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Fred Glover OptTek Systems, Inc. Boulder, Colorado Andreas Reinholz University of Dortmund Dortmund, Germany Metaheuristics International Conference June.

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Presentation on theme: "Fred Glover OptTek Systems, Inc. Boulder, Colorado Andreas Reinholz University of Dortmund Dortmund, Germany Metaheuristics International Conference June."— Presentation transcript:

1 Fred Glover OptTek Systems, Inc. Boulder, Colorado Andreas Reinholz University of Dortmund Dortmund, Germany Metaheuristics International Conference June 25-29, 2007 METAHEURISTICS IN SCIENCE AND INDUSTRY: NEW DEVELOPMENTS

2 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

3 Standard Optimization Software OptQuest ® 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)

4 Metaheuristic – Based Simulation Optimization

5 Realistic Optimization

6 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!

7 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

8 Efficiency is Critical! OptQuest ® vs. RiskOptimizer

9 Oil/Gas Financial Planning using

10 Problem Given a set of opportunities and limited resources… …determine the best set of projects that maximizes performance

11 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)

12 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

13 Base Case Determine project participation levels [0,1] that Maximize E(NPV) Keep < 10,000 M$ (Risk Control) All projects start in year 1 Base Case TGP = 0.4, OWF = 0.4, DL = 0.8, OHW = 1.0 E(NPV) = 37,393 =9,501

14 Deferment Case Determine 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 TGP 1 = 0.6, DL 1 =0.4, OHW 3 =0.2 E(NPV) = 47,455 =9, th Pc.=36,096 Deferment CaseBase Case TGP = 0.4, OWF = 0.4, DL = 0.8, OHW = 1.0 E(NPV) = 37,393 =9,501

15 Probability of Success Case Determine project participation levels AND starting times for each project that Maximize P(NPV > 47,455 M$) Keep 10 th Percentile of NPV > 36,096 M$ Projects may start in year 1, 2, or 3 TGP 1 = 0.6, DL 1 =0.4, OHW 3 =0.2 E(NPV) = 47,455 =9, th Pc.=36,096 Deferment CaseBase Case TGP = 0.4, OWF = 0.4, DL = 0.8, OHW = 1.0 E(NPV) = 37,393 =9,501 TGP 1 = 1.0, OWF 1 =1.0, DL 1 =1.0, OHW 3 =0.2 E(NPV) = 83,972 =18,522 P(NPV > 47,455) = th Pc.=43,359 Probability of Success Case

16 Extensions… Cash Flow Control Capital Expenditure Control Reserve Replacement Goals Production Goals Finding Costs Control Dry Hole Expectations Control Reserve Goals Net Profit Goals

17 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.

18 ER Resources Nurses Physicians Patient Care Technicians (PCTs) Administrative Clerks Emergency Rooms (ER)

19 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

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

21 Process Redesign Possible to improve E[CT] for P1 even further? Arrive at ER Transfer to room Receive treatment Fill out registration OK? Released Admitted Into Hospital Y N Current Process Arrive at ER Transfer to room Receive treatment Fill out registration OK? Released Admitted Into Hospital Y N Redesigned Process

22 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)

23 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

24 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)

25 Global Conclusions - 2 Key methodology is an integration of: Adaptive Memory Metaheuristics (TS) Evolutionary Metaheuristics Math Programming Data Mining (Pattern Analysis)

26 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


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