scholarly journals An Analytical Optimisation Framework for Airport Terminal Capacity Expansion

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Sultan Alodhaibi ◽  
Robert L. Burdett ◽  
Prasad K. D. V. Yarlagadda
Keyword(s):  
Resource Allocation ◽  
Simulated Annealing ◽  
Waiting Times ◽  
Discrete Event ◽  
Event Simulation ◽  
Numerical Testing ◽  
Physical Resources ◽  
Airport Terminal ◽  
Airport Terminals ◽  
Random Approach

This article considers how to allocate additional physical resources within airport terminals. An optimization model was developed to determine where additional resources should be placed to minimise passenger waiting times. The objective function is stochastic and can only be evaluated using discrete event simulation. As this model is stochastic and nonlinear, a Simulated Annealing (SA) metaheuristic was implemented and tested. The SA algorithm repeatedly perturbs a resource allocation solution using one of two methods. The first method is creating new solution randomly in each iteration, and the second method is local search that is mimicked by any move of the current solution of x solution chosen randomly in its neighborhood. Numerical testing shows that the random approach is best, and solutions that are 12.11% better can be obtained.

scholarly journals Simulation modeling and analysis of primary health center operations

SIMULATION ◽  
2021 ◽  
pp. 003754972110309
Author(s):  
Mohd Shoaib ◽  
Varun Ramamohan
Keyword(s):  
Resource Utilization ◽  
Waiting Times ◽  
Discrete Event ◽  
Simulation Models ◽  
Modeling And Analysis ◽  
Healthcare Facilities ◽  
Event Simulation ◽  
Analytical Approximations ◽  
Primary Health ◽  
Generic Modeling

We present discrete-event simulation models of the operations of primary health centers (PHCs) in the Indian context. Our PHC simulation models incorporate four types of patients seeking medical care: outpatients, inpatients, childbirth cases, and patients seeking antenatal care. A generic modeling approach was adopted to develop simulation models of PHC operations. This involved developing an archetype PHC simulation, which was then adapted to represent two other PHC configurations, differing in numbers of resources and types of services provided, encountered during PHC visits. A model representing a benchmark configuration conforming to government-mandated operational guidelines, with demand estimated from disease burden data and service times closer to international estimates (higher than observed), was also developed. Simulation outcomes for the three observed configurations indicate negligible patient waiting times and low resource utilization values at observed patient demand estimates. However, simulation outcomes for the benchmark configuration indicated significantly higher resource utilization. Simulation experiments to evaluate the effect of potential changes in operational patterns on reducing the utilization of stressed resources for the benchmark case were performed. Our analysis also motivated the development of simple analytical approximations of the average utilization of a server in a queueing system with characteristics similar to the PHC doctor/patient system. Our study represents the first step in an ongoing effort to establish the computational infrastructure required to analyze public health operations in India and can provide researchers in other settings with hierarchical health systems, a template for the development of simulation models of their primary healthcare facilities.

scholarly journals Balancing scarce hospital resources during the COVID-19 pandemic using discrete-event simulation

2021 ◽  
Author(s):  
G.J. Melman ◽  
A.K. Parlikad ◽  
E.A.B. Cameron
Keyword(s):  
Resource Allocation ◽  
Discrete Event Simulation ◽  
Elective Surgery ◽  
Discrete Event ◽  
Specific Patient ◽  
Event Simulation ◽  
The Impact ◽  
Allocation Decisions ◽  
Allocation Strategies ◽  
Resource Allocation Decisions

AbstractCOVID-19 has disrupted healthcare operations and resulted in large-scale cancellations of elective surgery. Hospitals throughout the world made life-altering resource allocation decisions and prioritised the care of COVID-19 patients. Without effective models to evaluate resource allocation strategies encompassing COVID-19 and non-COVID-19 care, hospitals face the risk of making sub-optimal local resource allocation decisions. A discrete-event-simulation model is proposed in this paper to describe COVID-19, elective surgery, and emergency surgery patient flows. COVID-19-specific patient flows and a surgical patient flow network were constructed based on data of 475 COVID-19 patients and 28,831 non-COVID-19 patients in Addenbrooke’s hospital in the UK. The model enabled the evaluation of three resource allocation strategies, for two COVID-19 wave scenarios: proactive cancellation of elective surgery, reactive cancellation of elective surgery, and ring-fencing operating theatre capacity. The results suggest that a ring-fencing strategy outperforms the other strategies, regardless of the COVID-19 scenario, in terms of total direct deaths and the number of surgeries performed. However, this does come at the cost of 50% more critical care rejections. In terms of aggregate hospital performance, a reactive cancellation strategy prioritising COVID-19 is no longer favourable if more than 7.3% of elective surgeries can be considered life-saving. Additionally, the model demonstrates the impact of timely hospital preparation and staff availability, on the ability to treat patients during a pandemic. The model can aid hospitals worldwide during pandemics and disasters, to evaluate their resource allocation strategies and identify the effect of redefining the prioritisation of patients.

scholarly journals Potentials of possible machine systems for directly loading logs in cut-to-length harvesting

2012 ◽  
Vol 42 (5) ◽  
pp. 970-985 ◽  
Author(s):  
Ola Ringdahl ◽  
Thomas Hellström ◽  
Ola Lindroos
Keyword(s):  
Fuel Consumption ◽  
Waiting Times ◽  
Discrete Event ◽  
Lead Times ◽  
Event Simulation ◽  
Variable Environments ◽  
Landing Sites ◽  
Machine Systems ◽  
Stand Conditions ◽  
Key Innovations

In conventional mechanized cut-to-length systems, a harvester fells and cuts trees into logs that are stored on the ground until a forwarder picks them up and carries them to landing sites. A proposed improvement is to place logs directly into the load spaces of transporting machines as they are cut. Such integrated loading could result in cost reductions, shorter lead times from stump to landing, and lower fuel consumption. However, it might also create waiting times for the machines involved, whereas multifunctional machines are likely to be expensive. Thus, it is important to analyze whether or not the advantages of any changes outweigh the disadvantages. The conventional system was compared with four potential systems, including two with autonomous forwarders, using discrete-event simulation with stochastic elements in which harvests of more than 1000 final felling stands (containing in total 1.6 million m3) were simulated 35 times per system. The results indicate that harwarders have substantial potential (less expensive on ≥80% of the volume and fuel consumption decreased by ≥18%) and may become competitive if key innovations are developed. Systems with cooperating machines have considerably less potential, limited to very specific stand conditions. The results conform with expected difficulties in integrating processing and transporting machines’ work in variable environments.

Discrete Event Simulation Of The COVID-19 Sample Collection Point Operation

2021 ◽  
Author(s):  
Martina Kuncova ◽  
Katerina Svitkova ◽  
Alena Vackova ◽  
Milena Vankova
Keyword(s):  
Discrete Event Simulation ◽  
Waiting Times ◽  
Discrete Event ◽  
Real Data ◽  
Simulation Software ◽  
Sample Collection ◽  
The Real ◽  
Event Simulation ◽  
Collection Process ◽  
Collection Point

The year 2020 was very challenging for everyone due to the COVID-19 pandemic. Many people turn their lives upside down from day to day. Politicians had to impose completely unprecedented measures, and doctors immediately had to adapt to the huge influx of patients and the massive demand for testing. Of course, not all processes could be planned completely efficiently, given that the situation literally changes from minute to minute, but sometimes better planning could improve the real processes. This contribution deals with the application of simulation software SIMUL8 to the analysis of the COVID-19 sample collection process in a drive-in point in a hospital. The main aim is to create a model based on the real data and then to find out the suitable number of other staff (medics) helping a doctor during the process to decrease the number of unattended patients and their waiting times.

scholarly journals Analysis of the Installed Productive Capacity in a Medical Angiography Room through Discrete Event Simulation

Processes ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. 660
Author(s):  
Félix Badilla-Murillo ◽  
Bernal Vargas-Vargas ◽  
Oscar Víquez-Acuña ◽  
Justo García-Sanz-Calcedo
Keyword(s):  
Resource Utilization ◽  
Discrete Event Simulation ◽  
Current System ◽  
Waiting Times ◽  
Continuous Process ◽  
Discrete Event ◽  
Computer Software ◽  
Productive Capacity ◽  
Continuous Process Improvement ◽  
Event Simulation

The installed productive capacity of a healthcare center’s equipment limits the efficient use of its resources. This paper, therefore, analyzes the installed productive capacity of a hospital angiography room and how to optimize patient demand. For this purpose, a Discrete Event Simulation (DES) model based on historical variables from the current system was created using computer software. The authors analyzed 2044 procedures performed between 2014 and 2015 in a hospital in San José, Costa Rica. The model was statistically validated to determine that it does not significantly differ from the current system, considering the DMAIC stages for continuous process improvement. In the current scenario, resource utilization is 0.99, and the waiting list increases every month. The results showed that the current capacity of the service could be doubled, and that resource utilization could be reduced to 0.64 and waiting times by 94%. An increase in service efficiency could be achieved by shortening maximum waiting times from 6.75 days to 3.70 h. DES simulation, therefore, allows optimizing of the use of healthcare systems’ resources and hospital management.

scholarly journals Employment of Emergency Advanced Nurses of Turkey: A Discrete-Event Simulation Application

Processes ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 48 ◽  
Author(s):  
Abdulkadir Atalan ◽  
Cem Donmez
Keyword(s):  
Length Of Stay ◽  
Discrete Event Simulation ◽  
Emergency Services ◽  
Treatment Time ◽  
Waiting Times ◽  
Discrete Event ◽  
Weekly Basis ◽  
Event Simulation ◽  
Number Of Patients ◽  
Efficiency Rate

In the present study, problems in emergency services (ESs) were dealt with by analyzing the working system of ESs in Turkey. The purpose of this study was to reduce the waiting times spent in hospitals by employing advanced nurses (ANs) to treat patients who are not urgent, or who may be treated as outpatients in ESs. By applying discrete-event simulation on a 1/24 (daily) and 7/24 (weekly) basis, and by employing ANs, it was determined that the number of patients that were treated increased by 26.71% on a 1/24 basis, and by 15.13% on a 7/24 basis. The waiting time that was spent from the admission to the ES until the treatment time decreased by 38.67% on a 1/24 basis and 53.66% on a 24/7 basis. Similarly, the length of stay was reduced from 82.46 min to 53.97 min in the ES. Among the findings, it was observed that the efficiency rate of the resources was balanced by the employment of ANs, although it was not possible to obtain sufficient efficiency from the resources used in the ESs prior to the present study.

scholarly journals Using Metaheuristic and Fuzzy System for the Optimization of Material Pull in a Push-Pull Flow Logistics Network

2013 ◽  
Vol 2013 ◽  
pp. 1-19 ◽  
Author(s):  
Afshin Mehrsai ◽  
Hamid-Reza Karimi ◽  
Klaus-Dieter Thoben ◽  
Bernd Scholz-Reiter
Keyword(s):  
Fuzzy System ◽  
Waiting Times ◽  
Discrete Event ◽  
Hybrid Approach ◽  
Logistics Network ◽  
Event Simulation ◽  
Logistics Networks ◽  
Alternative Material ◽  
Flows In Networks ◽  
Push And Pull

Alternative material flow strategies in logistics networks have crucial influences on the overall performance of the networks. Material flows can follow push, pull, or hybrid systems. To get the advantages of both push and pull flows in networks, the decoupling-point strategy is used as coordination mean. At this point, material pull has to get optimized concerning customer orders against pushed replenishment-rates. To compensate the ambiguity and uncertainty of both dynamic flows, fuzzy set theory can practically be applied. This paper has conceptual and mathematical parts to explain the performance of the push-pull flow strategy in a supply network and to give a novel solution for optimizing the pull side employing Conwip system. Alternative numbers of pallets and their lot-sizes circulating in the assembly system are getting optimized in accordance with a multi-objective problem; employing a hybrid approach out of meta-heuristics (genetic algorithm and simulated annealing) and fuzzy system. Two main fuzzy sets as triangular and trapezoidal are applied in this technique for estimating ill-defined waiting times. The configured technique leads to smoother flows between push and pull sides in complex networks. A discrete-event simulation model is developed to analyze this thesis in an exemplary logistics network with dynamics.

scholarly journals Discrete event simulation for problem solving in the context of an emergency department

2020 ◽  
Vol 11 (5) ◽  
pp. 1515
Author(s):  
Letícia Ali Figueiredo Ferreira ◽  
Igor Leão dos Santos ◽  
Ana Carla De Souza Gomes dos Santos ◽  
Augusto Da Cunha Reis
Keyword(s):  
Length Of Stay ◽  
Discrete Event Simulation ◽  
Waiting Times ◽  
Discrete Event ◽  
Saturation Point ◽  
Event Simulation ◽  
Emergency Care System ◽  
Patient Arrival ◽  
The Subject

Emergency departments (ED) are responsible for the immediate care and stabilization of patients in critical health conditions. Several factors have caused overcrowding in the emergency care system, but the variability of patient arrival and the triage process requires special attention. The criticality of these components and their configuration directly impact the waiting times, length of stay and quality of service, being the subject of several studies. So, this paper aims to understand by means of Discrete Event Simulation how ED works with the variation of patient arrival and how this variation highlights the bottlenecks of the triage process. Varying the patient arriving interval between 0.1 and 7.6 in a 4-hour scenario,  the system saturation point was established in β = 1.1. Besides, with the variation in the number of triages points, a considerable decrease in the total length of stay spent and the waiting times were noticed, mainly when there was two triage points operating simultaneously.

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