Using discrete event simulation to analyze the impact of job priorities on cycle time in semiconductor manufacturing

2002 ◽  
Author(s):  
D. Fronckowiak ◽  
A. Peikert ◽  
K. Nishinohara
Keyword(s):  
Discrete Event Simulation ◽  
Cycle Time ◽  
Semiconductor Manufacturing ◽  
Discrete Event ◽  
Event Simulation ◽  
The Impact

Performance Optimization of Semiconductor Manufacturing Equipment by the Application of Discrete Event Simulation

2008 ◽  
Author(s):  
Markus Pfeffer ◽  
Richard Oechsner ◽  
Lothar Pfitzner ◽  
Heiner Ryssel ◽  
Berthold Ocker ◽  
...  
Keyword(s):  
Discrete Event Simulation ◽  
Cycle Time ◽  
Performance Optimization ◽  
Semiconductor Manufacturing ◽  
Discrete Event ◽  
Simulation Models ◽  
Manufacturing Equipment ◽  
Event Simulation ◽  
Different Types ◽  
The Impact

Semiconductor wafer fabrication facilities (wafer fabs) are amongst the most complex production facilities. State-of-the-art wafer fabs comprise a large product variety, hundreds of processing steps per product, almost hundreds of machines of different types, and automated transportation systems combined with reentrant flows throughout the fab. In addition to the high complexity, wafer fabs require very high capital investment and an undisturbed operation. Semiconductor manufacturers are facing fierce competition as more global capacity is being added. Through this intense competition, semiconductor manufacturers have to improve their processes from a technological as well as from a logistical point of view in order to be successful within the global market. The logistics not only involves fab wide optimization strategies but also the individual equipment performance, for example cycle time and throughput, has to be considered. In this paper, the need for performance optimization of semiconductor manufacturing equipment is identified and the capability of discrete event simulation for such optimizations is being elaborated. Characteristics of different types of simulation models are described and the simulation model selection is explained. For case studies, several simulation models of different semiconductor manufacturing equipment have been developed. Using five examples, different optimization strategies, dependent on the application of the semiconductor manufacturing equipment, have been investigated by discrete event simulation. The paper shows the influence of the integration of metrology into deposition equipment, the impact of different batch sizes for oxidation processes, and the optimized dimensioning of photolithography equipment. Furthermore, the throughput and cycle time of different deposition equipment are optimized by the evaluation of various improvement strategies. All investigations have been performed with real data extracted from already utilized equipment or at least with data from the equipment suppliers of prototype equipment.

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.

A Methodology to Evaluate Complex Manufacturing Systems through Discrete-Event Simulation Models

2012 ◽  
Vol 502 ◽  
pp. 7-12 ◽  
Author(s):  
L.P. Ferreira ◽  
E. Ares ◽  
G. Peláez ◽  
M. Marcos ◽  
M. Araújo
Keyword(s):  
Discrete Event Simulation ◽  
Manufacturing Systems ◽  
Discrete Event ◽  
Simulation Models ◽  
Network Configuration ◽  
Event Simulation ◽  
Automobile Assembly ◽  
Loop Network ◽  
Complex Manufacturing Systems ◽  
The Impact

This paper proposes a methodology to analyze complex manufacturing systems, based on discrete-event simulation models. The methodology was validated by performing different simulation experiments and will be applied to a multistage multiproduct production line, based on a real case, with a closed-loop network configuration of machines and intermediate buffers consisting of conveyors, which is very common in the automobile sector. A simulation model in an Arena environment was developed, which allowed for an analysis of the important aspects not yet studied in specialized literature, namely the assessment of the impact of the production sequence on the automobile assembly line. Various sequence rules were analyzed and the performance of each of the corresponding simulation models was registered.

scholarly journals Designing optimal COVID-19 testing stations locally: A discrete event simulation model applied on a university campus

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253869
Author(s):  
Michael Saidani ◽  
Harrison Kim ◽  
Jinju Kim
Keyword(s):  
Simulation Model ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Daily Basis ◽  
Health Crisis ◽  
Event Simulation ◽  
Discrete Event Simulation Model ◽  
Rapid Result ◽  
The Usa ◽  
The Impact

Providing sufficient testing capacities and accurate results in a time-efficient way are essential to prevent the spread and lower the curve of a health crisis, such as the COVID-19 pandemic. In line with recent research investigating how simulation-based models and tools could contribute to mitigating the impact of COVID-19, a discrete event simulation model is developed to design optimal saliva-based COVID-19 testing stations performing sensitive, non-invasive, and rapid-result RT-qPCR tests processing. This model aims to determine the adequate number of machines and operators required, as well as their allocation at different workstations, according to the resources available and the rate of samples to be tested per day. The model has been built and experienced using actual data and processes implemented on-campus at the University of Illinois at Urbana-Champaign, where an average of around 10,000 samples needed to be processed on a daily basis, representing at the end of August 2020 more than 2% of all the COVID-19 tests performed per day in the USA. It helped identify specific bottlenecks and associated areas of improvement in the process to save human resources and time. Practically, the overall approach, including the proposed modular discrete event simulation model, can easily be reused or modified to fit other contexts where local COVID-19 testing stations have to be implemented or optimized. It could notably support on-site managers and decision-makers in dimensioning testing stations by allocating the appropriate type and quantity of resources.

scholarly journals The impact of interoperability of electronic health records on ambulatory physician practices: a discrete-event simulation study

2014 ◽  
Vol 21 (1) ◽  
pp. 21-29 ◽  
Author(s):  
Yuan Zhou ◽  
Jessica S Ancker ◽  
Mandar Upahdye ◽  
Nicolette M McGeorge ◽  
Theresa K Guarrera ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Discrete Event Simulation ◽  
Simulation Study ◽  
Discrete Event ◽  
Health Records ◽  
Physician Practices ◽  
Event Simulation ◽  
Electronic Health ◽  
The Impact

Abstract 16: A Discrete Event Simulation to Assess the Impact of EMS Routing Algorithms on the Number Needed to Route and Transport Time

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Brittany M Bogle ◽  
Andrew W Asimos ◽  
Wayne D Rosamond
Keyword(s):  
Discrete Event Simulation ◽  
Census Tract ◽  
Discrete Event ◽  
Routing Algorithms ◽  
Screening Tools ◽  
Transport Time ◽  
Stroke Incidence ◽  
Event Simulation ◽  
Routing Policies ◽  
The Impact

Introduction: Proposed EMS routing algorithms permit additional transport time to an endovascular center (EC) beyond the closest non-EC for patients with suspected large vessel occlusion acute ischemic stroke (LVO). The effectiveness of these algorithms depends on screening tools and patient location relative to EC and non-ECs. We implemented routing algorithms in a discrete event simulation to examine their impact on one region. Methods: We simulated stroke and stroke mimic patients screened by EMS over a year using hospital locations and demographics of Mecklenburg County, NC. We used an 8% LVO prevalence among those screened and geographically distributed patients using published stroke incidence rates and census tract population estimates, stratified by age, sex, and race. We estimated distance from census tract centroids to the nearest EC and non-EC using real road travel times. Last known well (LKW) was probabilistically assigned using county data. A patient was EC-routed if they screened positive, had LKW ≤6 hours and were within an allowable additional transport time. We simulated policies that varied by stroke severity screen (LAMS ≥ 4, RACE ≥ 5, C-STAT ≥ 2) and allowable additional transport time (10, 20, and 30 minutes). We define Number Needed to Route (NNR) as the number of patients enduring additional transport time to route one LVO patient to an EC. Results: Over 100 replications, EMS screened an average of 3102 patients annually; 249 were LVOs. NNRs were 2.6 (LAMS ≥ 4), 5.3 (RACE ≥ 5), and 9.3 (C-STAT ≥ 2). The number of EC-routed non-LVOs ranged from 87 (LAMS ≥ 4, 10 minutes) to 859 (C-STAT ≥ 2, 30 minutes). The proportion of LVOs within 10 and 20 minutes of added transport time to an EC was 67% and 99.6% respectively. EC-routing added a mean of 5.5 and 9.5 minutes to transport time for 10 and 20 minute policies respectively. A 20 minute policy EC-routed 1.8 times more patients than a 10 minute policy (e.g. C-STAT: 957 vs. 535). Increasing from a 20 to 30 minute policy routed only 4 more patients, thus these policies had similar results. Conclusions: We designed and tested a simulation tool to evaluate LVO routing policies. It is easily modifiable to aid in tailoring routing policies to a specific region. We propose using NNR as an intuitive metric of non-LVO overtriage.

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