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

2019 ◽  
Vol 11 (12) ◽  
pp. 3242 ◽  
Author(s):  
Ming Zhang ◽  
Yu Zhang ◽  
Zhifeng Qiu ◽  
Hanlin Wu
Keyword(s):  
Large Scale ◽  
Facility Location Problem ◽  
Construction Cost ◽  
Location Model ◽  
Set Covering ◽  
Two Stage ◽  
Emergency Medical ◽  
Rescue System ◽  
Rescue Time ◽  
Ground Ambulances

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.

Construction and Grouping of a Chinese People’s Armed Police Forces Provincial-Level Mobile Rescue Hospital System

2016 ◽  
Vol 12 (4) ◽  
pp. 502-506
Author(s):  
Baoguo Yu ◽  
Zhen Yang ◽  
Song Bai ◽  
Xuexian Shan ◽  
Qi Lv ◽  
...  
Keyword(s):  
Large Scale ◽  
Fast Response ◽  
Hospital System ◽  
Provincial Level ◽  
Service Support ◽  
Emergency Medical ◽  
Response Flexibility ◽  
Rescue System ◽  
Frequent Variety ◽  
Police Forces

AbstractIn recent years, with the increasingly frequent variety of large-scale disasters that have happened in China, the Chinese People’s Armed Police Forces (PAP) has undertaken increasingly frequent and diversified tasks, which has led to greater requirements for the construction of emergency medical rescue equipment. Therefore, as determined by the characteristics of the PAP’s tasks and based on the construction of special boxes and frame tent equipment, a new PAP mobile rescue hospital system was successfully developed, and all PAP provincial-level medical rescue teams have been equipped with this system. In the present article, we describe this mobile rescue hospital system, which is mainly composed of professional emergency vehicles, frame-type tents, and advanced medical equipment. The system has the following characteristics: significant integration, a fast response, flexibility, and practicability. The mobile rescue system is generally used as the army’s own health service support system and to provide certain emergency medical rescue services to disaster-stricken people. The successful construction and further application of this system have significance in terms of accelerating the response of rescue teams and the emergency treatment ability of the PAP’s provincial-level emergency medical rescue teams. (Disaster Med Public Health Preparedness. 2018;12:455–459)

scholarly journals Quantity decisions of two-stage competitive location model based on different location modes

2021 ◽  
Author(s):  
Yadong Li ◽  
Xuemei Li
Keyword(s):  
Branch And Bound ◽  
Price Competition ◽  
Large Scale ◽  
Optimal Location ◽  
Location Model ◽  
Competitive Location ◽  
Decision Variable ◽  
Branch And Bound Method ◽  
Two Stage ◽  
New Firm

AbstractThe facility location of a competing firm in a market has great importance in supply chain management. The two-stage competitive location model formulates the decision process of an entrant firm facing both location and price competition. In this paper, we incorporated the facility quantity as a decision variable into a two-stage competitive location model with the objective of maximized profit. Sequential location mode and simultaneous location mode were applied to simulate different location behavior. We developed an approximate branch and bound method to accelerate optimal location searching speed under the premise of accuracy. Greedy algorithm and approximate branch and bound method were used in two location modes. From algorithm evaluation, we found that the approximate branch and bound method is an ideal supplement of the traditional branch and bound method, especially for location problems with large-scale potential locations. Compare the results of the two modes, we found when a new firm is going to enter a market with both price and location competition, sequential location mode is an advantage strategy, since it can gain more profit than simultaneous location mode.

The Effect of the Familiarity of a Survey Sender on Response Outcomes in a Large-Scale Survey of Emergency Medical Services Agencies

2021 ◽  
Vol 44 (3) ◽  
pp. 260-267
Author(s):  
Morgan M. Millar ◽  
Hilary A. Hewes ◽  
Andrea L. Genovesi ◽  
Michael Ely ◽  
Braden Green ◽  
...  
Keyword(s):  
Emergency Medical Services ◽  
Large Scale ◽  
Nationwide Survey ◽  
Survey Response ◽  
Medical Services ◽  
Healthcare Organizations ◽  
Emergency Medical ◽  
Established Relationship ◽  
To Receive

Survey response is higher when the request comes from a familiar entity compared to an unknown sender. Little is known about how sender influences response to surveys of organizations. We assessed whether familiarity of the sender influences response outcomes in a survey of emergency medical services agencies. Emergency medical services agencies in one U.S. state were randomly assigned to receive survey emails from either a familiar or unfamiliar sender. Both deployment approaches were subsequently used nationwide, with each state selecting one of the two contact methods. Experimental results showed that requests from the familiar sender achieved higher survey response (54.3%) compared to requests from the unfamiliar sender (36.9%; OR: 2.03; 95% CI: 1.23, 3.33). Similar results were observed in the subsequent nationwide survey; in states where the familiar sender deployed the survey, 62.0% of agencies responded, compared to 51.0% when the survey was sent by the unfamiliar sender (OR: 1.57; 95% CI: 1.47, 1.67). The response difference resulted in nearly 60 additional hours of staff time needed to perform telephone follow-up to nonrespondents. When surveying healthcare organizations, surveyors should recognize that it is more challenging to obtain responses without a pre-established relationship with the organizations.

scholarly journals Two-stage multi-tasking transform framework for large-scale many-objective optimization problems

2021 ◽  
Author(s):  
Lu Chen ◽  
Handing Wang ◽  
Wenping Ma
Keyword(s):  
Large Scale ◽  
Original Problem ◽  
Optimization Problems ◽  
Reference Points ◽  
Optimization Strategy ◽  
Two Stage ◽  
Decision Space ◽  
Second Stage ◽  
Decision Variables ◽  
Objective Space

AbstractReal-world optimization applications in complex systems always contain multiple factors to be optimized, which can be formulated as multi-objective optimization problems. These problems have been solved by many evolutionary algorithms like MOEA/D, NSGA-III, and KnEA. However, when the numbers of decision variables and objectives increase, the computation costs of those mentioned algorithms will be unaffordable. To reduce such high computation cost on large-scale many-objective optimization problems, we proposed a two-stage framework. The first stage of the proposed algorithm combines with a multi-tasking optimization strategy and a bi-directional search strategy, where the original problem is reformulated as a multi-tasking optimization problem in the decision space to enhance the convergence. To improve the diversity, in the second stage, the proposed algorithm applies multi-tasking optimization to a number of sub-problems based on reference points in the objective space. In this paper, to show the effectiveness of the proposed algorithm, we test the algorithm on the DTLZ and LSMOP problems and compare it with existing algorithms, and it outperforms other compared algorithms in most cases and shows disadvantage on both convergence and diversity.

scholarly journals Combined Aggregated Sampling Stochastic Dynamic Programming and Simulation-Optimization to Derive Operation Rules for Large-Scale Hydropower System

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 625
Author(s):  
Xinyu Wu ◽  
Rui Guo ◽  
Xilong Cheng ◽  
Chuntian Cheng
Keyword(s):  
Dynamic Programming ◽  
Stochastic Dynamic Programming ◽  
Large Scale ◽  
Simulation Optimization ◽  
Optimal Operation ◽  
Stochastic Dynamic ◽  
Operation Model ◽  
Two Stage ◽  
Operation Rules ◽  
Reservoir Systems

Simulation-optimization methods are often used to derive operation rules for large-scale hydropower reservoir systems. The solution of the simulation-optimization models is complex and time-consuming, for many interconnected variables need to be optimized, and the objective functions need to be computed through simulation in many periods. Since global solutions are seldom obtained, the initial solutions are important to the solution quality. In this paper, a two-stage method is proposed to derive operation rules for large-scale hydropower systems. In the first stage, the optimal operation model is simplified and solved using sampling stochastic dynamic programming (SSDP). In the second stage, the optimal operation model is solved by using a genetic algorithm, taking the SSDP solution as an individual in the initial population. The proposed method is applied to a hydropower system in Southwest China, composed of cascaded reservoir systems of Hongshui River, Lancang River, and Wu River. The numerical result shows that the two-stage method can significantly improve the solution in an acceptable solution time.

scholarly journals A Matheuristic Approach Combining Local Search and Mathematical Programming

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Carolina Lagos ◽  
Guillermo Guerrero ◽  
Enrique Cabrera ◽  
Stefanie Niklander ◽  
Franklin Johnson ◽  
...  
Keyword(s):  
Mathematical Programming ◽  
Local Search ◽  
Large Scale ◽  
Search Algorithm ◽  
Facility Location Problem ◽  
Specific Information ◽  
Local Search Algorithm ◽  
Capacitated Facility Location Problem ◽  
Capacitated Facility Location ◽  
Installation Cost

A novel matheuristic approach is presented and tested on a well-known optimisation problem, namely, capacitated facility location problem (CFLP). The algorithm combines local search and mathematical programming. While the local search algorithm is used to select a subset of promising facilities, mathematical programming strategies are used to solve the subproblem to optimality. Proposed local search is influenced by instance-specific information such as installation cost and the distance between customers and facilities. The algorithm is tested on large instances of the CFLP, where neither local search nor mathematical programming is able to find good quality solutions within acceptable computational times. Our approach is shown to be a very competitive alternative to solve large-scale instances for the CFLP.

scholarly journals A Compound Wind Power Forecasting Strategy Based on Clustering, Two-Stage Decomposition, Parameter Optimization, and Optimal Combination of Multiple Machine Learning Approaches

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3586 ◽  
Author(s):  
Sizhou Sun ◽  
Jingqi Fu ◽  
Ang Li
Keyword(s):  
Wind Power ◽  
Large Scale ◽  
Wind Farm ◽  
Search Algorithm ◽  
Interpolation Method ◽  
Least Square ◽  
Support Vector ◽  
Two Stage ◽  
Wind Power Forecasting ◽  
Power Forecasting

Given the large-scale exploitation and utilization of wind power, the problems caused by the high stochastic and random characteristics of wind speed make researchers develop more reliable and precise wind power forecasting (WPF) models. To obtain better predicting accuracy, this study proposes a novel compound WPF strategy by optimal integration of four base forecasting engines. In the forecasting process, density-based spatial clustering of applications with noise (DBSCAN) is firstly employed to identify meaningful information and discard the abnormal wind power data. To eliminate the adverse influence of the missing data on the forecasting accuracy, Lagrange interpolation method is developed to get the corrected values of the missing points. Then, the two-stage decomposition (TSD) method including ensemble empirical mode decomposition (EEMD) and wavelet transform (WT) is utilized to preprocess the wind power data. In the decomposition process, the empirical wind power data are disassembled into different intrinsic mode functions (IMFs) and one residual (Res) by EEMD, and the highest frequent time series IMF1 is further broken into different components by WT. After determination of the input matrix by a partial autocorrelation function (PACF) and normalization into [0, 1], these decomposed components are used as the input variables of all the base forecasting engines, including least square support vector machine (LSSVM), wavelet neural networks (WNN), extreme learning machine (ELM) and autoregressive integrated moving average (ARIMA), to make the multistep WPF. To avoid local optima and improve the forecasting performance, the parameters in LSSVM, ELM, and WNN are tuned by backtracking search algorithm (BSA). On this basis, BSA algorithm is also employed to optimize the weighted coefficients of the individual forecasting results that produced by the four base forecasting engines to generate an ensemble of the forecasts. In the end, case studies for a certain wind farm in China are carried out to assess the proposed forecasting strategy.

Large-Scale Simulation of the Clocking Impact of 2D Combustor Profile on a Two Stage High Pressure Turbine

2014 ◽  
Author(s):  
Thomas E. Dyson ◽  
David B. Helmer ◽  
James A. Tallman
Keyword(s):  
High Pressure ◽  
Large Scale ◽  
High Pressure Turbine ◽  
Cfd Simulations ◽  
Two Stage ◽  
Substantial Impact ◽  
Sliding Mesh ◽  
Wall Flow ◽  
End Wall ◽  
Film Flows

This paper presents sliding-mesh unsteady CFD simulations of high-pressure turbine sections of a modern aviation engine in an extension of previously presented work [1]. The simulation included both the first and second stages of a two-stage high-pressure turbine. Half-wheel domains were used, with source terms representing purge and film flows. The end-wall flow-path cavities were incorporated in the domain to a limited extent. The passage-to-passage variation in thermal predictions was compared for a 1D and 2D turbine inlet boundary condition. Substantial impact was observed on both first and second stage vanes despite the mixing from the first stage blade. Qualitative and quantitative differences in surface temperature distributions were observed due to different ratios between airfoil counts in the two domains.

Sign in / Sign up

Export Citation Format

Share Document