Comparative Study of Emergency Logistics Path Planning

As natural disasters attract extensive attention worldwide,it is significant for governments and researchers to determine how to deliver emergency supplies after natural disasters.Considering the disaster victims' urgent needs for supplies after the disaster,based on the previous research,the path planning model from the shortest path and the optimal time was created in this paper.As in the actual road the outbreak of emergencies might cause some secondary disasters,including landslides triggered by heavy rain,paralysis of traffic together with a large crowd caused by earthquakes,the theory and method of the complex network to explore the topological structure characteristics of the whole network in the affected area were applied.Then,combining with the actual situation of the road network,the reliability analysis of the points,edges as well as topological structures were presented and a mathematical model based on reliability was proposed.Finally,two kinds of emergency logistics path planning models were compared and analyzed by some examples.The results showed that the relationship between time optimality and reliability was relevant.Emergency logistics path planning should not only consider the optimal transportation time of emergency logistics,but also pay attention to the reliability of transportation from a global perspective.

Emergency logistics scheduling with multiple supply-demand points based on grey interval

This study aimed to address the problem of post-disaster emergency material dispatching from multiple supply points to multiple demand points.In large-scale natural disasters,it is very important for multiple emergency material supply points to serve as sources of materials for multiple disaster sites and to determine emergency material scheduling solutions accurately.Furthermore,the quantity of emergency materials required at each disaster site is uncertain.To address this issue,in this study,we developed an emergency material scheduling model with multiple logistics supply points for multiple demand points based on the grey interval numbers.To optimize the proposed multi-supply-point and multi-demand-point emergency material scheduling mode,a multi-objective optimization algorithm based on a genetic algorithm was used.Experimental results demonstrate that the multi-objective optimization method can solve the emergency logistics scheduling problem better than the particle swarm optimization multi-objective solution algorithm.Additionally,the multi-supply point and multi-demand point emergency material dispatch model and optimization algorithm provides robust support for emergency management system decision-makers when they need to respond quickly to disaster relief activities.

Unmanned Technology-Based Civil-Military Intelligent Logistics System:From Construction to Integration

The stockpiling,delivery,and provision of emergency material were in the public gaze of millions of people when the coronavirus disease 2019(COVID-19)broke out.Civil-military integra-tion emergency logistics silently opened up the“second battlefield”of anti-epidemic,and estab-lished a lifeline under that emergency situation.Research on the construction of civil-military integ-rated logistics system plays an extremely important role and occupies a significant position in en-suring social stability and security as well as the stable development of social economy in China.The modern economy driven by the Internet,Internet of Things,and big data demonstrates a rap-id growing trend calling for efficient,fast,and convenient logistics.It is urgent to upgrade or build an intelligent logistics system with intelligent technology and unmanned technology as the core to meet the international and domestic market demand.As mentioned above,this paper analyzes and expounds the construction problem and practical significance of civil-military integration emer-gency logistics system based on unmanned technology,and puts forward the strategy of construct-ing civil-military integration emergency logistics system with unmanned technology under the new system.

Multi-stage emergency medicine logistics system optimization based on survival probability

Using sudden cardiac deaths as an example and maximizing survival rate as the goal, this paper studies the influence of multi-stage medical logistics system optimization on the survival rate of sudden illness. A distribution model of survival is built, drone and ambulance arrival probability over time are discussed, a formula is proposed for maximum possible survival rate based on the probability of emergency medical logistics reaching the patient, and the results are analyzed using empirical data fitting distribution and numerical experiments performed with the model. The model is discussed as a reference point for management decision making by changing model parameters. Results show that compared to using current ambulance vehicles, ambulance drones delivering medical equipment for first aid on-site in emergencies can significantly increase survival rate, and the effect of collaborative multi-stage logistics optimization is better than that of any single stage logistics response optimization. Simulation results show that the medical rescue logistics service radius, speed, loading capacity and performance of ambulance drones impact the probability of survival, and there is an optimal service radius depending on the shape of probability distribution, which provides new information for management decisions.

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.