Emergency Supplies Distribution Research Articles

Regional-scale dynamic planning for distributing emergency supplies under evolving tropical cyclones

A well-designed response plan for the distribution of emergency supplies (ES) during an evolving tropical cyclone (TC) can effectively minimize socio-economic losses in coastal regions. However, the dynamic and uncertain nature of a TC is likely to result in spatio-temporally varying ES demands in the different areas within a hazard-stricken region. While meteorological forecasting technologies and advanced data collection mechanisms can provide real-time information on an evolving storm and its impacts, a methodology that can systematically integrate such information into real-time ES allocation decisions has yet to be formulated. This study develops a framework to support proactive, regional-scale, multi-phase ES allocation decisions that adapt to the dynamic characteristics of a TC as it evolves in real time. The framework consists of two modules: an updating module that uses real-time information collected during the evolving storm to continuously update the projections of the ES demands in different areas within the region; and an optimization module that allocates the ES according to the updated demand projections by minimizing the ES shortages and excessive redundancies, as well as the logistics costs of the allocations. An application of the framework to ES planning in Zhejiang Province, China, during Typhoon Lekima in 2019 is presented.

A humanitarian vehicle routing problem synchronized with drones in time-varying weather conditions

This paper applies the cooperative delivery mode of trucks synchronized with drones to humanitarian logistics (HVRP-SD), considers the impact of time-varying weather conditions on the synchronous delivery from two dimensions (HVRP-SD-TVW): drone safety and drone delivery efficiency, and investigate a multi-objective optimization problem. This issue concerns not only the delivery timeliness of relief supplies, but also the fairness of supplies allocation. First, we establish a basic multi-objective mixed integer programming model for HVRP-SD, and then establish an extended mathematical model for HVRP-SD-TVW. To solve the multi-objective optimization problem, we develop a hybrid multi-objective evolutionary algorithm (HMOEA). According to the characteristics of HVRP-SD-TVW, HMOEA designs a variable-length two-dimensional array encoding method, fitness evaluation rules, multimodal mutation, and specialized local search operators. A set of numerical experiments prove that HMOEAS is highly competitive, and both the multimodal mutation and local search have significant effects on improving the quality of solutions. Further, taking the emergency supplies distribution in flood-stricken areas of Gongyi City, China in 2021 as a sample case, we prove that weather conditions play an important role in the truck-drone synchronized delivery decision by comparing the results of the basic model and extended model. In addition, we respectively analyze the variation in solutions under different weather scenarios, departure times, and drone risk resilience to guide decision-making.

Multi-objective medical supplies distribution open vehicle routing problem with fairness and timeliness under major public health emergencies

Fair and timely delivery of supplies plays a critical role under major public health emergencies. In this paper, aiming at fairness and timeliness, an optimization model of open vehicle routing problem for medical supplies distribution is established considering the urgency of the demand. We adopt a differential evolutionary algorithm with fast non-dominated solution sorting to solve the proposed model, obtaining an approximate Pareto optimal solution set. Through the comparison of algorithms, the results showed that the differential evolutionary algorithm with non-dominated sorting is superior with a shorter runtime and more diverse solutions, while the epsilon constraint method has more accurate solutions. In the case verification, the quality of the solutions of both algorithms was within the acceptable range, but the runtime of the epsilon constraint method was too long to be applicable. The results can provide theoretical suggestions and practical guidance for decision-makers in emergency supplies distribution.

Research on the Distribution of Emergency Supplies during the COVID-19 Epidemic

The COVID-19 virus spreads quickly and is highly contagious, and the timeliness of emergency supplies is extremely demanding, so adequate and efficient emergency supplies have become a necessary prerequisite for strict prevention and control during the outbreak. However, the available research results are still relatively few, and using the brain storm optimization algorithm to solve the emergency supplies distribution problem is still blank. In order to optimize the emergency supplies distribution system in the context of COVID-19, this paper studies the open vehicle routing problem by considering the load weight constraints, demand constraints, and time window constraints. With the shortest total distribution distance as the objective function, a mathematical model of mixed integer linear programming is established, and the crossover operation of the genetic algorithm, the destruction operator and the repair operator of the large neighborhood search algorithm are introduced in the brain storm optimization algorithm to enhance the diversity of the solution while making the solution toward the optimal direction. Through the verification of two sets of examples, we found that compared with the other 13 solution algorithms, the solution capability and solution efficiency of the brain storm optimization algorithm are better than the comparison algorithms, which can significantly reduce the length of the distribution route and lower the distribution cost, and has excellent convergence and stability. Meanwhile, it can effectively improve the distribution efficiency of emergency supplies, which is of great significance to help the emergency reception and regular prevention and control during the outbreak of the COVID-19 epidemic. In addition, it has the necessary guidance for the improvement of personnel efficiency in the supply chain and logistics industry.

Dynamic Optimization of Emergency Logistics for Major Epidemic Considering Demand Urgency

In recent years, epidemic disasters broke through frequently around the world, posing a huge threat to economic and social development, as well as human health. A fair and accurate distribution of emergency supplies during an epidemic is vital for improving emergency rescue efficiency and reducing economic losses. However, traditional emergency material allocation models often focus on meeting the amount of materials requested, and ignore the differences in the importance of different emergency materials and the subjective urgency demand of the disaster victims. As a result, it is difficult for the system to fairly and reasonably match different scarce materials to the corresponding areas of greatest need. Consequently, this paper proposes a material shortage adjustment coefficient based on the entropy weight method, which includes indicators such as material consumption rate, material reproduction rate, durability, degree of danger to life, and degree of irreplaceability, to enlarge and narrow the actual shortage of material supply according to the demand urgency. Due to the fact that emergency materials are not dispatched in one go during epidemic periods, a multi-period integer programming model was established to minimize the adjusted total material shortage based on the above function. Taking the cases of Wuhan and Shanghai during the lockdown and static management period, the quantitative analysis based on material distribution reflected that the model established in this paper was effective in different scenarios where there were significant differences in the quantity and structure of material demand. At the same time, the model could significantly adjust the shortage of emergency materials with higher importance and improve the satisfaction rate.

A location-inventory-routing model for distributing emergency supplies

Dynamic last-mile distribution of emergency supplies to affected people in post-disaster situations is a key emergency response task, which however is seldom addressed in the literature. To fill this gap, we present a path-based location-inventory-routing model for developing a dynamic last-mile emergency supply distribution plan, including a local distribution center, points of distribution, and demand points. The model can be applied to evolving post-hurricane situations, where candidate point of distribution sites and demands dynamically change. Both cost and equity are incorporated into the planning goal to ensure that distribution of emergency supplies is cost-efficient and fair. With a case study of the previously flooded Red Hook neighborhood in New York City during Hurricane Sandy, we show that our model outperforms different comparison models significantly. Moreover, we obtain managerial insights regarding location, inventory, and routing flexibility, which could be leveraged for post-hurricane distribution practice.

A subgradient-based neurodynamic algorithm to constrained nonsmooth nonconvex interval-valued optimization

In this paper, a subgradient-based neurodynamic algorithm is presented to solve the nonsmooth nonconvex interval-valued optimization problem with both partial order and linear equality constraints, where the interval-valued objective function is nonconvex, and interval-valued partial order constraint functions are convex. The designed neurodynamic system is constructed by a differential inclusion with upper semicontinuous right-hand side, whose calculation load is reduced by relieving penalty parameters estimation and complex matrix inversion. Based on nonsmooth analysis and the extension theorem of the solution of differential inclusion, it is obtained that the global existence and boundedness of state solution of neurodynamic system, as well as the asymptotic convergence of state solution to the feasible region and the set of LU-critical points of interval-valued nonconvex optimization problem. Several numerical experiments and the applications to emergency supplies distribution and nondeterministic fractional continuous static games are solved to illustrate the applicability of the proposed neurodynamic algorithm.

Optimising the Distribution of Multi-Cycle Emergency Supplies after a Disaster

In order to achieve rapid and fair distribution of emergency supplies after a large-scale sudden disaster, this paper constructs a comprehensive time perception satisfaction function and a comprehensive material loss pain function to portray the perceived satisfaction of disaster victims based on objective constraints such as limited transport, multimodal transport and supply being less than demand, and at the same time considers the subjective perception of time and material quantity of disaster victims under limited rational conditions, and constructs a multi-objective optimisation model for the dispatch of multi-cycle emergency supplies by combining comprehensive rescue cost information. For the characteristics of the proposed model, based on the NSGA-II algorithm, generalized reverse learning strategy, coding repair strategy, improved adaptive crossover, variation strategy, and elite retention strategy are introduced. Based on this, we use the real data of the 2008 Wenchuan earthquake combined with simulated data to design corresponding cases for validation and comparison with the basic NSGA-II algorithm, SPEA-II and MOPSO algorithms. The results show that the proposed model and algorithm can effectively solve the large-scale post-disaster emergency resource allocation problem, and the improved NSGA- II algorithm has better performance.

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.

An Advanced Study of Urban Emergency Medical Equipment Logistics Distribution for Different Levels of Urgency Demand.

At the early stage of a major public health emergency outbreak, there exists an imbalance between supply and demand in the distribution of emergency supplies. To improve the efficiency of emergency medical service equipment and relieve the treatment pressure of each medical treatment point, one of the most important factors is the emergency medical equipment logistics distribution. Based on the actual data of medical equipment demand during the epidemic and the characteristics of emergencies, this study proposed an evaluation index system for emergency medical equipment demand point urgency, based on the number of patients, the number of available inpatient beds, and other influencing factors as the index. An urban emergency medical equipment distribution model considering the urgency of demand, the distribution time window, and vehicle load was constructed with the constraints. Wuhan, Hubei Province, China, at the beginning of the outbreak was selected as a validation example, and the Criteria Importance Though Intercriteria Correlation (CRITIC) method and the genetic algorithm were used to simulate and validate the model with and without considering the demand urgency. The results show that under the public health emergencies, the distribution path designed to respond to different levels of urgency demand for medical equipment is the most efficient path and reduces the total distribution cost by 5%.

Research on emergency supplies distribution in 5G network environment based on TSP model

Along with the improvement of technology and the gradual popularization of 5G network, the application of UAVs is becoming more and more widespread, and the delivery mode of "delivery vehicle + UAV" has gradually become a new and effective delivery method. In order to solve the real problem of emergency supplies to be delivered to the disaster area after the disaster and to improve the efficiency of emergency supplies delivery, this new and effective delivery mode is used to solve the delivery problem. In this paper, a TSP model based on Floyd algorithm and genetic algorithm is established and the optimal distribution scheme is given. In the delivery mode of "delivery vehicle + UAV", the individual evaluation function is improved, and the flight conditions of UAV are introduced, and the optimal delivery scheme is given by simulation selection, crossover and variation. In the mode of "delivery vehicle + UAV" and the delivery vehicle has a single weight limit of 500 kg, The optimal solution is given for single "delivery vehicle + UAV" and the optimal solution is given for a single "delivery vehicle + UAV" and a double "delivery vehicle + UAV".

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.

Study on the optimization of urban emergency supplies distribution paths for epidemic outbreaks

The humanitarian logistics vehicle routing problem (VRP) of urban emergency supplies in response to epidemic outbreaks is studied in this work to address the adverse effects of epidemic outbreaks on human society. Under the premise that the greater the urgency of the demand for various emergency supplies by the hospitals in the city’s jurisdiction, the higher the priority of distribution, the fitness function of the highest vehicle utilization rate and the lowest transportation cost is established. The aim of the research is to construct a multi-objective mathematical model of emergency supplies distribution for large cities in case of an epidemic outbreak. Based on the multi-verse optimization algorithm and the differential evolution algorithm, a hybrid multi-verse optimizer algorithm based on differential evolution (DE-IMOV) is proposed. A simulation experiment is then carried out to verify the model. The calculation results show that the developed algorithm can effectively reduce the distribution cost and improve the full load rate of vehicles on the basis of considering the urgency of emergency supplies demand, which provides a solution for the optimization of heterogeneous vehicle distribution paths for urban emergency supplies in response to epidemic outbreaks.

A Greedy Randomized Adaptive Search Procedure (GRASP) for the multi-vehicle prize collecting arc routing for connectivity problem

Natural disasters such as earthquakes can severely impact road networks. Depending on the disaster intensity and the size of the affected area, the network may be divided into multiple disconnected parts. In a disaster response context, decision-makers need to determine the roads that should be unblocked to facilitate relief activities such as search and rescue, evacuation, and distribution of emergency supplies. The multi-vehicle prize collecting arc routing for connectivity problem (KPC-ARCP) is a well-known problem dealing with such a scenario. A matheuristic to solve the KPC-ARCP was proposed in previous research, which tested instances with fewer than 400 vertices and 700 edges. However, it is unknown whether the matheuristic can handle larger instances. This article proposes a Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic with the hypothesis that GRASP is faster and can solve more extensive networks. Two sets of tests are performed on randomly generated instances with increasing size. The gap in the objective function values and the execution times of GRASP versus the matheuristic are compared. The results indicate that GRASP can achieve objective function values as good as the matheuristic and is significantly faster depending on the parameter settings.

Emergency Material Reserve Location Research in Urban Areas Based on the Supply and Demand Perspective: A Case from China

In order to improve the scientific layout of urban emergency material reserve, first, considering that the road condition has an important impact on the effect of emergency rescue, the concept of road damage risk factor is introduced in this article to build satisfaction attenuation function based on the thought of demand satisfaction decreasing within the coverage radius; second, based on the maximum coverage model, a multi-objective and multi-repeated cover model was established to solve the limited supply capacity of emergency supplies reserve; finally, the model is verified by using Yanqing district in Beijing. The study’s results showed that the improved maximum coverage model can scientifically determine the coverage and quantity of emergency supplies of each emergency material reserve, and improve the efficiency and scientificity of emergency supplies distribution. In order to avoid the disorderly distribution of emergency supplies, it is suggested that the coverage radius and scope of the emergency material reserve should be determined based on the balance between supply and demand.

Research on Emergency Supplies Distribution Based on Improved Simulated Annealing Algorithm

With the improvement of technology and the popularization of 5G network, the use of UAVs is becoming more and more widespread, and the delivery mode of "delivery vehicle + UAV" has become a new and effective delivery mode in the event of an accident. It is very important to use the joint delivery of "delivery vehicle + UAV" to deliver emergency supplies to the designated location in the shortest time possible in case of accidents. In this paper, we focus on the optimization strategy of the distribution mode under the condition of the known distance of the distribution location and other information, and make the optimal distribution route plan and select the optimal starting point according to the known data, so as to improve the efficiency of the distribution of emergency materials. First of all, we adopt the mode of single-truck distribution and solve for the minimum distance in the case of single-truck distribution. On this basis, the transport path of the transport vehicle is solved by using a transport vehicle with an unmanned aerial vehicle to assist in distribution, with other conditions remaining unchanged.

Research on Decision Model of Emergency Material Distribution Based on Random Information

As an important link of emergency logistics, emergency material distribution has been widely concerned and applied in the development of today’s society. From the consideration of the fairness and efficiency of material distribution, there is a kind of negative and negative relationship between them. As a lever, information updating can be used to solve the problems of fairness and efficiency in the distribution of emergency supplies. Based on fuzzy objective programming, the classification decision model of emergency materials is discussed in this paper.

Distribution of the Emergency Supplies in the COVID-19 Pandemic: A Cloud Computing Based Approach

The containment of the COVID-19 pandemic was significantly affected by the unbalanced distribution of emergency supplies, low coordinated transport efficiency, high costs, and the inability of nonprofit organizations to handle the emergency supplies efficiently. Based on the COVID-19 experience, in this paper, we build a cloud platform for emergency supplies distribution to reduce the asymmetry of emergency logistics information, reduce the costs, and improve the efficiency of emergency supplies distribution. Our proposed method uses a genetic algorithm with the monarch scheme to optimize the urban emergency supplies distribution. The numerical results and sensitivity analysis for a sample network indicate that using the proposed platform the integrated cost in different cities are reduced by 29.01%, 28.67%, and 22.73%, the required time in different cities are reduced by 22.98%, 26.59%, and 36.65%. The results suggest that the proposed method reduces the integrated cost and transportation time and finds the optimal distribution path.

Optimal dispatching strategy for power emergency supplies distribution considering the cooperation of supply reserve points

Power accidents in extreme conditions have seriously threatened the safe operation of the power system. Therefore, the research on optimal dispatching strategy for power emergency supplies in extreme conditions is the focus of power companies. According to the strong timeliness and weak economy of extreme conditions, this paper puts forward a two-layer optimization model of power emergency supplies distribution: according to the reserve and demand of supplies supply point and distribution point, an optimal dispatching model of power emergency supplies is put forward at the bottom level considering the time factor. At the same time, considering the time sequence evolution of extreme conditions, the optimal dispatching model of power emergency supplies is proposed to ensure that the power emergency supplies can still meet the demand after the extreme conditions deteriorate. Finally, the comprehensive learning particle swarm optimization algorithm is used to solve the problem, and the example results show that the model is effective and reasonable.

Adaptive logistics: Conceptualising strategies and best practices from humanitarian emergency supplies distribution

Adaptive logistics: Conceptualising strategies and best practices from humanitarian emergency supplies distribution