FLEXSIM QUARTERLY - V-Research · PDF file4 FlexSim Quarterly Letter to the Readers Spreading Simulation Knowledge \So, what’s the purpose of this blog...it’s to provide an informal

$Page 1: FLEXSIM QUARTERLY - V-Research · PDF file4 FlexSim Quarterly Letter to the Readers Spreading Simulation Knowledge \So, what’s the purpose of this blog...it’s to provide an informal$
FLEXSIM QUARTERLY

A FLEXSIM PUBLICATION

July 2014

FS

FlexSim Software Products, Inc.

Managing EditorMarkus Cueva

Copyright © 2014 FlexSim Software Products, Inc.

Published by FlexSim Software Products, Inc.

http://www.flexsim.com/

http://www.flexsim.com/

Contents

Letter to the ReadersCliff King . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Papers and StudiesResearch: Central Composite Design Optimization Using Simulation Approach

Abdulkadir Atalan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5White Paper: Emulation-Based Solutions for Virtual Planning and Commissioning

of Production PlantsRobert Schoch, Ruth Fleisch, Thorsten Prante and Alexander Walch . . . . . . . . . . . . . . 19

4 FlexSim Quarterly

Letter to the ReadersSpreading Simulation Knowledge

“So, what’s the purpose of this blog. . . it’s to provide an informal environment for the expressionof everything from opinions about the evolution of simulation in support of healthcare operationsand management to the impact of current methods like Supply Chain and Lean, to the best wayto manage many of the restrictions that are being imposed on healthcare organizations throughevery aspect of the Affordable Care Act.”

I am very excited with the launch of our new website dedicated to healthcare simulation, http://healthcare.flexsim.com/. The quote above is from the site’s first blog post, courtesy of Lou Keller, and sets the stagefor a focus on simulation knowledge that is relevant to today’s healthcare professionals. We have greatlysimplified the layout, design and content compared to the FlexSim website, making it much easier to findand access the information. If you are interested in healthcare simulation, we encourage you to stop by oftenand see what’s new.

We at FlexSim are pleased to be associated with so many individuals who are passionate about researching,studying, and implementing simulation principals. Enjoy the research and white paper you will find inthis issue of FlexSim Quarterly ; these papers represent many hours of dedicated work in an effort to sharesimulation knowledge with others.

Best,

Cliff KingChief Technical OfficerFlexSim Software Products, Inc.


http://healthcare.flexsim.com/

http://healthcare.flexsim.com/

July 2014 5

Central Composite Design Optimization Using Computer

Simulation Approach

Abdulkadir Atalan1

April 2014

AbstractResponse surface methodology is an important tool in modern quality engineering. It has become

common sense to use experimental design and other optimization methods in studies that aim to improveproduct quality. On the other hand, computer discrete simulations are widely used by companies to im-prove their performances. The objective of this article is to apply Central Composite Design (CCD) inthe Response Surface Method (RSM), along with discrete event simulation techniques using quality char-acteristics to minimize variability of responses and maximize the target value, in order to find optimumvalues.

Introduction

This paper has three main areas: Response SurfaceMethod (RSM), discrete event simulation and opti-mization of the problem. Response surface analy-sis in this study is defined as a solution that min-imizes variability subject to a constraint on meanperformance and maximizes target value subject toa constraint on standard deviation value. RSM is atechnique that is useful for the modeling and anal-ysis of problems in which a response of interest isinfluenced by several variables and the objective isto optimize this response [13]. Central CompositeDesign (CCD) is one of the RSM tools. Generally,CCD focuses on fitting a second-order, linear regres-sion model of the mean responses and the levels of thedesign variables (as a strong suggestion of the CCDto the simulation analyst interested in performingdesigned second-order simulation experiment). Theprevious studies have been on maximizing or mini-mizing the mean of the responses [5]. This methodstates that the variability of responses is stable, how-ever, this idea wasn’t able to work in real problems.

Many companies have frequently needed to usecomputer simulations in a wide range of areas [4]primarily because of the advantages computer simu-

lation provides, such as saving time, saving cost andchanging resources easily for the industry.

In the past, some researchers have applied fac-torial design to a simulation model. These appli-cations and conventional design methods are intenton improving the performances of goods [14]. How-ever, these situations may not be valid to get healthyresults. The main problems in many industrial en-vironments to improve the quality of product andperformances are the necessity to succeed a targetvalue and intercept a small variance [5]. This anno-tation of CCD expresses to minimize sensitivity tovariation of the products [14].

This study develops a new method of RSM in-cludes a simulation experiment which utilizes theCCD. Minimizing of variability of the target valueand maximizing target value of the standard devi-ation value are the main optimization problems inCCD. This paper shows a method that overcamethese obstacles using CCD methodology and a sim-ulation tool in a real-life problem.

The dual response surface method has been ap-plied to CCD optimization. Generated and gathereddata from the simulation of the FlexSim Healthcaremodel, which was chosen to model this simulationbecause of its ease of use, was optimized in two

1Department of Industrial Engineering, Clemson University.


6 FlexSim Quarterly

ways: Minitab Statistic Software and Maple Soft-ware, which are used in the academic and industrialarea by researchers and companies to compare out-comes from these tools in order to show that resultsare accurate.

Central Composite Design

Multi-objective problems contain many constraintswhich may not able to be solved without the use ofResponse Surface Methodology with computer sim-ulations [11]. Experimental design methodology andcomputer simulation are stated to evaluate the effectsof the constraints and their interactions elaboratedin the process [12]. The factors caused the effectscreated by the interaction of these factors, which inturn generated the responses.

RSM consists of a group of mathematical and sta-tistical techniques used in the development of an ad-equate functional relationship between a response ofinterest, y, and a number of associated control (orinput) variables denoted by x1, x2, . . . , xk [10]. Thebasic idea is to fit a model for the response variableand then explore various settings of interest for theexplanatory variables [5].

Figure 1: CCD for n = 3 and a =√

3.

Box-Behnken designs have treatment combina-tions that are at the midpoints of the edges of theexperimental space and require at least three con-tinuous factors. Points on Figure 1 represent theexperimental runs that are done.

Box-Behnken designs allow for gaining efficientestimation of the first-order and second-order coef-ficients. Because Box-Behnken designs often havefewer design points, they can be less expensive to dothan CCDs with the same number of factors. How-ever, because they do not have an embedded facto-rial design, they are not suited for sequential exper-iments.

The runs were led by the effects of independentfactors on the response along with the experimentalsituation which were visualized in CCD model de-signed experiments according to the RSM concept[6].

The conventional experiment design is focused onmostly a second-order design and the levels of thefactors [15]. A second-order model can significantlyimprove the optimization process when a first-ordermodel suffers lack of fit due to interaction betweenvariables and surface curvature. A general second-order model is defined in Equation 1, where xi andxj are the design variables and a are the turning pa-rameters.

y = a0 +

n∑i=1

aixi +

n∑i=1

aiix2i +

n∑i=1

n∑j=1

aijxixj

i < j (1)

y = β0+β1x1+β2x2+β3x3+β4x4+β5x5+β11x21+

β22x22 +β33x

23 +β44x

24 +β55x

25 +β12x1x2 +β13x1x3+

β14x1x4 + β15x1x5 + β23x2x3 + β24x2x4 + β25x25+

β34x3x4 + β35x3x5 + β45x4x5 (2)

According to Equation 1, the second order de-sign equation for five variables is shown in Equa-tion 2, where y is the predicted response; β0 is themodel constant; x, x2, x3, x4 and x5 are indepen-dent variables; β1, β2, β3, β4 and β5 are linear coeffi-cients; β12, β13, β14, β15, β23, β24, β25, β34, β35 and β45are cross product coefficients; and β11, β22, β33, β44and β55 are the quadratic coefficients [13].


July 2014 7

Types of Quality Characteristics

According to Ding [5], the first second-order modelsfor the sample mean (wµ) and the sample standarddeviation (wσ) were found by Vining and Myers andare shown in Equations 3 and 4.

wµ = β0+

k∑i=1

βixi+

k∑i=1

βiix2i +

k∑i=1

k∑j=1

βijxixj+εµ

i < j (3)

wσ = γ0+

k∑i=1

γixi+

k∑i=1

γiix2i +

k∑i=1

k∑j=1

γijxixj+εσ

i < j (4)

Determining the optimum operating conditionsor optimum factor settings (using control factors),which minimize performance variability and devia-tion from the target value, is preferable. Vining andMyers [5] optimized the scheme, shown in Equation5.

Minimize wσ subject to wµ = τ,

where τ is the target value. (5)

There are three types of quality of characteris-tics: the-smaller-the-better (S-type), the-larger-the-better (L-type), and the-nominal-the-best (N-type.)According to the S-type approach, minimization ofthe performance mean and the deviation from thetarget value is expressed in Equation 6.

Minimize µ subject to σ = τσ

X ∈ Ω (6)

The L-type approach focuses on maximization ofthe performance mean, and the deviation from thetarget value are expressed in Equation 7.

Maximize µ subject to σ = τσ

X ∈ Ω (7)

According to the N-type approach, minimizationof performance variability and the deviation from thetarget value are expressed in Equation 8.

Minimize σ or σ2 subject to µ = τ

X ∈ Ω (8)

Discrete Event Simulation

A simulation approach, including large statisticalknowledge in order to achieve analytical solutionsusing data, is one of the most popular tools for qual-ity engineering systems. Simulation utilizes compli-cated variables and constraints to define a systemprecisely [12]. Besides predicting performances ofsystems, simulation also enables forecasting for thefuture. The simulation model gives a better under-standing of the possible performance of the real sys-tem if it is run accurately [3].

As an analytical method, simulation includes sys-tems and models of those systems [9]. The use of De-sign of Experiments with Computer Simulation per-mitted a more efficient analysis of the results fromthe simulation model to manage decision making.

Case Study: An Emergency Pa-tient’s Process

Data CollectionThe data collected was averages based on other rawdata studies. These averages came from many dif-ferent sources, and created an accurate distributionfor a simulation model. A large amount of raw datacollected from articles proved to be more useful thanthe previously found averages.

Overall, the averages were used to calculate a dis-tribution modeling of the number of people who ar-rived at a clinic. Due to the nature of this pieceof information, it wasn’t as imperative to get databeyond the average. Using averages from the var-ious sources, and average patient arrival rate, thenumber of resources was decided upon. The otherprocess distributions came from the data collectedfrom studies. The processes required distributionsthat were extracted from the data and are shown inTable 2.


8 FlexSim Quarterly

Data analysis software was used to analyze theraw data recorded in the study. Each of the time pe-riods listed above are processes that a patient needsto go through while they are in the clinic. Eachof these processes takes an average amount of time,with a certain distribution. Using data analysis soft-ware, the various processes that occur when a patientgoes to a clinic were accurately simulated. When themodel is run, the output data closely matches the ac-tual raw data collected. This is the basic model andis being used to show the great potential of mod-eling software being used in the clinical throughputanalysis.

Process Time Assigned

Arriving Patient Exponential (0.0, 10.0, 0)

Check-in Uniform (3, 5, 0)

Triage Triangular (3, 15.0, 5, 0)

Treatment Uniform (20, 30, 0)

Check-out Uniform (3, 5, 0)

Table 3: Simulation process time.

Building Simulation ModelThe emergency room of a small hospital operatesaround the clock [1]. It is staffed by three clerksat the reception office and three medical doctors onthe premises and is assisted by three nurses, one foreach doctor. Figure 2 depicts a diagram of the pa-tient flow chart in the emergency department system,from arrival to discharge.

Figure 2: A hospital emergency room patient flow.

The model is working properly according to thedata collected. In order to make the simulation runeven better, it will be necessary to collect more rawdata from researchers and update the model basedon the scenarios. Because of the differences betweenstudies, it is important to collect data wherever pos-

sible for a simulation model to accurately representthe clinic that is looking to implement changes. Theresults in Table 8 show response data for the numberof patients who were treated.

Choosing CCDThe experiment is a 53 central composite, Box-Behnken design that has five experimental variables:physicians (x1), nurses (x2), clerks (x3), triage rooms(x4), and exam rooms (x5), unreplicated on each de-sign combination. The experimental conditions aredisplayed in Table 4.

Level

Factors (-1) (0) (+1)

A: Number of Physicians 1 2 3

B: Number of Nurses 1 2 3

C: Number of Clerks 1 2 3

D: Number of Triage Rooms 1 2 3

E: Number of Exam Rooms 1 2 3

Table 4: Conditions of the experiment.

Case Study ResultsThe observations y1, y2, . . . , yn, which are built bycontrol factors X1, X2, . . . , Xn, are generated fromthe experiment that has n runs and m replications[7]. The mean (y) and variance (s2) estimators arecalculated from the observations. This experimentaldesign is shown in Table 5.

After completion of these steps, second-orderfunctions are developed for the mean and standarddeviation. Using this information for the mean andstandard deviation, the general form of the estimatedmean and standard deviation response surface func-tions with n variables are shown in Equations 9 and10.

µ(x) = Xβµ, where

βµ = (X ′X)−1X ′y and y = [y1, y2, . . . , yn]′ (9)


July 2014 9

Figure 3: A hospital emergency room simulation overview (FlexSim Healthcare).

σ(x) = Xβσ, where

βσ = (X ′X)−1X ′s and s = [s1, s2, . . . , sn]′ (10)

N-Type 1: Minimize σ or σ2 subject to µ ≥ τUSLX ∈ Ω (11)

N-Type 2: Minimize σ or σ2 subject to µ ≤ τLSLX ∈ Ω (12)

All combinations of scenarios were designed inorder to analyze the data in Minitab. Second-orderestimator functions are developed for the mean andstandard deviation of each characteristic, show inEquations 13 and 14.

µ(x) = 42.5238+8.8117x1 +3.2843x2 +0.6105x3−1.1287x4+10.9749x5−9.1163x21−2.5124x22−0.7760x23

− 1.6955x24 − 8.0148x25 + 3.03x1x2 + 0.4148x1x3+

0.1267x1x4+8.3619x1x5−3.3210x2x3+1.4124x2x4+

0.6549x2x5−0.3881x3x4+0.0181x3x5−0.4619x4x5(13)

σ(x) = 3.9713+0.1667x1−0.42265x2−0.7319x3−0.2201x4 + 0.4557x5 − 1.5x21 − 0.04x22 − 0.9264x23−

1.29x24 − 1.7787x25 − 0.028x1x2 + 1.258x1x3−0.06195x1x4+1.74381x1x5−1.36x2x3−1.63841x2x4+

1.18404x2x5−0.1354x3x4+0.0977x3x5−0.184x4x5(14)

The optimal operating situations x∗ =[X∗1 , X

∗2 , . . . , X

∗n], which are known as optimal fac-

tor settings [7], and optimal process target valueµ∗ = [µ∗1, µ

∗2, . . . , µ

∗n] are defined in Table 6 and Table

7 using the Minitab and Maple softwares along withthe corresponding process target value, the expectednumber of throughout and standard deviation.

According to the L-type characteristic, approx-imately one or two physicians, one or two nurses,


10 FlexSim Quarterly

one clerk who utilizes check-in and check-out at thesame location, one exam room, and one or two triagerooms are proposed to have the maximum treatednumber of patients, neglecting resources cost. Ac-cording to the N-type characteristic, one physician,one nurse, one clerk who is responsible for check-inand check-out at the same location, one exam room,and one triage room are suggested to have minimumvariability of responses, neglecting resources cost.

The developed N-type (1) approach is interestedin minimization of the target value whereas N-type(2) is focused on having less resources to treat themaximum number of patients.

Conclusion

In an industrial environment, the main goal is to de-velop the potential quality for products and systemperformance. Many companies have lost profit due toincorrect applications of methods, causing defectionof products or services. This research shows thatthe optimal solution depends on minimizing sensi-tivity to variation of the products and services fromthe target value or setting the optimal mean abovethe process mean. In order to maximize benefit, theoptimal process should be set properly. In this pa-per, CCD and discrete event simulation were usedto generate data that has been determined numeri-cally using N-type and L-type approaches to find theoptimum solution.

References

[1] T. Altiok and B. Melamed. Simulation Model-ing and Analysis with Arena. Academic Press,Amsterdam, 2007.

[2] L.F. Alvarez. Design Optimization Based onGenetic Programming. PhD thesis, Universityof Bradford, UK, 2000.

[3] R.R. Barton. Designing simulation experiments.In Proceedings of the 2002 Winter SimulationConference, pages 45–51, 2002.

[4] R.A. Bates, R.S. Kenett, D.M. Steinberg, andH.P. Wynn. Achieving robust design fromcomputer simulations. Quality Technology and

Quantitative Management, 3(2):161–177, 2006.[5] R. Ding, D.K. Lin, and D. Wei. Dual-response

surface optimization: A weighted mse approach.Quality Engineering, 2004.

[6] S. Ghanbarzadeh, H. Valizadeh, and P.Z. Mi-lani. Application of response surface methodol-ogy in development of sirolimus liposomes pre-pared by thin film hydration technique. BioIm-pacts, 3(2):75–81, 2013.

[7] P.L. Goethals and B.R. Cho. The develop-ment of multi-response experimental designs forprocess parameter optimization. InternationalJournal of Quality & Reliability Management,28:628–648, 2011.

[8] J.R. Hussey, R.H. Myers, and E.C. Houck. Cor-related simulation experiments in first-order re-sponse surface design. Operations Research,35(5):744–758, 1987.

[9] W.D. Kelton, R.P Sadowski, and D.A. Sad-owski. Simulation with Arena. WCB/McGraw-Hill, Boston, Mass, 2004.

[10] A.I. Khuri and S. Mukhopadhyay. Response sur-face methodology. Wiley Interdisciplinary Re-views: Computational Statistics, 2, 2010.

[11] D.N. Mavris, O. Bandte, and D.A. Delaurentis.Robust design simulation: A probabilistic ap-proach to multidisciplinary design. Journal ofAircraft, 1999.

[12] J.A. Montevechi, A.F. Pinho, and F.A. Marins.Application of design of experiments on the sim-ulation of a process in an automotive industry.In Proceedings of the 2007 Winter SimulationConference, 2007.

[13] D.E. Montgomery. Design and Analysis of Ex-periments. John Wiley, New York, 2001.

[14] G. Park, K. Hwang, T. Lee, and K.H. Lee. Ro-bust design: An overview. Aiaa Journal, 44,2006.

[15] J.D. Tew. Using central composite designs insimulation experiments. In Proceedings of the24th Winter Simulation Conference, 1992.


July 2014 11

Appendix

Figure 4: Arriving patient distribution.

Figure 5: Check-in distribution.

Figure 6: Triage distribution.

Figure 7: Treatment by physician distribution.

Figure 8: Check-out distribution.

Figure 9: Estimated regression coefficients for meanoutputs from Minitab.

Figure 10: Estimated regression coefficients for stan-dard deviation outputs from Minitab.



Figure 11: Number of patients who are treated in current simulation model.

Figure 12: Resources utilization rate in current simulation model.

Table 8: Box-Behnken CCD, process data from the simulation model.

A B C D E Y(1) Y(2) Y(3) Y(4) Y(5) Y(MEAN) Y(S)1 1 2 2 2 21 22 21 20 21 20.9105 0.51833 1 2 2 2 30 31 32 29 29 30.3524 1.27311 3 2 2 2 21 22 22 22 22 21.8381 0.50013 3 2 2 2 43 44 42 45 43 43.4000 1.14022 2 1 1 2 43 42 41 40 42 41.6000 1.14022 2 3 1 2 43 42 41 44 42 42.4000 1.14022 2 1 3 2 42 40 41 39 40 40.5524 0.98892 2 3 3 2 40 39 40 40 40 39.8000 0.44722 1 2 2 1 15 17 19 15 16 16.5524 1.62622 3 2 2 1 21 22 22 21 21 21.2248 0.37822 1 2 2 3 40 38 39 39 39 39.0952 0.59572 3 2 2 3 45 45 54 44 44 46.3871 4.08391 2 1 2 2 15 20 33 20 21 21.7105 6.52543 2 1 2 2 42 38 40 39 39 39.6838 1.39891 2 3 2 2 22 22 22 22 21 21.8114 0.46973 2 3 2 2 42 41 42 41 41 41.4438 0.3768

Continued on next page


July 2014 13

Table 8 – continued from previous pageA B C D E Y(1) Y(2) Y(3) Y(4) Y(5) Y(MEAN) Y(S)2 2 2 1 1 19 21 20 20 21 20.2571 0.82932 2 2 3 1 21 21 19 21 21 20.6667 0.94282 2 2 1 3 44 43 42 44 44 43.3333 0.94282 2 2 3 3 42 41 42 42 42 41.8952 0.32012 1 1 2 2 30 32 32 42 32 33.6324 4.56992 3 1 2 2 42 42 43 100 42 53.7200 25.87262 1 3 2 2 42 42 43 100 42 53.7200 25.87262 3 3 2 2 43 41 42 42 42 42.1238 0.60201 2 2 1 2 21 22 22 21 22 21.7124 0.51583 2 2 1 2 39 40 41 41 41 40.4686 0.95501 2 2 3 2 20 22 22 21 21 21.2057 0.76363 2 2 3 2 39 40 41 41 41 40.4686 0.95502 2 1 2 1 22 22 21 21 20 21.1829 0.85942 2 3 2 1 21 22 20 20 21 20.8305 0.60232 2 1 2 3 44 44 44 43 43 43.5390 0.36582 2 3 2 3 44 43 43 43 43 43.2590 0.49981 2 2 2 1 21 19 19 19 19 19.4514 0.88903 2 2 2 1 20 19 20 20 20 19.9067 0.33201 2 2 2 3 22 22 23 22 22 22.1295 0.34243 2 2 2 3 58 50 52 52 104 63.4324 22.85622 1 2 1 2 36 65 37 36 65 47.8057 15.69772 3 2 1 2 43 42 42 71 93 58.2543 23.05532 1 2 3 2 25 30 59 28 27 33.7229 14.18752 3 2 3 2 43 41 42 42 42 42.0267 0.60852 2 2 2 2 40 40 41 41 41 40.4476 0.59912 2 2 2 2 41 40 42 41 41 40.8476 0.84342 2 2 2 2 40 40 41 58 41 43.8762 7.75862 2 2 2 2 42 40 42 58 52 46.6762 7.79502 2 2 2 2 39 40 42 41 47 41.6476 3.19632 2 2 2 2 39 40 41 41 48 41.6476 3.6354



Figure 13: Design of simulation scenarios according to CCD.

Figure 14: Simulation experiment overview.

Number of Factors Central Composite Optional Number of Blocks Default Number of Center Points

3 15 1 3

4 27 3 3

5 46 2 3

6 54 2 6

Table 1: Response Surface Methodology: Box-Behnken design.

Factors/Parameters Data

Patient Arrival Time Distribution Exponential Distribution

Resources Clerk, Nurse, Physician, Triage Room, Exam Room

Processing Time Distribution Exponential, Uniform and Triangular Distribution

Queue Discipline FIFO (First-in, First-out)

Table 2: Simulation parameters.


July 2014 15

Run X1 X2 . . . Xn Replications (Y ) y s

1

Control Factor Settings

y11 y12 . . . y1j . . . y1m y1 s1

2 y21 y22 . . . y2j . . . y2m y2 s2...

......

...

n yn1 yn2 . . . ynj . . . ynm yn sn

Table 5: Response Surface Methodology format.

Quality Characteristic x∗ Throughout σ

L-Type (1.3853, 1.29, 1, 1, 1.4326) 54 2.073

N-Type (1, 1, 1, 1, 1) 53 1.606

Table 6: Optimal process results.

Quality Characteristic x∗ Throughout σ

L-Type (1.3853, 1.29, 1, 1, 1.4326) 54 2.073

N-Type (1) (1, 3, 2.975, 1, 1) 18 20.827

N-Type (2) (1.1373, 1, 1, 1, 1.1405) 53 1.654

Table 7: Developed method optimal process results.

Figure 15: Simulation experiment output (number of treated patients).



Figure 16: Maple optimization output.


July 2014 17

Figure 17: Normal distribution plot – upper tail.

Figure 18: Normal distribution plot – lower tail.



Figure 19: L-Type approach for resources utilization rate from simulation model according to Minitab results.

Figure 20: N-Type approach for resources utilization rate from simulation model according to Minitab result.

Figure 21: L-Type approach for resources utilization rate from simulation model according to Maple result.

Figure 22: N-Type approach for resources utilization rate from simulation model according to Maple result.


July 2014 19

Emulation-Based Solutions for Virtual Planning and

Commissioning of Production Plants

Robert Schoch1, Ruth Fleisch1, Thorsten Prante2 and Alexander Walch2

April 2014

AbstractThe purpose of cut-to-size plants is to realize cutting, sorting, and stacking of panels of different

sizes and materials. In order to support decision making during the complex sales procedures of suchplants, V-Research, together with Schelling Anlagenbau, developed an emulation tool which enables theplanner to model, emulate, and animate plant processes. This facilitates exact forecasts of the achievableperformance [7]. To still ensure cost effectiveness, the tool is also beneficially employed in later phasesof design and implementation, and even beyond. Here, we describe the use of emulation to find anoptimal solution concerning the plant performance in the special case of plants with feedback in thematerial flow, where the occurrence of deadlocks has to be avoided [4]. Such layouts with saw cycles arenecessitated by the requirement to manufacture smaller lot sizes, resulting in cutting patterns, whichcannot be manufactured by plants with a linear material flow.

Introduction

Schelling Anlagenbau designs and delivers fully au-tomated cut-to-size plants. Having to meet manifoldand specialized requirements of different plant opera-tors results in a high degree of structural complexity.Plant variety is achieved by combining componentsinto devices such as cut-to-size saws, turning units,or roller tracks, and these devices are again com-bined into cut-to-size plants following the modulardesign principle. In addition, cut-to-size plants fea-ture a dynamic behavior, the complexity of whichis considerably larger for plants designed for make-to-order production than for plants configured formass production. Customer-specific manufacturingimplies small lot sizes or even lot size one. In orderto still keep the amount of waste, arising from cut-ting a panel into pieces, at a low level, the cuttingpatterns become more complex. Figure 1 contrastsa simple cutting pattern for mass production witha more sophisticated one for make-to-order produc-tion.

Figure 1: Panel cutting patterns (simple on the leftvs. more complex on the right).

Conventional cut-to-size plants with two saws oforthogonal placement in the material flow—one forrip-cuts and another for cross-cuts—can process pan-els according to cutting patterns such as those de-picted on the left side of Figure 1. However, themanufacturing of panels according to more complexcutting patterns, like the one shown on the right sideof the same figure, exceeds the capabilities of suchplants. To overcome this limitation, plant layoutswhich feature material flows with feedback have beenintroduced. An example of such a cut-to-size plantis depicted in Figure 2.

In the context of cut-to-size plants, a feedbackin the material flow means that panels which have

1V-Research GmbH, Dornbirn, Austria.2Schelling Anlagenbau GmbH, Schwarzach, Austria.



Figure 2: Cut-to-size plant with feedback in the material flow.

been cut by a certain saw will be allocated to thesame saw again for further partitioning via a feed-back conveyor. More generally speaking, a panel (orparts of it) can be fed back to the same saw again andagain as often as required to fulfill the specificationgiven by a certain complex cutting pattern.

The downside of feedbacks in the material flowtowards realizing more complex cutting patterns isthat they may cause deadlocks [1]. A deadlock is de-fined as a state of the manufacturing system in whichthe system or parts of it remains indefinitely blockedand cannot terminate its pending tasks [3].

Thus, the objective is to design cut-to-size plantswith feedbacks in the material flow in such a waythat they can be operated without any deadlocks oc-curring, while at the same time maximizing plantperformance. Such planning tasks cannot be han-dled with static methods, but require simulation tobe successfully solved. In order to achieve this goal,V-Research (a center for industrial research and de-velopment) together with Schelling Anlagenbau (aworld leader in the domain of cut-to-size plants) de-

veloped an emulation tool where the plant controlsoftware, which is later applied in conjunction withthe real plant, is connected to and controls a virtualmodel of the cut-to-size plant. The term ‘emulation’indicates a special case of simulation: it is simulationsupplemented with the coupling of real-world func-tional components [6]. This not only minimizes thecredibility gap often discussed in the context of com-plex simulations, but the planning and analysis toolalso enables the plant to improve iteratively until keyperformance indicators guaranteed by the manufac-turer of the cut-to-size plant are met. In this way,emulation serves to deal with the complexities men-tioned above, especially in regard to the dynamicbehavior.

Emulation Tool

Cut-to-size plants are centrally and hierarchicallycontrolled real time systems. As Figure 3 illustrates,the process control layer, constituting the highestlayer in the control pyramid and comprising the plant


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control software, sends orders to the control layer (or,more precisely, to the programmable logic controllers(PLC) of the devices) and receives confirmation whenall actions have been processed. At the lowest level,the field layer represents all mechanical componentswith their actuators and sensors. The field layer iscontrolled by PLCs which process sensor data andgenerate control signals for the actuators.

Figure 3: Central hierarchical control system.

The latter two layers of the real-world system(control layer and field layer) are modeled in a simu-lation model, where the virtual control layer is con-nected to the process control layer. Consequently, in-stead of controlling the real plant, the process controllayer controls the virtual one. This is called an emu-lation system, as some functional part of the softwaresystem is carried out by a part of the real system [6].

Realization of the Emulation ApproachAfter starting the emulation, the plant control soft-ware broadcasts orders to the virtual control layer,which are generated on the basis of a production listwith cutting patterns of the panels (see Figure 4).On the virtual control layer, an order is divided intoa task sequence. A single task describes an activ-ity that imitates a physical mechanism, for example‘lowering of the pressure beam’ of the order ‘saw’.

The virtual field layer, which is based on the sim-ulation engine FlexSim, retrieves the task sequencesand transforms them into discrete events. It im-plements 3D visualization of the mechanical compo-nents and of the panels in progress as well as of theirmovements according to the tasks, and thus displaysthe processes related to material handling and trans-port in the cut-to-size plant. After execution of thetasks of an order, the confirmation of the order is sent

to the plant control software via the virtual controllayer. Depending on the order confirmation, new or-ders are triggered.

An important issue to be considered with re-gard to emulation is that the virtual field and pro-cess control layers, with their different clocks for thescheduled arrival times of events, have to be syn-chronized. Thereto, a variation of the ‘blocking ren-dezvous pattern’ [2], was implemented, ensuring cor-rect chronologies in the transmission of the ordersand at the same time allowing execution of an emu-lation run at least a magnitude faster than real time.Hence, the behavior in the virtual field layer is inde-pendent of simulation speed.

An important issue to be considered with re-gard to emulation is that the virtual field and pro-cess control layers, with their different clocks for thescheduled arrival times of events, have to be syn-chronized. Thereto, a variation of the ‘blocking ren-dezvous pattern’ [2], was implemented, ensuring cor-rect chronologies in the transmission of the ordersand at the same time allowing execution of an emu-lation run at least a magnitude faster than real time.Hence, the behavior in the virtual field layer is inde-pendent of simulation speed.

Additionally, the presented emulation approachdoes not contain any stochastic processes, nor areuncertainties modeled in another way. The reason isthat the plant control software controls the materialflow in the model by generating the orders for feedingthe plant with raw panels or for processing the partson the basis of valid plant-specific cutting patterns.Aside from that, the performance indicators are cal-culated without taking into account probabilities ofsystem errors for the sake of accuracy.

User InteractionIn order to facilitate the handling of the emulationtool for domain experts such as technical sales per-sonnel, who commonly do not have simulation ex-pertise, a user interface was developed for modelingcut-to-size plants, analyzing recorded data, and man-aging different models (see Figure 4). In a graphi-cal editor, templates of components and devices canbe combined following the modular design principleand parameterized to form a system model of theplant in question. The parameters comprise process-relevant and mechanical information, velocities, as



Figure 4: System architecture (left side) and order handling (right side).

well as meta-information for the instantiation of eachcomponent and device in both layers of the virtualmodel. Once the plant is specified as a system model,an emulation run can be triggered in the user inter-face with the effect that the automated transforma-tion of the system model into a simulation modeland the automated instantiation of the virtual con-trol layer take place. While running the emulation,run time information is logged, which afterwards canbe viewed and analyzed by means of the user inter-face.

Why Emulation?The input data for production in a cut-to-size plantare available in the form of panel cutting patterns,which have to be interpreted correctly in order toprepare orders for processing the panels. In the realsystem, the plant control software performs this task.The reproduction of the interpretation of the cuttingpatterns in a simulation model and, more generally,of the logic underlying the control of the plant wouldhave necessitated efforts which have been avoided byintegrating the plant control software with the sim-ulation model.

Besides minimizing the credibility gap by bring-ing the model closer to reality [6], another reason

for emulation is that the emulation tool can serveas a test bed for the plant control software. Thisproved indispensable for the realization of the con-cept of feedbacks in the material flow: the deadlock-avoidance procedure implemented in the plant con-trol software had to be iteratively examined and ad-justed. Testing, analyzing, and adapting of the plantcontrol system already during the phase of develop-ment of the cut-to-size plant reduces time and costsfor commissioning.

Testing Plant Control Software

For the purpose of handling deadlocks in the caseof a cut-to-size plant featuring feedbacks in its ma-terial flow, the deadlock avoidance method [5] hasbeen chosen for implementation in the plant controlsoftware. With this method, decisions are made inreal time and are based on the analysis of the currentstate of the plant. For example, one question whichhas to be answered is whether a new panel shall beallocated to the saw or one from the feedback con-veyor. The method is intended to not only avoiddeadlocks that may occur but also to contribute to ahigh performance for the plant. For balancing these


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two competing objectives, adjustment of the param-eters of the deadlock avoidance method is essential.This, and the testing of the implemented method ingeneral, cannot be done with the real plant as, first,the plant is usually not constructed at the time theplant control software is programmed and, second,testing of software with the real plant during com-missioning would incur high costs and is usually notaccepted by the operator of the plant.

For these reasons, the presented emulation toolis employed to identify an optimal solution regard-ing the plant performance. Here it should be notedthat emulation, just like simulation, is an evaluationtechnique and does not automatically produce an op-timal solution, unless emulation runs are controlledby an external search loop. An emulation run andthe subsequent analysis of results serve as a basisfor modifications of parameter values of the decisionrules implemented in the plant control software. Thisis repeated until the absence of deadlocks can be en-sured and critical performance indicators, such as cy-cle times, throughput rates, and utilization of plantdevices (e.g. saws), are optimized. The optimal so-lution found is based on the assumption that for realproduct manufacturing there are valid plant-specificcutting patterns, and hence predefined constraints,such as maximal size of panel, size of part of panelor number of cuts per cycle, are used for pattern for-mation for part lists.

Summary of Particularities andAdvantages

The most obvious particularity of the presented plan-ning and analysis tool is the emulation approach,which means that the real plant control software isconnected to the virtual model of the plant and con-trols it. In this way, closeness to reality is achievedto an extent, which would be possible in the caseof using pure simulation models only by reproduc-ing the complete plant control software resulting insignificant additional effort and costs.

The separation of the system into the two lev-els ‘modeling and results’ as well as ‘emulation andvisualization’ (see system architecture in Figure 4)enables plant experts to model, emulate, and ani-mate cut-to-size plants without detailed simulation

knowledge.The emulation tool is successfully employed not

only during sales procedures but also during controldevelopment, which enhances cost effectiveness.

In order to support decision making during salesnegotiation, the emulation tool is used for demon-strating the capability to meet specific customer re-quirements, calculating critical performance indica-tors accurately, and animating the plant, its pro-cesses, and the material flow in 3D. This increasesthe planning security for both the plant manufac-turer and potential operators, reduces informationasymmetries between them, and raises the trustwor-thiness of the manufacturer [7].

Regarding the plant control, the concept of feed-backs in the material flow of cut-to-size plants wouldnot have been realized with acceptable efforts with-out the emulation tool as a test bed. The virtualcommissioning of cut-to-size plants allows testingand adapting of plant control software already priorto construction of the real plant to ensure deadlock-free plants while still fulfilling performance-indicatorrequirements [4].

Acknowledgments

This article discusses the results and findings of aresearch project within the K-Project ‘IntegratedDecision Support Systems for Industrial Processes(ProDSS),’ which has been financed under the Aus-trian funding scheme COMET (COMpetence centersfor Excellent Technologies).

Animation

Animation of a cut-to-size plant (without feedbackin the material flow) in FlexSim by use of theemulation tool: http://www.v-research.at/en/

references/system-planning.html.

References

[1] E.G. Coffman, M.J. Elphick, and A. Shoshani.System Deadlocks. Computing Surveys, 3:67–78,1971.

[2] B.P. Douglass. Real Time UML: Advances inthe UML for Real-time Systems. Addison-Wesley,


http://www.v-research.at/en/references/system-planning.html

http://www.v-research.at/en/references/system-planning.html


Boston, 2004.[3] M.P. Fanti and M. Zhou. Deadlock Control Meth-

ods in Automated Manufacturing Systems. IEEETransactions on Systems, Man, and Cybernetics,Part A: Systems and Humans, 34:5–22, 2004.

[4] R. Fleisch, R. Schoech, T. Prante, andR. Pflegerl. Consistent Use of Emulation AcrossDifferent Stages of Plant Development – TheCase of Deadlock Avoidance for Cyclic Cut-to-size Processes. In Proceedings of the 2013 WinterSimulation Conference, pages 2565–2576, 2013.

[5] Z. Li, N. Wu, and M. Zhou. Deadlock Controlof Automated Manufacturing Systems Based on

Petri Nets – A Literature Review. IEEE Trans-actions on Systems, Man, and Cybernetics, PartC: Applications and Reviews, 42:437–462, 2012.

[6] I. McGregor. The Relationship Between Simula-tion and Emulation. In Proceedings of the 2002Winter Simulation Conference, pages 1683–1688,2002.

[7] R. Schoech, S. Schmid, C. Hillbrand, andR. Fleisch. Optimising Plant Layout DecisionsBased on Emulation Models – Technical Frame-work and Practical Insights. International Jour-nal of Simulation and Process Modelling, 8:92–103, 2013.


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