Emmanuel Fernandez
ECECS Dept.
Univ. Cincinnati
Emmanuel Fernandez
Associate Professor
emmanuel@ececs.uc.edu
Emmanuel Fernandez
ECECS Dept.
Univ. Cincinnati
INTERESTS
Telecommunications
Information
Technology
Algorithms,
Software Tools
Stochastic Models,
Decision & Control
Processes,
Dynamic Programming
Operations &
Logistics:
Semiconductor fabs
Basic
Methodology
Emmanuel Fernandez
ECECS Dept.
Univ. Cincinnati
OVERVIEW
• Phase 1: 1990-1996: Learning and Adaptive Systems, Models
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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 Fernandez
ECECS Dept.
Univ. Cincinnati
OUTLINE
• Motivation: Applications
– Semiconductor manufacturing operations
– Logistics
– Information Networks
o Fault & Security Management in communication networks
o 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 Fernandez
ECECS 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 Fernandez
ECECS 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
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– 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 Fernandez
ECECS 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:
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“Torrents” of information!
Inefficient “manual” methods
Do not use available information
No models
• Develop basic models & solution
SRC/ISMT
Emmanuel Fernandez
ECECS Dept.
Univ. Cincinnati
Information Technology &
Telecommunication Networks
• Routing calls in the
Intelligent Network
• Security and Fault
Management
• Software and Web tools:
– SYSCODE
– Computations & MATLAB
Web course.
Emmanuel Fernandez
ECECS Dept.
Univ. Cincinnati
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
Emmanuel Fernandez
ECECS 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 Fernandez
ECECS 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 Fernandez
ECECS Dept.
Univ. Cincinnati
Operations & Logistics: Scheduling
Army Training Resources
Emmanuel Fernandez
ECECS 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.