1 / 26

METAHEURISTICS IN SCIENCE AND INDUSTRY: NEW DEVELOPMENTS

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

fionan
Download Presentation

METAHEURISTICS IN SCIENCE AND INDUSTRY: NEW DEVELOPMENTS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  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 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)

  4. Metaheuristic – BasedSimulation 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. OptQuest® Components • Evolutionary Scatter Search • Advanced Tabu Search • Linear & Mixed Integer Programming • Pattern Classification & Curve Fitting • Neural Networks • Support Vector Machines & Trees • SAT Data Mining

  8. OptQuest® vs. RiskOptimizer Efficiency is Critical!

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

  14. Deferment Case Base Case Deferment Case TGP = 0.4, OWF = 0.4, DL = 0.8, OHW = 1.0 E(NPV) = 37,393s =9,501 TGP1 = 0.6, DL1=0.4, OHW3=0.2 E(NPV) = 47,455s =9,513 10th Pc.=36,096 Determine project participation levels [0,1] AND starting times for each project that Maximize E(NPV) Keep s < 10,000 M$ (Risk Control) Projects may start in year 1, 2, or 3

  15. Probability of Success Case Base Case Deferment Case Probability of Success Case TGP = 0.4, OWF = 0.4, DL = 0.8, OHW = 1.0 E(NPV) = 37,393s =9,501 TGP1 = 0.6, DL1=0.4, OHW3=0.2 E(NPV) = 47,455s =9,513 10th Pc.=36,096 TGP1 = 1.0, OWF1=1.0, DL1=1.0, OHW3=0.2 E(NPV) = 83,972s =18,522 P(NPV > 47,455) =0.99 10th Pc.=43,359 Determine 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

  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 Patient Arrival Emergency Room (ER) Admit Treatment Objective = minimize expected total asset cost while ensuring a reasonable average patient cycle time Approach= optimize current process, redesign process and re-optimize. Release 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? Transfer to room Y OK? Arrive at ER Receive treatment Fill out registration Released N Admitted Into Hospital Current Process Receive treatment Transfer to room OK? Y Arrive at ER Released Fill out registration N Admitted Into Hospital 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 anintegration 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

More Related