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.

Simulation of a Closed-Loops Assembly Line

2012 ◽  
Vol 502 ◽  
pp. 127-132 ◽  
Author(s):  
L.P. Ferreira ◽  
E. Ares ◽  
G. Peláez ◽  
A. Resano ◽  
C.J. Luis-Pérez ◽  
...  
Keyword(s):  
Assembly Line ◽  
Closed Loop ◽  
Discrete Event ◽  
Specific Class ◽  
Production Lines ◽  
Closed Loops ◽  
Event Simulation ◽  
Automobile Assembly ◽  
Loop Network ◽  
The Impact

The aim of the work presented in this paper describes the development of a decision support system based on a discrete-event simulation model of an automobile assembly line. The model focuses at a very specific class of production lines with a four closed-loop network configuration. One key characteristic in the closed-loop system is that the number of pallets inside the first three loops has been made constant. The impact of the number of pallets circulating on the first three closed-loops and of the proportion of four-door car bodies on the performance of the production line has been thoroughly investigated. This has been translated into the number of cars produced per hour, in order to improve the availability of the entire manufacturing system.

Utilizing Combinatorial Testing on Discrete Event Simulation Models for Sustainable Manufacturing

2009 ◽  
Author(s):  
Bjo¨rn Johansson ◽  
Raghu Kacker ◽  
Ru¨ediger Kessel ◽  
Charles McLean ◽  
Ram Sriram
Keyword(s):  
Discrete Event Simulation ◽  
Manufacturing Systems ◽  
Discrete Event ◽  
Sustainable Manufacturing ◽  
Optimal Solution ◽  
Computation Time ◽  
Simulation Models ◽  
Combinatorial Testing ◽  
Covering Arrays ◽  
Event Simulation

This paper describes how combinatorial testing using covering arrays can be implemented to optimize discrete event simulation models of manufacturing systems for measures of sustainability. Discrete event simulation models often have hundreds of parameters and many test values for each parameter. Generally the interactions between the parameter-values are not well understood; this can lead to sub-optimization of the system. Most optimization engines and software for discrete event simulation packages use full factorial designs, which require many runs and hence a lot of computation time. In this paper we introduce combinatorial testing using a test-suite generation tool called NIST-ACTS (National Institute of Standards and Technology - Advanced Combinatorial Test Suites) to dramatically decrease the number of runs required to detect the interactions and determine an optimal solution.

scholarly journals Supporting scale-up of COVID-19 RT-PCR testing processes with discrete event simulation

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0255214
Author(s):  
Jad El Hage ◽  
Patti Gravitt ◽  
Jacques Ravel ◽  
Nadia Lahrichi ◽  
Erica Gralla
Keyword(s):  
Discrete Event Simulation ◽  
Discrete Event ◽  
Scale Up ◽  
Simulation Models ◽  
The United States ◽  
Molecular Testing ◽  
Rt Pcr ◽  
Event Simulation ◽  
Trade Offs ◽  
The Impact

Testing is critical to mitigating the COVID-19 pandemic, but testing capacity has fallen short of the need in the United States and elsewhere, and long wait times have impeded rapid isolation of cases. Operational challenges such as supply problems and personnel shortages have led to these bottlenecks and inhibited the scale-up of testing to needed levels. This paper uses operational simulations to facilitate rapid scale-up of testing capacity during this public health emergency. Specifically, discrete event simulation models were developed to represent the RT-PCR testing process in a large University of Maryland testing center, which retrofitted high-throughput molecular testing capacity to meet pandemic demands in a partnership with the State of Maryland. The simulation models support analyses that identify process steps which create bottlenecks, and evaluate “what-if” scenarios for process changes that could expand testing capacity. This enables virtual experimentation to understand the trade-offs associated with different interventions that increase testing capacity, allowing the identification of solutions that have high leverage at a feasible and acceptable cost. For example, using a virucidal collection medium which enables safe discarding of swabs at the point of collection removed a time-consuming “deswabbing” step (a primary bottleneck in this laboratory) and nearly doubled the testing capacity. The models are also used to estimate the impact of demand variability on laboratory performance and the minimum equipment and personnel required to meet various target capacities, assisting in scale-up for any laboratories following the same process steps. In sum, the results demonstrate that by using simulation modeling of the operations of SARS-CoV-2 RT-PCR testing, preparedness planners are able to identify high-leverage process changes to increase testing capacity.

scholarly journals Modeling and Analysis of Reentrant Manufacturing Systems: Micro- and Macroperspectives

2011 ◽  
Vol 2011 ◽  
pp. 1-17
Author(s):  
Fenglan He ◽  
Ming Dong ◽  
Dong Yang
Keyword(s):  
Discrete Event Simulation ◽  
Continuum Model ◽  
Manufacturing Systems ◽  
Discrete Event ◽  
Simulation Models ◽  
Continuum Models ◽  
Modeling And Analysis ◽  
Supply Chain Networks ◽  
Event Simulation ◽  
Reentrant Manufacturing

In order to obtain the better analysis of the multiple reentrant manufacturing systems (MRMSs), their modeling and analysis from both micro- and macroperspectives are considered. First, this paper presents the discrete event simulation models for MRMS and the corresponding algorithms are developed. In order to describe MRMS more accurately, then a modified continuum model is proposed. This continuum model takes into account the re-entrant degree of products, and its effectiveness is verified through numerical experiments. Finally, based on the discrete event simulation and the modified continuum models, a numerical example is used to analyze the MRMS. The changes in the WIP levels and outflux are also analyzed in details for multiple re-entrant supply chain networks. Meanwhile, some interesting observations are discussed.

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.

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