

In order to ensure people's normal life, it is especially important to deliver emergency supplies in a timely and accurate manner. Along with the improvement of technology and the gradual popularization of 5G network, the application of drones is becoming more and more widespread, and the delivery mode of "delivery vehicle + drone" has gradually become a new and effective delivery method. The delivery mode of "delivery vehicle + drone" refers to the following: in the process of material delivery, the delivery vehicle delivers to a certain location at the same time, the drone can also deliver to the surrounding feasible locations, and return to the delivery vehicle to reload the materials and replace the battery after the delivery is completed. This distribution mode can greatly improve the efficiency of emergency supplies distribution, and can also solve the distribution of supplies under complex road conditions and avoid secondary damage to people from secondary disasters. This paper is motivated to study the distribution mode of emergency supplies based on complex network model and cluster analysis model.

Research on Location-Routing Problem of Maritime Emergency Materials Distribution Based on Bi-Level Programming

To solve the location-routing problem of maritime emergency materials distribution (MEMD-LRP) of multi-agent joint decision, a bi-level programming model of MEMD-LRP is constructed. The model is established on the premise of considering the rescue time window and emergency materials distribution priority, aiming at the strong regional characteristics of maritime accidents, strong timeliness of maritime rescue, great difficulty of rescue, high cost of rescue, and so on. From the point of view of the emergency management department, the upper level makes the location decision to minimize the construction cost of the shore-based emergency materials reserves and the time satisfaction loss cost of the accident point. From the point of view of the commercial rescue unit, the lower level plans the routes by minimizing the cost of emergency material distribution, ship transportation, ship dispatch, and time penalty. A hybrid algorithm of the ant colony and tabu search is designed to solve the model, and an example is designed and analyzed based on the case of the Bohai Sea to verify the effectiveness of the model and algorithm. Modeling and algorithm design based on bi-level programming can help decision-makers at different levels in maritime emergency logistics systems to make more scientific and reasonable decisions to improve the service capability of the maritime emergency logistics system.

Location Allocation Collaborative Optimization of Emergency Temporary Distribution Center under Uncertainties

Emergencies cause uncertainty in supply chain environment and risks of disruption. Mitigating such risks in emergency supply chain relies on efficient relief material distribution, and in the distribution logistics system, emergency facility location interacts with material allocation clearly. This paper aims to provide a collaborative optimization for the location allocation of temporary emergency distribution centers, with objectives of minimizing rescue time and maximizing demand satisfaction rate. A location allocation model of emergency logistics is formulated by considering uncertain demand and supply information at the response stage of disaster relief. The model is solved by a plant growth simulation algorithm. At last, the feasibility and effectiveness of the model and algorithm in practical application are verified by evaluating a case of COVID-19 prevention and control in Handan city. This paper provides references for decision makers to accomplish the location allocation of emergency facilities and material distribution when dealing with actual situations.

A multi-dimensional robust optimization approach for cold-chain emergency medical materials dispatch under COVID-19: A case study of Hubei Province

For the optimization problem of the cold-chain emergency materials (CEM) distribution routes with multi-demand centers and soft time windows and to solve dispatching materials to medical treatment institutions in various places of the disaster areas under COVID-19, a multi-dimensional robust optimization (MRO) model was proposed, which was solved by a hybrid algorithm combined Pareto genetic algorithm and the improved grey relative analysis (IGRA). The proposed model comprehensively takes into consideration of the cost factors of the cold-chain logistics and robustness of solution with the purpose of minimizing the costs and maximizing robustness. The availability of the proposed approach and hybrid algorithm were thoroughly discussed and qualified through a real-world numerical simulation test case, which was a previous risk area located at Hubei Province. Research results show an average-cost reduction of 4.51% and a robustness increment of 11.69% in addition to consider the urgencies of demand. Consequently, not only the costs can be slightly reduced and the robustness be heightened, but also the blindness of the distribution can be avoided effectively with the demand urgency being considered. Research result indicates that when combining with the specific process of supplies dispatching in the prevention and control, the proposed approach is in a far better agreement in practice, and it could meet the diverse requirements of the emergency scenarios flexibly.