Post on 26-Jan-2016
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Emmanuel FernandezECECS Dept.Univ. Cincinnati
Emmanuel FernandezAssociate Professor
emmanuel@ececs.uc.edu
Emmanuel FernandezECECS Dept.Univ. Cincinnati
INTERESTS
Stochastic Models,Decision & Control
Processes,Dynamic Programming
TelecommunicationsInformation Technology
Operations & Logistics:
Semiconductor fabs
Basic Methodology
Algorithms,Software Tools
Emmanuel FernandezECECS Dept.Univ. Cincinnati
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).
Emmanuel FernandezECECS Dept.Univ. Cincinnati
OUTLINE
• Motivation: Applications
– Semiconductor manufacturing operations
– Logistics
– Information Networkso Fault & Security Management in communication networkso 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).
Emmanuel FernandezECECS Dept.Univ. Cincinnati
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).
Emmanuel FernandezECECS Dept.Univ. Cincinnati
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.
Emmanuel FernandezECECS Dept.Univ. Cincinnati
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
Emmanuel FernandezECECS Dept.Univ. Cincinnati
Information Technology &Telecommunication Networks
• Routing calls in the Intelligent Network
• Security and Fault Management
• Software and Web tools:– SYSCODE
– Computations & MATLABWeb course.
Emmanuel FernandezECECS Dept.Univ. Cincinnati
The Intelligent Network:Routing Toll-free Calls (AT&T)
• AT&T - UA project
• Route 800- traffic tocall 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
Emmanuel FernandezECECS Dept.Univ. Cincinnati
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
Emmanuel FernandezECECS Dept.Univ. Cincinnati
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)
Emmanuel FernandezECECS Dept.Univ. Cincinnati
Operations & Logistics: Scheduling Army Training Resources
Emmanuel FernandezECECS Dept.Univ. Cincinnati
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.