scholarly journals An Optimization Model to Address Overcrowding in Emergency Departments Using Patient Transfer

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zeynab Oveysi ◽  
Ronald G. McGarvey ◽  
Kangwon Seo
Keyword(s):  
Optimization Model ◽  
Emergency Departments ◽  
Efficient Frontier ◽  
Mixed Integer ◽  
Service Capacity ◽  
Patient Transfers ◽  
Emergency Situations ◽  
Computational Testing ◽  
Ed Overcrowding ◽  
Expected Waiting Time

Overcrowding of emergency departments (EDs) is a problem that affected many hospitals especially during the response to emergency situations such as pandemics or disasters. Transferring nonemergency patients is one approach that can be utilized to address ED overcrowding. We propose a novel mixed-integer nonlinear programming (MINLP) model that explicitly considers queueing effects to address overcrowding in a network of EDs, via a combination of two decisions: modifying service capacity to EDs and transferring patients between EDs. Computational testing is performed using a Design of Experiments to determine the sensitivity of the MINLP solutions to changes in the various input parameters. Additional computational testing examines the effect of ED size on the number of transfers occurring in the system, identifying an efficient frontier for the tradeoff between system cost (measured as a function of the service capacity and the number of patient transfers) and the systemwide average expected waiting time. Taken together, these results suggest that our optimization model can identify a range of efficient alternatives for healthcare systems designing a network of EDs across multiple hospitals.

Optimization design of drive system for industrial robots based on dynamic performance

2017 ◽  
Vol 44 (6) ◽  
pp. 765-775 ◽  
Author(s):  
LianZheng Ge ◽  
Jian Chen ◽  
Ruifeng Li ◽  
Peidong Liang
Keyword(s):  
Optimization Model ◽  
Optimization Problem ◽  
Industrial Robot ◽  
Dynamic Performance ◽  
System Optimization ◽  
Drive System ◽  
Industrial Robots ◽  
Mixed Integer ◽  
Light Weight ◽  
Content Type

Purpose The global performance of industrial robots partly depends on the properties of drive system consisting of motor inertia, gearbox inertia, etc. This paper aims to deal with the problem of optimization of global dynamic performance for robotic drive system selected from available components. Design/methodology/approach Considering the performance specifications of drive system, an optimization model whose objective function is composed of working efficiency and natural frequency of robots is proposed. Meanwhile, constraints including the rated and peak torque of motor, lifetime of gearbox and light-weight were taken into account. Furthermore, the mapping relationship between discrete optimal design variables and component properties of drive system were presented. The optimization problem with mixed integer variables was solved by a mixed integer-laplace crossover power mutation algorithm. Findings The optimization results show that our optimization model and methods are applicable, and the performances are also greatly promoted without sacrificing any constraints of drive system. Besides, the model fits the overall performance well with respect to light-weight ratio, safety, cost reduction and others. Practical implications The proposed drive system optimization method has been used for a 4-DOF palletizing robot, which has been largely manufactured in a factory. Originality/value This paper focuses on how the simulation-based optimization can be used for the purpose of generating trade-offs between cost, performance and lifetime when designing robotic drive system. An applicable optimization model and method are proposed to handle the dynamic performance optimization problem of a drive system for industrial robot.

Optimizing Disruption Tolerance for Rail Transit Networks Under Uncertainty

2021 ◽  
Author(s):  
Lei Xu ◽  
Tsan Sheng (Adam) Ng ◽  
Alberto Costa
Keyword(s):  
Robust Optimization ◽  
Optimization Model ◽  
Optimization Problem ◽  
Mixed Integer ◽  
Rail Transit ◽  
Distributionally Robust Optimization ◽  
Performance Function ◽  
Transit Networks ◽  
Disruption Tolerance ◽  
Distributionally Robust

In this paper, we develop a distributionally robust optimization model for the design of rail transit tactical planning strategies and disruption tolerance enhancement under downtime uncertainty. First, a novel performance function evaluating the rail transit disruption tolerance is proposed. Specifically, the performance function maximizes the worst-case expected downtime that can be tolerated by rail transit networks over a family of probability distributions of random disruption events given a threshold commuter outflow. This tolerance function is then applied to an optimization problem for the planning design of platform downtime protection and bus-bridging services given budget constraints. In particular, our implementation of platform downtime protection strategy relaxes standard assumptions of robust protection made in network fortification and interdiction literature. The resulting optimization problem can be regarded as a special variation of a two-stage distributionally robust optimization model. In order to achieve computational tractability, optimality conditions of the model are identified. This allows us to obtain a linear mixed-integer reformulation that can be solved efficiently by solvers like CPLEX. Finally, we show some insightful results based on the core part of Singapore Mass Rapid Transit Network.

scholarly journals MP20: Evaluation of the disruptors during advanced life support in emergency departments

CJEM ◽  
2020 ◽  
Vol 22 (S1) ◽  
pp. S49-S49
Author(s):  
J. Truchot ◽  
D. Michelet ◽  
D. Drummond ◽  
P. Plaisance
Keyword(s):  
Emergency Departments ◽  
Exploratory Study ◽  
Online Survey ◽  
Life Support ◽  
Medical Curriculum ◽  
Real Life ◽  
Advanced Life Support ◽  
Cross Sectional ◽  
Phone Calls ◽  
Ed Overcrowding

Introduction: Simulation is used as a teaching technique in the medical curriculum, and especially for advanced life support (ALS). However, simulated ALS can differ greatly from real life ALS. The aim of this exploratory study was to identify the different disruptors associated with real life ALS. Methods: We conducted a cross-sectional, anonymous, online survey that included 32 items. It was distributed by email to emergency physicians from five emergency departments in Paris. The aim of this online survey was to identify the elements perceived as disruptors during ALS. Other aspects of the survey explored the perceived differences between simulated ALS and real life ALS. Descriptive statistics of percentage, mean and standard deviation were used to analyse the data. Results: Among 100 surveyed physicians, 43 (43%) answers were analysed. 53% were women with a mean age of 32 ± 3 years old. The identified disruptors from real life ALS were task interruptions mainly from non-medical staff (n = 16; 37%), patient's siblings (n = 5; 12%), other specialists (n = 5; 12%) and the phone calls (n = 2; 5%). The situation of ED overcrowding (n = 12; 28%) was also mentioned as a potential disruptor. Overall, physicians reported that some technical and non-technical tasks were harder to perform in real life compared to simulated sessions. Conclusion: This exploratory study allowed the identification of disruptors encountered in real life cases of ALS, and may be used for future simulation-based teaching to enhance realism during sessions

scholarly journals Multi-Objective Optimization Model for P + R and K + R Facilities’ Collaborative Layout Decision

2020 ◽  
Vol 12 (21) ◽  
pp. 8833
Author(s):  
Wei Wang ◽  
Zhentian Sun ◽  
Zhiyuan Wang ◽  
Yue Liu ◽  
Jun Chen
Keyword(s):  
Optimization Model ◽  
Road Traffic ◽  
Supply And Demand ◽  
Optimal Decision ◽  
Multi Objective Optimization ◽  
Service Capacity ◽  
Location Selection ◽  
Multi Objective ◽  
Proposed Model ◽  
Collaborative Decision

In order to reduce the pressure on urban road traffic, multi-modal travel is gradually replacing single-modal travel. Park and ride (P + R) and kiss and ride (K + R) are effective methods to integrate car transportation and rail transit. However, there is often an imbalance between supply and demand in existing car occupant transfer facilities, which include both P + R and K + R facilities. Therefore, we aim to conduct a research on P + R and K + R facilities’ collaborative decision. It first classifies car occupant transfer facilities into types and levels and sets the service capacity of each category. On the premise of ensuring the occupancy of parking spaces, our model aims to maximize the intercepted vehicle mileage and transfer utility and establishes an optimal decision model for car occupant transfer facilities. The model collaboratively decides the facilities in terms of location selection, layout arrangement, and overflow demand conversion to balance the supply and demand. We choose Chengdu as an example, apply the multi-objective optimization model of car occupant transfer facilities, give improved schemes, and further explore the influence of the quantity of facilities on the optimization objectives. The results show that the scheme obtained by the proposed model is significantly better than the existing scheme.

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