A mathematical model for efficient emergency transportation in a disaster situation

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.

A new humanitarian relief logistic network for multi-objective optimization under stochastic programming

Millions of affected people and thousands of victims are consequences of earthquakes, every year. Therefore, it is necessary to prepare a proper preparedness and response planning. The objectives of this paper are i) minimizing the expected value of the total costs of relief supply chain, ii) minimizing the maximum number of unsatisfied demands for relief staff and iii) minimizing the total probability of unsuccessful evacuation in routes. In this paper, a scenario based stochastic multi-objective location-allocation-routing model is proposed for a real humanitarian relief logistics problem which focused on both pre- and post-disaster situations in presence of uncertainty. To cope with demand uncertainty, a simulation approach is used. The proposed model integrates these two phases simultaneously. Then, both strategic and operational decisions (pre-disaster and post-disaster), fairness in the evacuation, and relief item distribution including commodities and relief workers, victim evacuation including injured people, corpses and homeless people are also considered simultaneously in this paper. The presented model is solved utilizing the Epsilon-constraint method for small- and medium-scale problems and using three metaheuristic algorithms for the large-scale problem (case study). Empirical results illustrate that the model can be used to locate the shelters and relief distribution centers, determine appropriate routes and allocate resources in uncertain and real-life disaster situations.

Towards an efficient collection and transport of COVID-19 diagnostic specimens using genetic-based algorithms

The speed by which the COVID-19 pandemic spread throughout the world makes the emergency services unprepared to answer all the patients’ requests. The Tunisian ministry of health established a protocol planning the sample collection from the patients at their location. A triage score is first assigned to each patient according to the symptoms he is showing, and his health conditions. Then, given the limited number of the available ambulances in each area, the location of the patients and the capacity of the nearby hospitals for receiving the testing samples, an ambulance scheduling and routing plan needs to be established so that specimens can be transferred to hospitals in short time. In this paper, we propose to model this problem as a Multi-Origin–Destination Team Orienteering Problem (MODTOP). The objective is to find the optimal one day tour plan for the available ambulances that maximizes the collected scores of visited patients while respecting duration and capacity constraints. To solve this NP-hard problem, two highly effective approaches are proposed which are Hybrid Genetic Algorithm (HGA) and Memetic Algorithm (MA). The HGA combines (i) a k-means construction method for initial population generation and (ii) a one point crossover operator for solution recombination. The MA is an improvement of HGA that integrates an effective local search based on three different neighborhood structures. Computational experiments, supported by a statistical analysis on benchmark data sets, illustrate the efficiency of the proposed approaches. HGA and MA reached the best known solutions in 54.7% and 73.5% of instances, respectively. Likewise, MA reached a relative error of 0.0675% and performed better than four existing approaches. Real-case instances derived from the city of Tunis were also solved and compared with the results of an exact solver Cplex to validate the effectiveness of our algorithm.

Fairness in ambulance routing for post disaster management

Disaster management generally includes the post-disaster stage, which consists of the actions taken in response to the disaster damages. These actions include the employment of emergency plans and assigned resources to (i) rescue affected people immediately, (ii) deliver personnel, medical care and equipment to the disaster area, and (iii) aid to prevent the infrastructural and environmental losses. In the response phase, humanitarian logistics directly influence the efficiency of the relief operation. Ambulances routing problem is defined as employing the optimisation tools to manage the flow of ambulances for finding the best ambulance tours to transport the injured to hospitals. Researchers pointed out the importance of equity and fairness in humanitarian relief services: managing the operations of ambulances in the immediate aftermath of a disaster must be done impartially and efficiently to rescue affected people with different priority in accordance with the restrictions. Our research aim is to find the best ambulance tours to transport the patients during a disaster in relief operations while considering fairness and equity to deliver services to patients in balance. The problem is formulated as a new variant of the team orienteering problem with hierarchical objectives to address also the efficiency issue. Due to the limitation of solving the proposed model using a general-purpose solver, we propose a new hybrid algorithm based on a machine learning and neighbourhood search. Based on a new set of realistic benchmark instances, our quantitative analysis proves that our algorithm is capable to largely reduce the solution running time especially when the complexity of the problem increases. Further, a comparison between the fair solution and the system optimum solution is also provided.

Metaheuristics in the decentralization of SAMU bases using simulation in northeastern Brazil

BACKGROUND AND OBJECTIVE

The growth of the urban population exerts considerable pressure on municipalities' public managers to focus their attention on providing emergency medical care that meets the growing demand for emergency pre-hospital medical care. Currently, there are a significant number of traffic accidents and other serious occurrences, such as heart attacks, drownings, epidemics, fires and disasters (floods, landslides, earthquakes) that demand a prompt and seamless response from pre-hospital medical care. As a result of such scenario, the present article endeavours to apply a dual-coverage mathematical model (DSM-Double Standard Model) to define the optimal location of the Emergency Medical Service (SAMU) decentralized dispatch bases in Natal/RN and conduct a simulation study to evaluate the displacement of ambulances between such bases.

METHODS

The methodological course that was followed by this research constitutes of 12 steps. The location of decentralized bases for sending emergency ambulances was established using the DSM model and the simulation model was performed using the FlexSim© software version 2018 evaluating base coverage in relation to the total number of calls by demand points for different scenarios.

RESULTS

The results obtained throughout the research demonstrated the feasibility of redefining the decentralized bases of SAMU/Natal ambulances as a strategy to reduce response time and guarantee compliance with performance parameters established by international organizations (the World Health Organization, for instance, establishes the time of 8 minutes for emergency medical service calls response). The simulation study showed a significant reduction in response time, by up to 60% in some cases.

CONCLUSION

The proposition of new locations for the decentralized dispatch bases of the SAMU/Natal can provide an overall significant reduction on the ambulance response time, so as to contribute to expedite the initiation of treatment of patients, if necessary, sent to hospitals.

Multi-Objective Two-Stage Stochastic Programming Model for a Proposed Casualty Transportation System in Large-Scale Disasters: A Case Study

Disaster management is a process that includes mitigation, preparedness, response and recovery stages. Operational strategies covering all stages must be developed in order to alleviate the negative effects of the disasters. In this study, we aimed at minimizing the number of casualties that could not be transported to the hospitals after the disaster, the number of additional ambulances required in the response stage, and the total transportation time. Besides, we assumed that a data-driven decision support tool is employed to track casualties and up-to-date hospital capacities, so as to direct the ambulances to the available hospitals. For this purpose, a multi-objective two-stage stochastic programming model was developed. The model was applied to a district in Istanbul city of Turkey, for a major earthquake. Accordingly, the model was developed with a holistic perspective with multiple objectives, periods and locations. The developed multi-objective stochastic programming model was solved using an improved version of the augmented ε-constraint (AUGMECON2) method. Hence, the Pareto optimal solutions set has been obtained and compared with the best solution achieved according to the objective of total transportation time, to see the effect of the ambulance direction decisions based on hospital capacity availability. All of the decisions examined in these comparisons were evaluated in terms of effectiveness and equity. Finally, managerial implication strategies were presented to contribute decision-makers according to the results obtained. Results showed that without implementing a data-driven decision support tool, equity in casualty transportation cannot be achieved among the demand points.

Vehicle Routing Optimisation in Humanitarian Operations: A Survey on Modelling and Optimisation Approaches

The growing field of humanitarian operations is driven by frequent events of disasters seen in the world today. Within this field, Operations Research (OR) plays a critical role in alleviating the suffering of victims that are impacted by disasters. This paper focuses on the branch of a well-known OR problem, known as the Vehicle Routing Problem (VRP), within the selected scope of humanitarian operations. A total of 123 papers of the last decade are reviewed and classified under the humanitarian operations of supply and delivery, evacuation as well as rescue operations. Besides specific disaster management phases and disaster types, various modelling challenges are highlighted, hinting towards a richer and more complex VRP seen under selected model characteristic classifications. Furthermore, established solution approaches, including hybrid solutions, are highlighted and classified, discussing how they are applied in the context of these humanitarian operations. The inclusion of a machine learning solution approach under the same classification is proposed. Finally, the trend and future outlook of VRP for the suggested humanitarian operations are discussed and highlighted.

Two-Stage Covering Location Model for Air–Ground Medical Rescue System

This study tackled the multimodal facility location problem in emergency medical rescue. First, an intermodal setting was suggested, i.e., considering cooperation between ground ambulances and helicopters in emergency medical rescues. Specifically, four scheduling modes were structured: air only, ground only, air-ground combined mode if landing and take-off site for helicopters near the wounded is available, and air-ground transshipment if the landing and take-off site for helicopters near the wounded is not available. Second, a two-stage covering location model was proposed. In the first stage, a set-covering model was developed to achieve maximum coverage and minimal total construction cost of emergency rescue facilities. The optimal mixed allocation proportion of helicopters and ground ambulances was then obtained to guarantee cohesion between the hierarchical models and covering characteristics and the economic efficiency of location results. In the second stage, for given emergency locations, an emergency scheduling mode matrix was constructed for meeting response time and total rescue time constraints. The proposed model obtains optimal results in terms of coverage, construction cost, and rescue time. A case study of Beijing, China validated the feasibility and efficiency of the two-stage covering location model for multimodal emergency medical rescue network. The proposed air-ground rescue system and two-stage covering location model can be extended and also used for large-scale disaster rescue management.

Point-of-Care Diagnosis of Acute Myocardial Infarction in Central Vietnam: International Exchange, Needs Assessment, and Spatial Care Paths

OBJECTIVES

Objectives were to (a) advance point-of-care (POC) education, international exchange, and culture; (b) report needs assessment survey results from Thua Thien Hue Province, Central Vietnam; (c) determine diagnostic capabilities in regional health care districts of the small-world network of Hue University Medical Center; and (d) recommend Spatial Care Paths that accelerate the care of acute myocardial infarction (AMI) patients.

METHODS

We organized progressively focused, intensive, and interactive lectures, workshops, and investigative teamwork over a 2-year period. We surveyed hospital staff in person to determine the status of diagnostic testing at 15 hospitals in 7 districts. Questions focused on cardiac rapid response, prediabetes/diabetes, infectious diseases, and other serious challenges, including epidemic preparedness.

RESULTS

Educational exchange revealed a nationwide shortage of POC coordinators. Throughout the province, ambulances transfer patients primarily between hospitals, rarely picking up from homes. No helicopter rescue was available. Ambulance travel times from distant sites to referral hospitals were excessive, longer in costal and mountainous areas. Most hospitals (92.3%) used electrocardiogram and creatine phosphokinase-MB isoenzyme to diagnose AMI. Cardiac troponin I/T testing was performed only at large referral hospitals.

CONCLUSIONS

Central Vietnam must improve rapid diagnosis and treatment of AMI patients. Early upstream POC cardiac troponin testing on Spatial Care Paths will expedite transfers directly to hospitals capable of intervening, improving outcomes following coronary occlusion. Point-of-care coordinator certification and financial support will enhance standards of care cost-effectively. Training young physicians pivots on high-value evidence-based learning when POC cardiac troponin T/cardiac troponin I biomarkers are in place for rapid decision making, especially in emergency rooms.