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Emmanuel Fernandez Associate Professor emmanuel@ececs.uc

Emmanuel Fernandez Associate Professor emmanuel@ececs.uc.edu. INTERESTS. Telecommunications Information Technology. Stochastic Models, Decision & Control Processes, Dynamic Programming. Basic Methodology. Algorithms, Software Tools. Operations & Logistics: Semiconductor fabs.

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Emmanuel Fernandez Associate Professor emmanuel@ececs.uc

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  1. Emmanuel Fernandez Associate Professor emmanuel@ececs.uc.edu

  2. INTERESTS Telecommunications Information Technology Stochastic Models, Decision & Control Processes, Dynamic Programming Basic Methodology Algorithms, Software Tools Operations & Logistics: Semiconductor fabs

  3. OVERVIEW • Phase 1: 1990-1996: Learning and Adaptive Systems, Models with Partial Information, Average Optimality Criteria. • Phase 2: 1994-1998: Non-standard Optimality Criteria, Modeling Applications, Algorithms & Software Tools. • Phase 3: 1998-Present: Risk-Sensitive Models, Security & Fault Management in Telecommunication Networks, Operational Methods in Semiconductor Manufacturing. • Over 61 refereed publications(6 b, 18+ j, 37 c) • Four Ph.D.s, 3 M.Sc., 18+ undergrad. RA’s. • Honors: • Tau Beta Pi Professor of the Year, David Rist Prize MORS, IEEE Life Member Fund Research Initiation Award (Eng. Foundation).

  4. OUTLINE • Motivation: Applications • Semiconductor manufacturing operations • Logistics • Information Networks • Fault & Security Management in communication networks • Routing in the Intelligent Network • Stochastic Decision & Control Models: • Optimality Criteria: Why Risk-Sensitivity? • Basic Research Risk sensitive results: • Optimality equations & the Vanishing Discount Approach (AC). • Modular functions & structured policies (DC).

  5. APPLICATIONS • Semiconductor Manufacturing: • Capacity expansion & allocation, • Preventive maintenance scheduling (AMD). • Information Networks: • Routing in the Intelligent Network (AT&T); • Security & fault management.. • Operations & Logistics: • Workforce management; • Scheduling military training resources (Army).

  6. Semiconductor Manufacturing:Capacity Expansion & Allocation • NSF/SRC Project at U. Maryland (PI’s: M. Fu & S. Marcus) • EF Sabbatical project (begun Fall 98) • EF liaison with industry (AMD) during 99 • Integrate transient product dynamics over entire fab life cycle: Markov Decision Process (MDP) models • allocating/adding tool and process capacity • dynamic uncertain demands (e.g., market shifts) • transient dynamics (e.g., technology shrinks/shifts) • Computational Investigation & Cost Modeling • Tool: SYSCODE (University of Arizona software) • Stochastic Systems Control and Decision Algorithms Software Laboratory • Find optimal policy for different parameters : • demand distribution • inventory cost and/or backlogging cost • Simple policies vs. optimal policy • Infinite horizon results vs. finite horizon A Markov Decision Process Model for Capacity Expansion and Allocation: IEEE Conf. Decision & Control, 1999.

  7. Industry Interaction:Advanced Micro Devices • Joint effort UA & ISR • On-site visits • Preventive maintenance • Within allowed window, when to do PM? • Information Technology: • “Torrents” of information! • Inefficient “manual” methods • Do not use available information • No models • Develop basic models & solution SRC/ISMT

  8. Information Technology & Telecommunication Networks • Routing calls in the Intelligent Network • Security and Fault Management • Software and Web tools: • SYSCODE • Computations & MATLAB Web course.

  9. The Intelligent Network:Routing Toll-free Calls (AT&T) • AT&T - UA project • Route 800- traffic to call centers • State information: • Workload at call centers • Incomplete information • Periodic updates • Solution: • POMDP model • Heuristic Policy Iteration Algorithm R. Milito & E. Fernandez: (a) IEEE TAC 1995, (b) IEEE Conf. Decision & Control 1995

  10. Information Networks: Security and Fault Management • Joint project with M. Shayman, U. Maryland. • Searching for faults in a given domain: • Scheduling tests • Single/Multiple faults • Test sequence constraints • Risk-sensitive criterion • Interchange argument: • Explicit scheduling rules • Qualitative analysis • Security intrusions: • Similar to fault management • 1999 Allerton Conference • IEEE TAC 2001 • Proposals

  11. Operations & Logistics: Scheduling Army Training Resources • LTC M. McGinnis: Ph.D. UA • Thousands of recruits/year • Many installations/bases • Decisions: • Company size • Length of training period • Number of companies to activate/retire each week. • Model: Inventory-type • Solution: Heuristic Policy Iteration Algorithm • Decision support software (in use by Army). Journal Military Op. Res. 1996 (Winner of David Rist Prize)

  12. Operations & Logistics: Scheduling Army Training Resources

  13. Logistics: Workforce Management • Recruit-retain-dismiss individuals • Intrinsic individual’s potential • Unobservable state • Random productivity • Bayesian stochastic model • The firm’s lifetime is long: • Average cost criterion • Adaptive control through Bayesian learning • Qualitative analysis of case studies Fdez, Jain, Lee, Rao, Rao: Management Science 1995.

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