fred glover opttek systems, inc. boulder, colorado andreas reinholz university of dortmund dortmund,...
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Fred GloverOptTek Systems, Inc.Boulder, Colorado
Andreas ReinholzUniversity of DortmundDortmund, Germany
Metaheuristics International ConferenceJune 25-29, 2007
METAHEURISTICSIN SCIENCE AND INDUSTRY: NEW DEVELOPMENTS
OptTek Customized Simulation Optimization Applications
• Portfolio Management• Supply Chain Applications• Strategic and Operational Planning• Financial Planning• Manufacturing Process Flow • Resource-Constrained Scheduling• Network Planning• Routing & Distribution• Data Mining• Biotechnology• Health Care
Standard Optimization SoftwareOptQuest®
• AnyLogic (a product of XJ Technologies Company)• Arena (a product of Rockwell Software/Systems Modeling Corp.)• Crystal Ball (a product of Decisioneering, Inc.)• CSIM (a product of Mesquite Software)• Enterprise Dynamics (a product of Incontrol)• FlexSim (a product of FlexSim Software Products, Inc.)• Micro Saint (a product of Micro Analysis and Design, Inc.)• OQNLP (a joint product developed with Optimal Methods, Inc.)• Parallel OptQuest® (enabled by Paradise®, a product of Scientific Computing)• Premium Solver Platform (a product of Frontline Systems)• Promodel/Innovate (products of Promodel Corporation)• Quest (a product of Delmia Corp.)• SimFlex (a product of Flextronics)• SIMPROCESS (a product of CACI)• SIMUL8 (a product of SIMUL8 Corporation)• TERAS (a product of Halliburton’s Landmark Graphics)• VIEO 1000 (a product of VIEO Corporation)
Metaheuristic – BasedSimulation Optimization
MetaheuristicOptimizer
SimulationModel
Input parameters Objective function value
Realistic Optimization
The Optimization Challenge
• Function to be Optimized• Highly Nonlinear• Nondifferentiable• Discrete or Continuous or Mixed
• Function Evaluations• Complex• Extremely Computation Intensive• One second to One Day per Evaluation!
• Evolutionary Scatter Search• Advanced Tabu Search• Linear & Mixed Integer Programming• Pattern Classification & Curve Fitting
• Neural Networks• Support Vector Machines & Trees• SAT Data Mining
OptQuest® Components
Example 5 Problem 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!
OptQuest® vs. RiskOptimizer
Oil/Gas Financial Planning using
Problem
• Given a set of opportunities and limited resources…
• …determine the best set of projects that maximizes performance
Portfolio Selection Problem
• Constraints: • Budget • Resource Availability • Scheduling and Sequencing of Projects • Project Dependencies, etc.
• Objectives:• Maximize Net Present Value (NPV)• Maximize Internal Rate of Return (IRR)• Maximize Business-Case Value (BCV)
Application Example
• 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
• Evaluation Time: 1s / Scenario
Base CaseDetermine project participation levels [0,1] that• Maximize E(NPV) • Keep < 10,000 M$ (Risk Control)• All projects start in year 1
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 $62,253.71
1,000 Trials 16 Outliers
Forecast: NPV
Base Case
TGP = 0.4, OWF = 0.4, DL = 0.8, OHW = 1.0
E(NPV) = 37,393 =9,501
Deferment CaseDetermine project participation levels [0,1] AND starting times for each project that• Maximize E(NPV) • Keep < 10,000 M$ (Risk Control)• Projects may start in year 1, 2, or 3
Frequency 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 $73,881.29
1,000 Trials 8 Outliers
Forecast: NPV
TGP1 = 0.6, DL1=0.4, OHW3=0.2
E(NPV) = 47,455 =9,513 10th Pc.=36,096
Deferment Case
Frequency Chart
M$
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 $62,253.71
1,000 Trials 16 Outliers
Forecast: NPV
Base Case
TGP = 0.4, OWF = 0.4, DL = 0.8, OHW = 1.0
E(NPV) = 37,393 =9,501
Probability of Success CaseDetermine project participation levels AND starting times for each project that• Maximize P(NPV > 47,455 M$) • Keep 10th Percentile of NPV > 36,096 M$• Projects may start in year 1, 2, or 3
Frequency Chart
M$
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 $73,881.29
1,000 Trials 8 Outliers
Forecast: NPV
TGP1 = 0.6, DL1=0.4, OHW3=0.2
E(NPV) = 47,455 =9,513 10th Pc.=36,096
Deferment Case
Frequency Chart
M$
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 $62,253.71
1,000 Trials 16 Outliers
Forecast: NPV
Base Case
TGP = 0.4, OWF = 0.4, DL = 0.8, OHW = 1.0
E(NPV) = 37,393 =9,501
Frequency Chart
M$
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) = 83,972 =18,522 P(NPV > 47,455) = 0.99 10th Pc.=43,359
Probability of Success Case
Extensions…
• Cash Flow Control• Capital Expenditure Control• Reserve Replacement Goals• Production Goals• Finding Costs Control• Dry Hole Expectations Control• Reserve Goals• Net Profit Goals
Hospital Emergency Room Process
Treatment
Patient Arrival
Emergency Room (ER)
Approach= optimize current process, redesign process and re-optimize.
Objective = minimize expected total asset
cost while ensuring a reasonable average
patient cycle time
Release
Admit
Joseph DeFee, CACI, Inc.
ER Resources
• Nurses• Physicians• Patient Care Technicians (PCTs)• Administrative Clerks• Emergency Rooms (ER)
Problem
• Minimize E[Total Asset Cost]• Subject to:
– E[Cycle Time] for Level 1 Patients < 2.4 hours– 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 between 1 and 20
Solution
• Set up OptQuest to run for 100 iterations and 5 runs per iteration
• Each run simulates 5 days of ER operation
• Results:– Best solution found in 6 minutes – E[TAC] = $ 25.2K (31% improvement)– E[CT] for P1 = 2.17 hours
Process RedesignPossible to improve E[CT] for P1 even further?
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
Solution of the Redesigned Process
• Set up OptQuest to run for 100 iterations and 5 runs per iteration
• Each run simulates 5 days of ER operation
• Results:– Best solution found in 8 minutes– E[TAC] = $ 24.6K (new best, 3.4% improvement)– E[CT] for P1 = 1.94 hours (12% improvement)
Conclusions
Simulation Optimization with OptQuest is able to
• fully address uncertainty from multiple sources• find high-quality solutions in reasonable time• follow modified models and re-optimize them• handle problems that are not solvable by classical
methods
Global Conclusions - 1
• These applications are only a fraction of the ways that metaheuristics and simulation are used in optimization involving non-linearity and uncertainty
• Over 60,000 user licenses of the system have been sold (each licensed user might have multiple kinds of problems)
Global Conclusions - 2
Key methodology is an integration of:
• Adaptive Memory Metaheuristics (TS)
• Evolutionary Metaheuristics
• Math Programming
• Data Mining (Pattern Analysis)
Wave of Future
Bootstrapping (Mutual Iterated Design):
• Metaheuristics (Sim Opt)
• Tuning (parameters) and Tailoring
• General Non-linear Models/Methods
• General Mixed Integer Models/Methods
• Knowledge Representation by Meta-Models