Roman Bart

1
IPS in complex and dynamic areasVisopt
Experience

• Roman BartákVisopt B.V. (NL) / Charles
  University (CZ)

2
Talk outline

• Preliminaries planning vs. scheduling constraint
  technology in a nutshell
• Complex Worlds transition schemes item flows
• Dynamic Worlds handling problem changes
• Conclusions complex demo

3
PreliminariesPlanning, scheduling, and
constraints
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Terminology
The planning task is to find out a sequence of
actions that will transfer the initial state of
the world into a state where the desired goal is
satisfied
The scheduling task is to allocate known
activities to available resources and time
respecting capacity, precedence (and other)
constraints
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IPS

• Intelligent planning scheduling
• Integrated planning scheduling

Planning
Scheduling
Planning
Scheduling
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Integration

• When do we need to integrate more?
• If there are too frequent backtracks from
  scheduling to planning.
• Improving the planner may help.
• If existence of the activity depends on
  allocation of other activities.
• We call it a process-dependent activity.
• Foregoing planning of activities cannot be done
  there!

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Process-dep. activity

• re-heating
• re-cycling

heat
process
process
setup
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Constraint technology

• based on declarative problem description via
• variables with domains (sets of possible
  values)e.g. start of activity with time windows
• constraints restricting combinations of
  variablese.g. endA lt startB
• constraint optimisation via objective
  functione.g. minimise makespan
• Why to use constraint technology?
• understandable
• open and extendible
• proof of concept

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Scheduling

• Constraints unary resource constraint
• Search strategies ordering of activities
• Decide first the activities with a minimal slack
• Choose ordering leading to a bigger slack

B (4)
C (5)
A (2)
A
B
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Complex Worldshandling complex resources

• Visopt experience

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Motivation

• Planning scheduling in complex areas
• resources with complex behaviour
• setup and cleaning activities
• complex relations between resources
• alternative recipes
• re-cycling
• Some examples
• mould change in plastic industries
• acid cleaning in food industries
• re-cycling in petrochemical industries
• ...

12
Complex resources

• Resource behaviour is described via
• a state transition diagram
• activity counters per state
• global activity counters
• e.g. force a given state (cleaning)after a given
  number of activities

A
A
A
B
C
C
C
C
A
A
A
C
C
B
A
A
A
clean
load
heat
unload
load
heat
unload
cool
clean
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Handling transitions

• A slot model of resources
• slot is a space for activity in the resource
• variables describe activity parameters in the
  slot
• state
• counters
• times
• constraints

• slots can slide in time
• slots cannot swap their position

state
counters
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Item flows

• Relations between resources are described
  viasupplier-consumer dependencies

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Handling dependencies

• Basic ideas
• when the activity is known (located to a slot)
  introduce related activities (suppliers/consumers)
  
• the solver is selecting among introduced
  activities (planning within scheduling)

Looking for suppliers
?
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Dynamic Worldshandling problem changes

• academic research

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Motivation

• Planning, scheduling timetabling problems
• changes in the problem formulation
• minimal changes to the solution
• other features
• over-constrained problems
• hard-to-solve problems
• Some examples
• gate allocation in airports
• production scheduling
• timetabling problems
• ...

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Soft solutions

• Return some solution even if no solution exists
• Soft constraints
• User assigns preferences/weights to the
  constraints.
• Motivation
• Some constraints express preferences rather than
  requirements.
• Return some solution even if one does not know in
  advance that no solution exists.
• Soft (incomplete) solutions
• Assign as many variables as possible (i.e.,
  without any conflict).
• Motivation
• In school timetabling assign as many courses as
  possible.
• Note
• Can be applied to hard-to-solve problems.

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Perturbations

• initial problem ? initial solution ?
• new problem ?
• Perturbation
• change in the new solution ? for ? w.r.t. ?
• The task
• Find a solution of the changed problem that
  minimises the number of perturbations.
• ? Minimal Perturbation Problem ?

Mapping between objects/variables
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MPP example

• Random placement problem
• Place a random set of rectangles (no overlaps) to
  a rectangular placement area

3
9
7
9
2
2
6
Change object 1 must be in row B
1
1
5
4
8
10
Solution of the changed problem with 3
perturbations
Initial problem
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Solving MPP

• Principle
• solve the changed problem
• use the initial solution as a guide
• Basic solver
• branch-and-bound
• limited assignment number search
• limit the number of attempts to assign a value to
  the variable
• ? linear search space (lan_limit
  number_of_variables)
• Guide
• first, assign values to variables with
  perturbation
• prefer values which minimise additional
  perturbations

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ConclusionsDemo
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Demo problem

• Parallel (with worker) and serial production
• Re-cycling of by-products after 3 parallel
  activities
• Synchronised cleaning after 8 production
  activities
• Learning curve and working time for the worker

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Expected solutions
25
IPS in complex and dynamic areasVisopt
Experience

• Roman BartákVisopt B.V. (NL) /Charles University
  (CZ)