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A hybrid-expert-system based tool for scheduling and
decision support
F.Franek, McMaster University+Terren Corp., Canada
V.L.Rosicky, Terren Corp., Canada
I.Bruha, McMaster University
Presenting TESS (Terren Expert System Shell), tightly
coupled with the relational database back-end, used for
scheduling of booking actions and decision support of
marketing activities in GREENWICHTM software system
for tour operators.
Slide 1
Need: most of tour operators in North America operate in
similar manner and very slim profit margin. Hence need for
some “standard” software for tour operators.
Problem: despite similarities, certain differences in the
sequence of booking actions across the whole business. Not
only different actions are taken, but there timing and
sequencing may be different.
Solution: kind of “standard” software that may be easily
tailored to the individual needs of each operator and that can
produce action plans based on the booking data accordingly.
Abstract problem: easy reconfiguration based on some rules,
action scheduling (planning) based on some rules.
Implementation: Rule-based expert system TESS!
Slide 2
Why TESS: Very flexible, very expressive
r1: (0.3*P(x,”abc”) [>=.3] & ~Q(x,y)) | R(2,3.4,y)
==and:f1,or:f2=>
0.7*~S(y,x) [>.6]
Why not TESS: Too flexible, too expressive, no “native”
database access.
Could not let users (of all people!!) to do it on their own, needed
to provide natural and integral access to database.
Solution: Design a language that limits the expressiveness and
flexibility, but allows “natural” expressions concerning database
access!!
SQL!!!
Slide 3
SELECT DepositWarning FROM Tess WHERE
AND booking_number=@bkg_number
AND agency_office=@ao_id
AND trip_id=@ts_id
AND trip_departure_date=@departute_date
AND BookingCompleted
AND ConfirmationLetterSent
AND (SELECT deposit FROM booking WHERE
booking_number=@bkg_number) = 0
AND (SELECT deposit_date FROM booking WHERE
booking_number=@bkg_number) >= @today
Slide 4
Data
Data
compiled TESS rule base
TESS native rules
TESS1
TESS2
temporary
TESS Rules
temporary
Relational
Database
ACTIVITY ENGINE
TESS INFERENCE ENG.
Data
Data
Actions
+
Activities
Schedule (Plan) of
Actions+Activities
Slide 5
Additional Advantage
TESS Rules
SQL QUERY
BUILDER
Rule Editor
Relational
Database
How is the scheduling done?
Day-to-day simulation of actions to produce the plan of
actions. The reason: the combinatorics of mutual interactions
is not conducive to analytical solution. Despite of this, the
natural constraints limit the combinatorial explosion to
Slide 6
tractable level.
CONCLUSION
For booking actions two-valued (1,0) logic suffices. Not so for
marketing activities, fuzzy-logic or other uncertainty-handling
approach necessary.
TESS natural for fuzzy-logic implemented through Certainty Value
Propagation Functions and and or. In the setting of marketing
activities we ran into the usual problem - where do the numbers
come from? - so we are experimenting with neural nets in the place
of CVPF’s and their training.
Slide 7