When Theory Crashed
into Reality
Yossi Rissin
Chief Executive Officer, Visopt B.V
Roman Barták
Chief Scientist, Visopt B.V.
1
What is the talk
about?
Theory
Practice
Planning
vs.
scheduling
2
Planners from Venus
Researchers from Mars
3
A theoretical
factory
OM
machines
O N jobs
- each job consists of Oi operations with the
precedence relation (dedicated machines for
operations)
O Job
Shop Scheduling (JCC)
- Flow Shop Scheduling
- Open Shop Scheduling
4
JSS in Practice?
„I have never seen a Job Shop
Scheduling Problem in practice“
Wim Nuiten, ILOG
5
The Human
Factor
(Planners & plant personnel are motivated by:)
O Pride.
No disclosure of mistakes, problems
and weaknesses.
O Position in the organisation. Position is
protected by being nice to superiors,
serving many masters at once, gaining
professional respect.
O Future security. No disclosure of
knowledge, development of organisation
dependency.
6
The Human
Factor
(Planners & plant personnel are characterised by:)
O Politics.
Internal politics and power plays
are key factor in decision taking.
O Inconsistency. A human being is tend to
inconsistency and easily affected by
mood, environment and psychology
pressure.
O Unexpected. Human behaviour can be
determined and can be foreseen just by
statistical methods (big numbers, long
periods, distributions, etc.)
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The Ideal
Scheduling
Projects
O
Fully automatic factory based on robots and
AGV’s
-
O
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Engineering oriented
No one to argue with
No one knows better
More visibility, less surprises and fluctuations
New factory, not operating yet
-
Very stable, no fluctuations
No previous “know-how”
No old rules and procedures
No bad habits
No day-to-day-reality to confront the theory
Points Of View
O
Planners
- The planner’s world consists of products and their
flow
- “how can I produce this product now, and this one and that
one…”
- “How can I satisfy Mr. X from sales and Mr. Y from the plant
and the customer at the same time, without getting into new
troubles…”
O
Academy
- The engineer/researcher world consists of resources
and their usage
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- “How can I use the resources to get max X and min Y…”
- “How can I get, using objective metrics, a plan that for the
long term, will improve the plant efficiency…”
Not Invented
Here
O
“We are different…”
- Means, what you know is useless here
O
“Outsiders cannot understand it, it takes a lot of
time…”
- Means, you have to listen to us or to spend part of
your life here
O
“Methods that suite others cannot implemented
here…”
- Means, your experience and knowledge are
impressive, but you have to start from scratch
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Visopt View
O Visual
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Modelling Language
Inside Visopt
O Complex
resources
load
clean
cool
heat
clean
load
O General
heat
load
heat
unload
cool
clean
item flow
Alternative recipes
12
unload
unload
N-to-N relations
Recycling
Quality Issues
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Theoretical
Objectives
O Minimise
makespan
O Minimise
lateness (tardiness)
O Minimise
earliness
O Minimise
the number of set-ups
O Maximise
O ...
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resource utilisation
Quality Definition
O
Quality metrics by the user/planner
- “It should looks like the schedules I am doing…”
- “Good plan should resemble those I use to make
manually…”
- “In order to produce good plan you have to follow my
rules, know-how, procedures…”
- Good plan is a one that can be ‘sold’ to production
people easily
O
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Most of times there are no history records of the
manual plans to analyse their efficiency!
Visopt View
O Understand
the reason by asking Why!
minimise makespan
minimise lateness
minimise earliness
minimise number of set-ups
maximise resource utilisation
...
more satisfied demands
penalty for delays
storing cost
expensive set-ups
fix expenses
So what is the common objective?
MONEY
In Visopt we minimise cost (= maximise profit).
16
Bridging the Gap
Lessons learned
17
The Common
Language
O The
planner tells a “story” – how to
produce a given product or product family,
but cannot follow what was understood
- Tables and fields say nothing to the planner
and not resemble his world
modelling is the key – same, simple
language for the user and the computer –
the ability to draw the user story
O Visual
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Best Is Worse
O
“The Worst Enemy Of The Good Is The Best”
- A very good plan (based on objective metrics)
delivered after three hours is not relevant anymore –
the factory is not the one it was few hours ago
O
The art of real-life scheduling is to deliver a plan
which is good enough and fast enough:
- Good enough – the user cannot improve it in
reasonable time
- Fast enough – depends on the plant dynamics. One
hour can be too late for one plant and very fast to
another
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The Cure Is The
Pain
Most manual planning methods that are
considered as “know-how” are not relevant to
automated scheduling…
O What is considered as the “solid true” (Cure), is
many times simplifications of reality to enable
the manual scheduling (The pain)
O Extract the real knowledge from the overall
know-how with the help of plant experts
O
- Always ask Why, for everything, and never accept an
answer such as “this is the way to do it”
- If there is no solid reason behind the “fact” – ignore it
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Scheduling Is
Knowledge
Handling
O Scheduling
is not mathematics, but first of
all a knowledge handling process
- Capturing the real knowledge
- Mapping the knowledge so the user can verify
and update it
- Process it concerning its elusive nature
- Understand and overcome the accurate
mathematical metrics when dealing with
knowledge
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What is the real
difference?
Practitioner
Researcher
2 slides per hour talk
O only three words are
different on these
slides
O
O
78 slides per hour talk
Based on „real-life“ data (PACT 96)!
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Thank you!
Yossi Rissin
[email protected]
Roman Barták
[email protected]
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