optimization software simulation optimization: new advances for real world optimization fred glover...
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![Page 1: OPTIMIZATION SOFTWARE SIMULATION OPTIMIZATION: NEW ADVANCES FOR REAL WORLD OPTIMIZATION Fred Glover (OptTek) Gary Kochenberger (OptTek &](https://reader036.vdocuments.us/reader036/viewer/2022062421/56649d6a5503460f94a4808f/html5/thumbnails/1.jpg)
OPTIMIZATIONSOFTWARE www.OptTek.com
SIMULATION OPTIMIZATION: NEW ADVANCES FOR REAL WORLD OPTIMIZATION
Fred Glover (OptTek)
Gary Kochenberger (OptTek & UCD)
(Special thanks to Marco Better)
OPTIMIZATIONSOFTWARE
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Outline
OptTek Systems, Inc. What is simulation optimization? Why is it important? Classical approaches Metaheuristic approaches Applications Conclusions
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OptTek Systems, Inc. Snapshot
Founded in 1992 Leading provider of optimization software to the general simulation market. OptQuest®, the company’s flagship software product
licensed to over 60,000 users the optimization standard for simulation modeling
Alliance partners number over twenty including: Halliburton Oracle CSC Flextronics Dassault CACI Lockheed Martin Rockwell Software HP
Consulting and Technical Services
OptTek Systems, Inc. 1919 Seventh StreetBoulder, CO 80302www.OptTek.com
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Simulation Optimization Software
Alion, Micro Analysis and Design Division CACI , SIMPROCESS Oracle, Decisioneering (Crystal Ball) Delmia, a subsidiary of Dassault Systèmes FlexSim Software Products Flextronics/SimFlex Frontline Systems (Premium Solver) GAMS Glomark Incontrol Enterprise Dynamics
Jada Management Systems Halliburton, Landmark Graphics Division HP, Mercury Division Mesquite Software Planview PROMODEL Corporation Risk Capital Management Rockwell Software (ARENA) SIMUL8 XJ Technologies
Our Channel Partners:
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OptTek Customized Simulation Optimization Software Applications
Portfolio Management securities and capital assets (projects, programs, initiatives, etc.)
Workforce Optimization Manpower planning, diversity planning Data Security Supply Chain Management Strategic and Operational Planning Financial Planning Manufacturing Process Flow Resource-Constrained Scheduling Business Process (re)Design
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What is Simulation Optimization?
Which of possibly many sets of model specifications (i.e., input parameters and/or structural assumptions) leads to optimal performance?
Inputparameters
Measure of performance
Simulationmodel
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Simulation OptimizationWhy is it required?
Complex models contain many variables and constraints as well as uncertainty
What-if approach unlikely to result in an optimal answer due to large number of possible solutions
Inability of pure optimization to model complexities, uncertainties and dynamics of scenarios
Simulation-Optimization removes these inabilities by combining both approaches
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Simulation-OptimizationWhy is it required?
A total solution requires both capabilities.
Integrated two-Step Solution
Simulation
Optimization
Both are necessary, neither is sufficient.
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Simulation OptimizationBenefits in Dealing with Uncertainty
Simulation enables understanding/modeling and communications of uncertainty.
Optimization enables management of uncertainty.
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Optimization on a Metamodel
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Classical Approaches
Stochastic approximation
– Gradient-based approaches
Sequential response surface methodology Random search Sample path optimization
– Also known as stochastic counterpart
Drawbacks:• Local in their search
• Rely heavily on randomness
• Lack of intelligent guidance
• No learning ability
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Metaheuristic Approaches
Based on neighborhood search– Tabu search
– Simulated annealing
Based on combining solutions in a population– Genetic algorithms
– Scatter search
Other:– Swarm methods
– Hybrid methods (e.g. tabu search + scatter search)
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Modular Design
MetaheuristicOptimizer
SimulationModel
Input parameters Objective function value
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Tabu Search
Uses a systematic neighborhood search to choose the best neighbor– Size of the neighborhood is controlled by candidate
list strategies
– The selection of the best neighbor is constrained by tabu functions
The best move may be nonimproving Memory functions (short and long term) are
updated after every move
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Tabu Search:Implementation Issues
Feasible point
Infeasible point
Current point
Optimal point
Nontabu moveTabu move
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Scatter Search
Combines solutions in a small reference set to create new trial solutions
Uses generalized combination methods with controlled randomization
The selection process is deterministic The updating of the reference set (aka the
“evolution process”) is also deterministic and attempts to create a balance between solution quality and diversity
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Basic Scatter Search
P
Diversification GenerationMethod
Repeat until |P| = PSize
Subset GenerationMethod
ImprovementMethod
Solution CombinationMethod
ImprovementMethod
Stop if no morenew solutions
Reference SetUpdate Method
RefSet
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Linear Combination Method
x3
1 2 3 4 5 6 7 8 9 10 11 12 13
1
2
3
4
5
6
7
8
9
10
x2 = (8,4)
x1 = (5,7)
x3 = x1 - r(x2 - x1)
x4 = x1 + r(x2 - x1)
x5 = x2 + r(x2 - x1)
y
x
x4
x5
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Issues Related to Metaheuristics for Simulation Optimization
Aggressiveness of the search– Balance between diversification and intensification
Solution representation– Combination methods
Use of metamodels to “save” on evaluations Constraint handling (soft vs. hard) Length of simulation and selection of best
solution
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Aggressiveness of the SearchO
bje
ctiv
e fu
nctio
n va
lue
Calls to the simulator
Aggressive and less diversified
Less aggressive but diversified
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Solution Representation
Continuous variables Discrete variables
– Resources (e.g., number of machines, number of technicians, etc.)
– Design choices (e.g., brand, category, etc.)
Binary variables– Special case of discrete variables
Permutation variables– Imply so-called all-different constraints
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Use of Meta-models
MetaheuristicOptimizer
SimulationModel
f(x)
Metamodelx
large d?
Discard x
Yes
No
)(ˆ xf
Neural networks,Regression, Data mining, etc.
)()(ˆbestxfxfd
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Handling Constraints
ConstraintMapping
x PenaltyFunction
Simulatorx*
F(x*)
G(x*)P(x*)
x = input parameters (possibly infeasible)
x* = mapped input parameters (constraint feasible)
F(x*) = objective function value
G(x*) = value of other output variables used in constraints
P(x*) = penalized objective function value
May allow desirable infeasible solutions from management perspective.
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Length of Simulation
Simulation runs during the optimization process are typically shorter than those of confirmation runs
A run can be terminated early if it can be predicted that the outcome will not improve upon the current best solution– This can be done with statistical analysis tools such
as confidence intervals and hypothesis testing
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OptQuest®
Scatter Search Advanced Tabu Search Linear Programming Integer Programming Neural Networks Linear Regression
Ten years of Research & Development funded by National Science Foundation (NSF) and Office of Naval Research (ONR)
A potent search engine that can pinpoint the best decisions to optimize plans.
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OptQuest vs. RiskOptimizer, Ex. 5 Prob. 14 Best solution = -8695.012285
-4397.23 Risk Pop 10-4576.85 Risk Pop 20
-4272.22 Risk Pop 50
-4765.34 Risk Pop 100
-8543.49 OptQuest Pop 20
-8695.01 OCL Boundary=.7
-9000
-8000
-7000
-6000
-5000
-4000
-3000
0 200 400 600 800 1000 1200 1400 1600 1800 2000
Simulations
Ob
ject
ive
Efficiency is Critical!
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OptQuest Applications
Optimization of Monte Carlo Models– Project portfolio selection
– Inventory order management
Optimization of Discrete Event Models– Six Sigma in an Emergency Room
– Job shop configuration
Optimization of Agent-based Models– Workforce diversity planning
– Manpower planning and scheduling
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Example 1 – Project PortfolioSelection in Oil and Gas
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Problem
Given a set of opportunities and limited resources determine the best set of projects that maximize performance while controlling risk.
Create a new portfolio
Augment an existing portfolio
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Traditional Approaches
Net Present Value Analysis / Ranking Methods
– Compute discounted cash flows and pick largest NPV
– Ignores uncertainty
Mean-Variance Optimization – Harry Markowitz (1952)
Minimize
Such that > Goal
• Normality of returns of assets must be assumed
• Quadratic Program
• Addresses correlation but limited to variance as measure of risk.
• Additional constraints such as cash flow and performance metrics may not be addressable.
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Simulation-Based Portfolio Selection
Use Monte Carlo simulation to model projects.– Unlimited ability to model complex situations
– Risk can be defined in multiple ways
Use OptQuest to select projects– Objectives based on outputs from simulation
– Additional constraints based on cash flows, etc.
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Components
Simulation Model Integer Variables
e.g., Only invest in one project within a group
Constraints e.g., Cash Flow
Multiple Objectives - “Requirements”e.g., Maximize Return Mean while keeping 5th percentile of return
above some goal (risk control).
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Application Information
5 Projects– Tight Gas Play Scenario (TGP)
– Oil – Water Flood Prospect (OWF)
– Dependent Layer Gas Play Scenario (DL)
– Oil - Offshore Prospect (OOP)
– Oil - Horizontal Well Prospect (OHW)
Ten year models that incorporate multiple types of uncertainty
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Budget-Constrained Project Selection
5 Projects– Expected Revenue and Distribution
– Probability of Success
– Cost
$2M Budget
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Base Case
Determine participation levels in each project [0,1] (Decision Variables) that
Maximize E(NPV) (Forecast)
While keeping NPV < 10 M$ (Forecast)
All projects must start in year 1.
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TGP = 0.4, OWF = 0.4, DL = 0.8, OHW = 1.
E(NPV) = 37.4M =9.5M
Base Case
Frequency Chart
M$
Mean = $37,393.13.000
.007
.014
.021
.028
0
7
14
21
28
$15,382.13 $27,100.03 $38,817.92 $50,535.82 $62,253.71
1,000 Trials 16 Outliers
Forecast: NPV
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Deferment Case
Determine participation levels in each project [0,1] AND starting times for each project that
Maximize E(NPV)
While keeping NPV < 10 M$
All projects may start in year 1, year 2, or year 3. (5x3=15 Decision Variables)
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TGP1 = 0.6, DL1=0.4, OHW3=0.2
E(NPV) = 47.5M =9.51M 10th Pc.=36.1M
TGP = 0.4, OWF = 0.4, DL = 0.8, OHW = 1.
E(NPV) = 37.39M =9.50M
Frequency Chart
M$
Mean = $37,393.13.000
.007
.014
.021
.028
0
7
14
21
28
$15,382.13 $27,100.03 $38,817.92 $50,535.82 $62,253.71
1,000 Trials 16 Outliers
Forecast: NPV
Base CaseFrequency Chart
M$
Mean = $47,455.10.000
.007
.014
.020
.027
0
6.75
13.5
20.25
27
$25,668.28 $37,721.53 $49,774.78 $61,828.04 $73,881.29
1,000 Trials 8 Outliers
Forecast: NPV
Deferment Case
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Probability of Success Case
Determine participation levels in each project [0,1] AND starting times for each project that
Maximize P(NPV > 47,455 M$) While keeping 10th Percentile of NPV > 36,096 M$
All projects may start in year 1, year 2, or year 3.
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TGP1 = 0.6, DL1=0.4, OHW3=0.2
E(NPV) = 47.5M =9.51M 10th Pc.=36.1M
Frequency Chart
M$
Mean = $83,971.65.000
.008
.016
.024
.032
0
8
16
24
32
$43,258.81 $65,476.45 $87,694.09 $109,911.73 $132,129.38
1,000 Trials 13 Outliers
Forecast: NPV
TGP1 = 1.0, OWF1=1.0, DL1=1.0, OHW3=0.2
E(NPV) = 83.9M =18.5M P(NPV > 47. 5) = 99% 10th Pc.=53.4M
Probability of Success Case
TGP = 0.4, OWF = 0.4, DL = 0.8, OHW = 1.
E(NPV) = 37.39M =9.50M
Frequency Chart
M$
Mean = $37,393.13.000
.007
.014
.021
.028
0
7
14
21
28
$15,382.13 $27,100.03 $38,817.92 $50,535.82 $62,253.71
1,000 Trials 16 Outliers
Forecast: NPV
Base CaseFrequency Chart
M$
Mean = $47,455.10.000
.007
.014
.020
.027
0
6.75
13.5
20.25
27
$25,668.28 $37,721.53 $49,774.78 $61,828.04 $73,881.29
1,000 Trials 8 Outliers
Forecast: NPV
Deferment Case
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Benefits
Easy to use Quickly evaluate many planning alternatives Optimized financial performance Better risk control using familiar metrics
Similar results found in larger problems • (e.g. oil & gas investment funnel with 256 projects).
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Example 2 – IT Project Portfolio Selection in Pharmaceuticals
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Problem Setup
Example 2: Monte Carlo Simulation• Portfolio of 20 potential projects
• Pharmaceutical product development Relatively long and costly R&D
Probability of Success factor after R&D is complete
• Mutually exclusive (substitute) products
• Dependent (complementary) products
• Choose the best (0,1) set of projects to: Maximize return
Control risk
Maximize probability of high NPV
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Base Case
Example 2: Summary Results(All cases subject to budget constraint).
– Base Case: Max E[NPV]
While St.Dev.(NPV) $ 650
– Result: E[NPV] = $ 2,139
P(5) = $ 1,086
St.Dev. = $ 639
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Case 2
Example 2: Summary Results(All cases subject to budget constraint).
– Case 2: Max E[NPV]
While P(5) $ 1,086
– Result: E[NPV] = $ 2,346
P(5) = $ 1,159
St.Dev. = $ 725
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Case 3
Example 2: Summary Results(All cases subject to budget constraint).
– Case 3: Max P(NPV > $2,139)
– Result: P(NPV > $2,139) = 62%
E[NPV] = $ 2,346
P(5) = $ 1,159
St.Dev. = $ 725
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Optimization DrivenSIX SIGMA
Using Simulation Optimization to Achieve Quality Goals
Example 3 – Six Sigma in an Emergency Room
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Minimizing Cycle Time at an ER
Treatment
Patient Arrival
Emergency Room
Approach= optimize current process, redesign process and re-optimize.
Objective = minimize expected cycle time for
critical patients
Release
Admit
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DMAIC Framework
Define the problem area
– Current ER process is too costly, in terms of operating cost and variability in level of service.
– Need to redesign ER process to reduce costs and guarantee service levels at a 95% confidence level or higher.
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DMAIC Framework
Describe the current process– Arriving patients are assigned a priority level according to
the criticality of their condition:• LEVEL 1: immediately taken to an ER Room.
• LEVELS 2 AND 3: first sign in, then undergo a triage assessment before being taken to an ER Room.
• Level 2 and 3 patients’ arrival rate is higher than Level 1 patients’.
• Higher priority patients can preempt resources being used by lower priority patients.
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DMAIC Framework
Describe the current process (Cont’d.)
– Current resources available:• Nurses (7)
• Physicians (3)
• Patient Care Technicians (PCTs) (4)
• Administrative Clerks (4)
• ER Rooms (20)
– Rooms not used by ER can be used by other wards.
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DMAIC Framework
Arrive at ER
Transfer toroom
Receivetreatment
Fill outregistration
OK? Released
AdmittedInto
Hospital
Y
N
Current Process for Level 1 Patient
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DMAIC Framework
Measure current performance
– Costs (per 100 hours of operation):• Cost of personnel: $51.7K
• Fixed ER room cost: $ 0.9K
• Total operating cost: $52.6K
– Level of Service (CT of critical patients):• Average: 1.98 hours
• 95% Confidence Interval: [1.94 – 2.02]
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DMAIC Framework
Measure current performance (Cont’d.)
– Process is too costly. Six Sigma team has set a new budget goal of $40.0K per 100 hours of operation.
– Service level variability is too great. New goal: at least 95% of Level 1 patients spend no longer than 2 hours in the ER.
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DMAIC Framework
Analyze problem to identify causes– Construct a workflow level simulation model of current
process.
– Use OptQuest® to optimize resource levels in order to minimize Level 1 patients’ CT. Why?
– Enumeration of all possible scenarios may require:• 7x3x4x4x20 = 6,720 scenarios tested
• 30 runs/scenario = 2 min. each
28 workdays to obtain best solution!
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DMAIC Framework
Analyze problem (Cont’d)
– Minimize E[CT] for Level 1 Patients
– Subject to:• Operating Cost <= $40.0K/100 hrs of operation
• Number of Nurses between 1 and 7
• Number of Physicians between 1 and 3
• Number of PCTs between 1 and 4
• Number of Clerks between 1 and 4
• Number of ER Rooms between 1 and 20
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DMAIC Framework
Analyze problem (Cont’d)– First, run 30 replications of the current operation:
• 7 nurses
• 3 physicians
• 4 PCTs
• 4 Admin. Clerks
• 20 ER Rooms
– Results:• E[OC] = $ 52.6K per 100 hrs. of operation
(TOO COSTLY! New budget <= $40.0K)
• E[CT] for Level 1 Patients = 1.98 hours New process should achieve this result, or better.
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DMAIC Framework
Analyze problem (Cont’d)– Next, set up OptQuest to run for 100 iterations and 30
runs per iteration.• Each run simulates 100 hours of ER operation.
• Results: Best solution found in 6 minutes
3 nurses, 3 physicians, 1 PCT, 2 clerks, 12 rooms
E[OC] = $ 36.2K (31% improvement)
E[CT] for P1 = 2.08 hours (too high!)
– Need to redesign process to assure quality goal is achieved on a 95% confidence level.
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DMAIC Framework
Improve the results by redesigning processes
Arrive at ER
Transfer toroom
Receivetreatment
Fill outregistration
OK? Released
AdmittedInto
Hospital
Y
N
Current Process
Arrive at ER
Transfer toroom
Receivetreatment
Fill outregistration
OK? Released
AdmittedInto
Hospital
Y
N
Redesigned Process
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DMAIC Framework
Improve the results by redesigning processes– E[CT] for P1 improves from 2.08 to 1.98 hours;
however, the upper limit of the 95% confidence interval is still above 2 hours.
– Re-optimize new process using OptQuest.
– Results:• Best solution found in 8 minutes
• 4 nurses, 2 physicians, 2 PCTs, 2 clerks, 9 rooms
• E[OC] = $ 31.8K (a 12% further improvement)
• E[CT] for P1 = 1.94 hours (95% C.I. is 1.91 – 1.99)
• MISSION ACCOMPLISHED!
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DMAIC Framework
Control the processes to ensure improvement goals are met– Implement changes and a performance
measurement system to continuously assess real performance.
– Readopt this simulation-optimization methodology whenever necessary to maintain adequate performance.
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Conclusions
Able to find high-quality solution quickly. Able to improve the model and re-optimize to
find better configurations. Highly unlikely to find solution of such high
quality relying solely on simulation.
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Conclusions
There is still much to learn and discover about how to optimize simulated systems both from the
theoretical and the practical points of view.
The opportunities are exciting!
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Questions & Feedback
www.OptTek.com
(303) 447-3255