scholarly journals Staffing and Capacity Planning for SARS-CoV-2 Monoclonal Antibody Infusion Facilities: A Performance Estimation Calculator based on Discrete-Event Simulations

2021 ◽  
Author(s):  
Caglar Caglayan ◽  
Jonathan Thornhill ◽  
Miles A. Stewart ◽  
Anastasia S. Lambrou ◽  
Donald Richardson ◽  
...  
Keyword(s):  
Monoclonal Antibody ◽  
Capacity Planning ◽  
Severe Disease ◽  
Discrete Event ◽  
Decision Support Tool ◽  
Performance Estimation ◽  
Infusion Time ◽  
Support Tool ◽  
Discrete Event Simulations ◽  
Field Team

Objective: The COVID-19 pandemic has significantly stressed healthcare systems. The addition of monoclonal antibody (mAb) infusions, which prevent severe disease and reduce hospitalizations, to the repertoire of COVID-19 countermeasures offers the opportunity to reduce system stress but requires strategic planning and use of novel approaches. Our objective was to develop a web-based decision-support tool to help existing and future mAb infusion facilities make better and more informed staffing and capacity decisions. Materials and Methods: Using real-world observations from three medical centers operating with federal field team support, we developed a discrete-event simulation model and performed simulation experiments to assess performance of mAb infusion sites under different conditions. Results: 162,000 scenarios were evaluated by simulations. Our analyses revealed that it was more effective to add check-in staff than to add additional nurses for middle-to-large size sites with ≥ 2 infusion nurses; that scheduled appointments performed better than walk-ins when patient load was not high; and that reducing infusion time was particularly impactful when load on resources was only slightly above manageable levels. Discussion: Physical capacity, check-in staff, and infusion time were as important as nurses for mAb sites. Health systems can effectively operate an infusion center under different conditions to provide mAb therapeutics even with relatively low investments in physical resources and staff. Conclusion: Simulations of mAb infusion sites were used to create a capacity planning tool to optimize resource utility and allocation in constrained pandemic conditions, and more efficiently treat COVID-19 patients at existing and future mAb infusion sites.

scholarly journals Modeling and simulation of customer dissatisfaction, worker unpunctuality and tolerance to delay in make-to-order supply chains measured through customer lifetime value performance

2018 ◽  
Vol 22 (2) ◽  
Author(s):  
Jorge Andrés Alvarado Valencia ◽  
Daniel Silva
Keyword(s):  
Word Of Mouth ◽  
Discrete Event ◽  
Decision Support Tool ◽  
Customer Lifetime Value ◽  
Negative Word ◽  
Support Tool ◽  
Make To Order ◽  
Customer Lifetime ◽  
Lifetime Value

Introduction: We developed a model for a make-to-order supply chain to evaluate the effects of worker unpunctuality, tolerance to delay and word-of-mouth according  to customer waiting time (dis)satisfaction in four customer lifetime value measures (CLTV): switching customers, the number of sales per customer, the average customer loyalty and the potential market reached.  Methods: We developed a hybrid (agent-based and discrete-event) simulation in a 33 * 4 experimental design. Results: All of the variables were significant in the four CLTV measures, except for tolerance to delay. The positive word-of-mouth effect was greater than the negative word-of-mouth effect. There were significant interactions between positive and negative word-of-mouth.  Conclusions:  This type of model becomes a decision support tool for businesses to evaluate their mid-to-long term performance taking into account their customers’ long-term behaviors and the relationships between potential customers in repetitive and competitive environments.

scholarly journals Framework for Operational Performance Measurements in Small and Medium Scale Industries Using Discrete Event Simulation Approach

2018 ◽  
Vol 8 (4) ◽  
pp. 3103-3107
Author(s):  
B. O. Odedairo ◽  
N. Nwabuokei
Keyword(s):  
Discrete Event Simulation ◽  
Discrete Event ◽  
Decision Support Tool ◽  
Simulation Approach ◽  
Case Scenario ◽  
Worst Case ◽  
Support Tool ◽  
Event Simulation ◽  
Work In Process ◽  
The Status

Globally, production systems must cope with limitations arising from variabilities and complexities due to globalization and technological advancements. To survive in spite of these challenges, critical process measures need to be closely monitored to ensure improved system performance. For production managers, the availability of accurate measurements which depict the status of production activities in real time is desired. This study is designed to develop an operational data decision support tool (ODATA-DST) using discrete event simulation approach. The work-in-process and processing time of each workstation/buffer station in a bottled water production system were investigated. The status of each job as they move through the system was used to simulate a routing matrix. The production output data for 50cl and 75cl product from 2014-2016 were collected. A mathematical model for routing jobs from the point of arrival to the point of departure was developed using discrete event simulation. A graphical user interface (GUI) was designed based on the factory’s performance measurement algorithm. Simulating the factory’s work-in-process with respect to internal benchmarks yielded a cycle time of 4.4, 6.23, 5.04 and throughput of 0.645, 0.455, 0.637 for best case scenario, worst case scenario and practical worst case scenario respectively. The factory performed below the simulated benchmark at 26%, 28%, 28% for the 50cl and at 51%, 54%, 59% for 75cl regarding the year 2014, 2015 and 2017 respectively. Performance measurement decision support tool has been developed to enhance the production manager’s decision making capability. The tool can improve production data analysis and performance predictions.

scholarly journals An Integrated Environment–Cost–Time Optimisation Method for Construction Contractors Considering Global Warming

2018 ◽  
Vol 10 (11) ◽  
pp. 4207 ◽  
Author(s):  
Kailun Feng ◽  
Weizhuo Lu ◽  
Shiwei Chen ◽  
Yaowu Wang
Keyword(s):  
Discrete Event ◽  
Environmentally Friendly ◽  
Decision Support Tool ◽  
Vital Role ◽  
Support Tool ◽  
Integrated Method ◽  
Event Simulation ◽  
The Times ◽  
Construction Plans ◽  
Environment Cost

Construction contractors play a vital role in reducing the environmental impacts during the construction phase. To mitigate these impacts, contractors need to develop environmentally friendly plans that have optimal equipment, materials and labour configurations. However, construction plans with optimal environment may negatively affect the project cost and duration, resulting in dilemma for contractors on adopting low impacts plans. Moreover, the enumeration method that is usually used needs to assess and compare the performances of a great deal of scenarios, which seems to be time consuming for complicated projects with numerous scenarios. This study therefore developed an integrated method to efficiently provide contractors with plans having optimal environment–cost–time performances. Discrete-event simulation (DES) and particle swarm optimisation algorithms (PSO) are integrated through an iterative loop, which remarkably reduces the efforts on optimal scenarios searching. In the integrated method, the simulation module can model the construction equipment and materials consumption; the assessment module can evaluate multi-objective performances; and the optimisation module fast converges on optimal solutions. A prototype is developed and implemented in a hotel building construction. Results show that the proposed method greatly reduced the times of simulation compared with enumeration method. It provides the contractor with a trade-off solution that can average reduce 26.9% of environmental impact, 19.7% of construction cost, and 10.2% of project duration. The method provides contractors with an efficient and practical decision support tool for environmentally friendly planning.

Modeling and simulation of customer dissatisfaction in waiting lines and its effects

SIMULATION ◽  
2016 ◽  
Vol 93 (2) ◽  
pp. 91-101 ◽  
Author(s):  
Jorge A Alvarado-Valencia ◽  
Gabriela C Tueti Silva ◽  
Jairo R Montoya-Torres
Keyword(s):  
Simulation Analysis ◽  
Discrete Event ◽  
Decision Support Tool ◽  
Business Decisions ◽  
Support Tool ◽  
Agent Based ◽  
Proposed Model ◽  
Customer Dissatisfaction ◽  
Parameter Values ◽  
Waiting Lines

A combination of discrete-event and agent-based simulation analysis using a field-tested psychological model for evaluating the effects of customer dissatisfaction in waiting lines beyond balking and reneging was developed. The proposed model assessed the effects that different psychological parameter values and business decisions in waiting lines have on the evaluation of waiting and, therefore, in customer satisfaction and mid-term profit. This model hence becomes a decision-support tool for businesses wanting to model their costs of customer dissatisfaction due to waiting lines in repetitive and competitive environments.

scholarly journals A Simulation-Based Decision Support Tool for Arctic Transit Transport

2015 ◽  
Author(s):  
Bernhard Schartmüller ◽  
Aleksandar-Saša Milaković ◽  
Martin Bergström ◽  
Sören Ehlers
Keyword(s):  
Decision Support ◽  
Discrete Event Simulation ◽  
Scale Effects ◽  
Discrete Event ◽  
Open Water ◽  
Decision Support Tool ◽  
Comparative Case Study ◽  
Support Tool ◽  
Event Simulation ◽  
Simulation Based

The Russian Federation attempts to foster the Northern Sea Route (NSR) as a transport alternative to the current Suez Canal Route (SCR). Therefore, this paper seeks to identify under which conditions the use of the NSR is economically feasible. To evaluate this in a realistic way it is essential to take the significant uncertainty of input variables like ice data predictions into account. For that reason a simulation-based decision-support (SBDS)-tool based on a discrete-event simulation model is developed. The SBDS-tool requires as input vessel dimensions, available power and information about the route(s) including waypoints and ice data. It calculates then the general performance of the vessel in both open water and ice. Next it generates day-specific ice conditions according to a probability distribution between lower and upper limit obtained from satellite measurements. Based on this and the previously calculated vessels’ performance the SBDS-tool calculates day-specific transit times and fuel consumptions for examined time period. This is then used as input for a discrete-event simulation to assess the number of roundtrips, transported cargo and fuel consumption for joint use of different routes, dependent on the predefined operational days along the routes. The obtained results are then used to calculate the cost per transported cargo unit between two ports and to assess the sensitivity in order to determine if an economically advantageous and robust transport system can be achieved. In addition, possible economy of scale effects using larger vessels can be evaluated. In order to show the applicability of the developed model a comparative case study for three container vessels operating between Rotterdam (NL) and Yokohama (JP) is carried out.

ForeSim-BI: A predictive analytics decision support tool for capacity planning

2020 ◽  
Vol 131 ◽  
pp. 113266 ◽  
Author(s):  
Duarte Dinis ◽  
Ângelo Palos Teixeira ◽  
Ana Barbosa-Póvoa
Keyword(s):  
Decision Support ◽  
Capacity Planning ◽  
Predictive Analytics ◽  
Decision Support Tool ◽  
Support Tool

Automation of input data to discrete event simulation for manufacturing: A review

2016 ◽  
Vol 07 (01) ◽  
pp. 1630001 ◽  
Author(s):  
Panagiotis Barlas ◽  
Cathal Heavey
Keyword(s):  
Discrete Event Simulation ◽  
Input Data ◽  
Manufacturing Industry ◽  
Discrete Event ◽  
Decision Support Tool ◽  
Future Research ◽  
Support Tool ◽  
Event Simulation ◽  
Manufacturing Applications ◽  
The Cost

Discrete event simulation (DES) is a well-established decision support tool in modeling work flows in manufacturing industry. But, there are an amount of practical and financial obstacles that deter the employment of this technology in industry. One of the main weaknesses of operating DES is the costs spent on collecting and mapping input data from different enterprise data resources into a DES model. Another issue is the cost of integrating simulation applications with other manufacturing applications. These barriers hinder the automated input of data into DES models and as a result deter use of real-time DES in manufacturing. This review presents the existing research studies in the literature that address the above issues, demonstrating in parallel the already implemented concepts. The scope of this review is to provide an overview of the input data phase, focusing on its automation and motivating researchers to re-examine this phase by highlighting future research directions.

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