scholarly journals Enhanced coverage by integrating site interdependencies in capacitated EMS location models

2021 ◽  
Author(s):  
Matthias Grot ◽  
Tristan Becker ◽  
Pia Mareike Steenweg ◽  
Brigitte Werners
Keyword(s):  
Discrete Event ◽  
Mathematical Formulation ◽  
Optimization Approach ◽  
Solution Quality ◽  
Similar System ◽  
Event Simulation ◽  
Simulation Based ◽  
Average System ◽  
Simplifying Assumptions ◽  
A Site

AbstractIn order to allocate limited resources in emergency medical services (EMS) networks, mathematical models are used to select sites and their capacities. Many existing standard models are based on simplifying assumptions, including site independency and a similar system-wide busyness of ambulances. In practice, when a site is busy, a call is forwarded to another site. Thus, the busyness of each site depends not only on the rate of calls in the surrounding area, but also on interactions with other facilities. If the demand varies across the urban area, assuming an average system-wide server busy fraction may lead to an overestimation of the actual coverage. We show that site interdependencies can be integrated into the well-known Maximum Expected Covering Location Problem (MEXCLP) by introducing an upper bound for the busyness of each site. We apply our new mathematical formulation to the case of a local EMS provider. To evaluate the solution quality, we use a discrete event simulation based on anonymized real-world call data. Results of our simulation-optimization approach indicate that the coverage can be improved in most cases by taking site interdependencies into account, leading to an improved ambulance allocation and a faster emergency care.

Abstract 2524: Increasing the Proportion of Patients Treated with Stroke Thrombolysis: Reducing In-hospital Delays has Substantially More Impact than Extension of the Time Window

Stroke ◽  
2012 ◽  
Vol 43 (suppl_1) ◽  
Author(s):  
Martin A James ◽  
Thomas Monks ◽  
Ken Stein ◽  
Martin Pitt
Keyword(s):  
Time Window ◽  
Discrete Event ◽  
Fold Increase ◽  
Service Improvement ◽  
Disability Benefit ◽  
Prospective Data ◽  
Stroke Service ◽  
Event Simulation ◽  
Simulation Based ◽  
The Impact

Background Pooled analyses show the benefit of IV alteplase for ischemic stroke up to 4·5 hours after onset, and expert guidelines have been updated to reflect this. However, the benefit from thrombolysis is critically time-dependent, and the additional benefit from extending the time window may be jeopardised by in-hospital delays. Methods We developed a discrete-event simulation based on prospective data from 1142 acute stroke patients arriving at our large district hospital over a two-year period to April 2011, modelling the time spent in the ED for triage and assessment, brain imaging and, if applicable, thrombolysis. Outputs from the model included arrival to treatment times (ATT), percentage of strokes thrombolysed, and the number of thrombolysed patients with a 90 day modified Rankin Scale (mRS) of 0-1. We sought to model the current stroke pathway (treatment <3 hours of onset), and compare it with developmental scenarios exploring the impact of extending treatment from 3 to 4.5 hours, of ED staff alerting the stroke service at triage, of ambulance pre-alert to the stroke service, and combinations of these measures. Results The model illustrates that extending the treatment window modestly increases the percentage of acute strokes thrombolysed, from 5% to 6% (95% CI 5.8-6.1%), and increases the number of thrombolysed patients with mRS 0-1 by 7 per year (95% CI 5.9-8.0). Both the triage alert and ambulance pre-alert scenarios increase thrombolysis rates to 15% (95% CI 14.9% to 15.7%); but the ambulance pre-alert reduces ATT by a mean of 27 mins (95% CI 26.3-28.4) compared to the triage alert scenario. The ambulance pre-alert scenario increases the number of thrombolysed patients with mRS 0-1 by 35/year (95% CI 32.9-37.7) compared to 22 (95% CI 20.4-23.5) in the triage alert scenario. Combining the treatment extension with either alerting measure does not increase the thrombolysis rate further (15%, 95% CI 14.7-15.1%). Sensitivity analysis illustrates that the pre-alert system is the least vulnerable to a drop in compliance rates. Conclusions Our simulation model shows that the greatest disability benefit accrues from measures to substantially reduce in-hospital delays to alteplase treatment - a potential three-fold increase in the proportion of patients treated. Compared to extending the time window for alteplase from 3 to 4.5 hours, eradicating in-hospital delays to treatment offers a five-fold greater disability benefit, and this should be the pre-eminent focus of service improvement for all emergency receiving hospitals.

scholarly journals A Simulation-Based Tool to Support Decision-Making in Logistics Design of a Can Packaging Line

2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Victoria G. Achkar ◽  
Valentina Bär ◽  
Franco Cornú ◽  
Carlos A. Méndez
Keyword(s):  
Decision Making ◽  
Material Handling ◽  
Discrete Event ◽  
Inventory Systems ◽  
Dynamic Interactions ◽  
Event Simulation ◽  
Proposed Model ◽  
Simulation Based ◽  
And Performance ◽  
Support Decision Making

AbstractThis study proposes an advanced discrete-event simulation-based tool to support decision-making in the internal logistic design of a packaging line of a multinational brewery company. The selected software, Simio, allows emulating, advising and predicting the behavior of complex real-world systems. The simulation model provides a 3D interface that facilitates verification and validation. In this work, the designed model is used to understand the dynamic interactions between multiple factors and performance measures including both material-handling and inventory systems and to define necessary quantities and/or capacities of resources for a future can packaging line. Based on the proposed model, a what-if analysis is performed to determine inventory threshold values and other critical variables in order to optimize the configuration of internal logistics in potential scenarios.

scholarly journals Multi Equipment Condition Based Maintenance Optimization Using Multi-Objective Evolutionary Algorithms

2019 ◽  
Vol 9 (22) ◽  
pp. 4849
Author(s):  
Aitor Goti ◽  
Aitor Oyarbide-Zubillaga ◽  
Ana Sanchez ◽  
Tugce Akyazi ◽  
Elisabete Alberdi
Keyword(s):  
Preventive Intervention ◽  
Discrete Event ◽  
Condition Based Maintenance ◽  
Optimization Approach ◽  
Actual System ◽  
Multi Objective ◽  
Event Simulation ◽  
Industrial Chain ◽  
Efficiency And Effectiveness ◽  
Optimal Values

Thanks to the digitalization of industry, maintenance is a trending topic. The amount of data available for analyses and optimizations in this field has increased considerably. In addition, there are more and more complex systems to maintain, and to keep all these devices in proper conditions, which requires maintenance management to gain efficiency and effectiveness. Within maintenance, Condition-Based Maintenance (CBM) programs can provide significant advantages, but often these programs are complex to manage and understand. The problem becomes more complex when equipment is analyzed in the context of a plant, where equipment can be more or less saturated, critical regarding quality, etc. Thus, this paper focuses on CBM optimization of a full industrial chain, with the objective of determining its optimal values of preventive intervention limits for equipment under economic criteria. It develops a mathematical plus discrete-event-simulation based model that takes the evolution in quality and production speed into consideration as well as condition based, corrective and preventive maintenance. The optimization process is performed using a Multi-Objective Evolutionary Algorithm. Both the model and the optimization approach are applied to an industrial case, where the data gathered by the IoT (Internet of Things) devices at edge level can detect when some premises of the CBM model are no longer valid and request a new simulation. The simulation performed in a centralized way can thus obtain new optimal values who fit better to the actual system than the existing ones. Finally, these new optimal values can be transferred to the model whenever it is necessary. The approach developed has raised the interest of a partner of the Deusto Digital Industry Chair.

scholarly journals Speeding Up Decision Support

2010 ◽  
pp. 255-273
Author(s):  
Navonil Mustafee ◽  
Simon J.E. Taylor ◽  
Korina Katsaliaki ◽  
Sally Brailsford
Keyword(s):  
Decision Support ◽  
Distributed Simulation ◽  
Discrete Event ◽  
Event Simulation ◽  
Healthcare Needs ◽  
Simulation Based ◽  
Long Time ◽  
Speed Up ◽  
Complex Health Problems ◽  
The Uk

Discrete-Event Simulation (DES) is a decision support technique that allows stakeholders to conduct experiments with models that represent real-world systems of interest. Its use in healthcare is comparatively new. Healthcare needs have grown and healthcare organisations become larger, more complex and more costly. There has never been a greater need for carefully informed decisions and policy. DES is valuable as it can provide evidence of how to cope with these complex health problems. However, the size of a healthcare system can lead to large models that can take an extremely long time to simulate. In this chapter the authors investigate how a technique called distributed simulation allows us to use multiple computers to speed up this simulation. Based on a case study of the UK National Blood Service they demonstrate the effectiveness of this technique and argue that it is a vital technique in healthcare informatics with respect to supporting decision making in large healthcare systems.

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