Constraint Programming for Industrial Applications H.Simonis

1
Constraint Programmingfor Industrial
ApplicationsH.Simonis
2
What is common among

• The production of Mirage 2000 fighter aircraft
• The personnel planning for the guards in all
  French jails
• The production of Belgian chocolates
• The selection of the music programme of a Pop
  music radio station
• The design of advanced signal processing chips
• The print engine controller in Xerox copiers

They all use constraint programming to solve
their problem
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Constraint Programming - in a nutshell

• Declarative description of problems with
• Variables which range over (finite) sets of
  values
• Constraints over subsets of variables which
  restrict possible value combinations
• A solution is a value assignment which satisfies
  all constraints
• Constraint propagation/reasoning
• Removing inconsistent values for variables
• Detect failure if constraint can not be satisfied
• Interaction of constraints via shared variables
• Incomplete
• Search
• User controlled assignment of values to variables
• Each step triggers constraint propagation
• Different domains require/allow different methods

4
Constraint Satisfaction Problems (CSP)

• Combinatorial Optimization Problems
• Different problems with common aspects
• Planning
• Scheduling
• Resource allocation
• Assignment
• Placement
• Logistics
• Financial decision making
• VLSI design

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Characteristics of these problems

• There are no general methods or algorithms
• NP-completeness
• different strategies and heuristics have to be
  tested.
• Requirements are quickly changing
• programs should be flexible enough to adapt to
  these changes rapidly.
• Decision support required
• co-operate with user
• friendly interfaces

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Techniques behind CLP
Predicate logic Unification Non-determinism
Logic Programming
Constraint propagation Consistency checking Demons
CLP tools
Operations Research
Artificial Intelligence
Simplex Linear programming Integer
programming Implicit enumeration Branch
bound Flow algorithms Scheduling methods
Graph theory Combinatorics Spatial data
structures Equation solving methods
Mathematics
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Eclipse

• Prolog based constraint toolkit
• Developed first at ECRC, then IC-Parc
• Easy to develop new constraint methods
• Sets of existing methods/libraries
• Basis of problem solvers for PT
• Widely used for academic work (gt 500 sites)

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CLP tool history
logic based
Other base language
ALICE
Prolog III
CLP(R)
CHIP
CHIP SEPIA
CAL
Decision Power
Charme
SNI Prolog
BNR
Pecos
cc(FD)
Charme Object
Prolog IV
CHIP V4
Solver
Eclipse
CHIP lib
Claire
CHIP V5
Oz
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Classification of CLP solvers

• Computation domain
• Deterministic solver (search under user control)
• Efficient methods
• More than one application
• Complete/incomplete
• decide satisfiability of constraint sets
• complexity of solver/ simplicity of programs
• Methods
• syntactic domain independent
• semantic domain dependent

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Constraint system structure
Constraints
Incomplete solver
Complete solver
Boolean
Boolean
Finite domains
Rational
Others
Others
Interval
Constraint solver
Control
Domain concept
Heuristics
Back tracking
Meta- heuristics
semantic
syntactic
Value selection
Variable selection
numerical
symbolic
element
disequality
inequality
equality
cumulative
diffn
cycle
among
sequence
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Computation domains

• Finite domains
• Linear arithmetic
• Boolean
• Pseudo Boolean
• Non-linear arithmetic
• Intervals
• Sets, sequences, lists

the initial domains in Prolog III, CLP(R) and
CHIP
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Incomplete finite domain solver

• Domain
• finite sets of values
• subsets of natural numbers
• Need for enumeration
• Classification criteria
• constraint granularity
• richness of constraint sets
• propagation results
• user definable constraints/control
• Methods
• explicit domain representation
• bound propagation/ removal of interior values
• heuristics based on domains

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Need for global constraints
1
X
X in 2,3
Y
2
Y in 2,3
?
U in 1,2,3,4
Z
3
Z in 1,3
U
4
local reasoning, no action
global reasoning, detect implications by
bi-partite matching
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Global constraints

• Work on sets of variables
• Global conditions, not local constraints
• Semantic methods
• Operations Research
• Spatial algorithms
• Graph theory
• Network flows
• Building blocks (high-level constraint
  primitives)
• Multi-purpose
• As general as possible
• Usable with other constraints
• Very strong propagation
• Acceptable algorithmic complexity

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Constraint morphology
precedence
diffn
cumulative
sequence
cycle
case
among
alldifferent
setup
disjunctive
prod/cons
permutation
\
gt, distance
atmost, atleast
circuit
element
Different
Dependency
Tour
Resource
Order
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Search tree visualization

• Generation of search tree representation at
  run-time
• Shows parent child relation, failed sub-trees,
  success nodes
• In examples here
• leaf failure nodes suppressed
• failure trees not collapsed
• Interface a set of simple meta-call predicates
• Supported by work in DISCIPL Esprit project
• Declarative debugging
• Visualization of constraint programming results
• Understand behaviour of different strategies

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Search tree tool
Menubar
Panel
Tree view
Other Views, here domain state
Info Area
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Example of tree search
credit(X1..X10, 8, 10, my_delete,
my_indomain, 4, part(1,2)),
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Success Stories for Constraint Logic Programming

• COSYTEC IC-Parc

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Overview

• Hardware design
• Compilation
• Financial problems
• Placement
• Cutting problems
• Stand allocation
• Air traffic control
• Frequency allocation
• Network configuration
• Product design
• Production step planning
• Production sequencing

• Production scheduling
• Satellite tasking
• Maintenance planning
• Product blending
• Time tabling
• Crew rotation
• Aircraft rotation
• Transport
• Personnel assignment
• Personnel requirement planning

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Five central topics

• Assignment
• Parking assignment
• Platform allocation
• Network Configuration
• Scheduling
• Production scheduling
• Project planning
• Transport
• Lorry, train, airlines
• Personnel assignment
• Timetabling, Rostering
• Train, airlines

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Stand allocation

• HIT (ICL Hong-Kong)
• assign ships to berths in container harbour
• developed with ECRCs version of CHIP
• then using DecisionPower (ICL)
• first operational constraint application
  (1989-90)
• APACHE (COSYTEC)
• stand allocation for airport
• Refinery berth allocation (ISAB)
• where to load/unload ships in refinery

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AIR FRANCE - APACHE

• Stand allocation system
• Originally developed for Air France, CDG2
• Packaged for large airports
• Complex constraint problem
• Technical operational constraints
• Incremental re-scheduler
• Cost model
• Max. nb passengers in contact
• Min. towing, bus usage
• Benefits and status
• Quasi real-time re-scheduling
• KAL, Turkish Airlines

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Network configuration

• RiskWise (PT)
• Locarim (France Telecom, COSYTEC)
• cabling of building
• Planets (UCB, Enher)
• electrical power network reconfiguration
• Load Balancing in Banking networks (ICON)
• distributed applications
• control network traffic
• Water Networks (UCB, ClocWise)

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RiskWise

• Traffic analysis/optimization for IP networks
• Find out end-to-end communication flows in the
  network of an ISP
• Suggest changes in network which optimize
  investment
• Handle incomplete data

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Production scheduling

• Atlas (Monsanto)
• Herbizide production
• Cerestar (OM Partners)
• Glucose production
• KAPSS (Mitsubishi Chemical)
• offset printing plates
• Trefi Metaux (Sligos)
• heavy industry production scheduling
• Michelin
• rubber blending, rework optimization

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DASSAULT - PLANE

• Assembly line scheduling
• developed by Dassault Aviation for Mirage 2000
  Jet/ Falcon business jet
• Two user system
• production planning 3-5 years
• commercial what-if sales aid
• Optimisation
• requirement to balance schedule
• minimise changes in production rate
• minimise storage costs
• Benefits and status
• replaces 2 week manual planning
• operational since Apr 94
• now used in US for business jets

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FINA - FORWARD

• Oil refinery production scheduling
• joint development by TECHNIP and COSYTEC
• incorporates ELF FORWARD LP tool
• Schedules daily production
• crude arrival -gt processing -gt delivery
• design, optimize and simulate
• Product Blending
• explanation facilities
• handling of over-constrained problems
• Status
• generic tool developed in 240 man days
• operational since June 94
• Operational at FINA, ISAB, BP

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Transport

• By Air
• AirPlanner (PT)
• Daysy (Lufthansa)
• Pilot (SAS)
• By Road
• Wincanton (IC-Parc)
• TACT (SunValley)
• EVA (EDF)
• By Rail
• CREW (Servair)
• COBRA (NWT)

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EDF- EVA

• Transportation of nuclear waste
• developed by GIST COSYTEC
• plans evacuation and transport for 54 sites
• Constraints
• availability of transport vehicles and vessels
• no and capacity of storage tanks
• compatibility of waste to vessels
• size of convoy, time
• Status
• operational since Oct 94
• 6 month plan in 5 minutes

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SERVAIR - CREW

• Crew rostering system
• assign service staff to TGV train timetable
• joint implementation with GSI
• Problem solver
• generates tours/cycles
• assigns skilled personnel
• Constraints
• union, physical, calendar
• Status
• operational since Mar 1995
• cost reduction by 5

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Personnel Planning

• RAC (IC-Parc)
• OPTISERVICE (RFO)
• Shifter (ERG Petroli)
• Gymnaste (UCF)
• MOSAR (Ministère de la JUSTICE)

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RFO - OPTI SERVICE

• Assignment of technical staff to tasks
• overseas radio broadcasting network - Radio
  France Outre-mer
• joint development by GIST and COSYTEC
• 250 journalists and technicians
• Features
• schedule manually, check, automatic
• rule builder to specify cost formulas
• Optimization
• minimize overtime, temporary staff
• compute cost of schedule
• Status
• operational since 1997
• installed worldwide in 8 sites
• Now used for other companies
• TSR
• Canal

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Case Study Semi Process Industry
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ATLAS (Monsanto, Antwerp)

• Scheduling system in chemical factory
• herbicide manufacturing and packaging
• integrated inventory control system
• Life system
• first phase operational since July 93
• second phase operational since March 94
• major revision in 98 (SAP connection)
• Developed jointly by
• OM Partners (Antwerp)
• COSYTEC
• Multi-user PC/UNIX system
• multiple schedulers
• co-operative behavior

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Manufacturing Process
Formulation
Packaging
intermediate products
Outside
G
finished products
Dr
Holding area
F1
L1
raw material
L2
F2
L3
Gr
L4
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Dynamic Scheduling Environment
Sales forecast (6 month horizon)
Orders (delay 1day to 4 weeks)
Stock level
update planned/realized
Data base
Arrivals (lead time 3 weeks)
Production Schedule
Shop floor data
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Scheduling Process
Priority orders
Users
Schedulers
Old schedule
Fine tuning of sequence
Factory
Packaging
Formulation
medium/long term impact
Production
Purchasing
Production problems
Visualization of schedule (stock
level, delays, down times...)
New Schedule
short term reaction
Re-scheduling Formulation
Re-scheduling Packaging
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Architecture
X 25
Scheduling CHIP
USA
ORACLE
Users PC / XWindowsGUI
HP UNIX
Purchasing
Planner Factory
SchedulerFormulation
Scheduler Packaging
Production manager
Ethernet
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The system

• Connection to other systems
• Data download/upload from/to local and remote
  mainframes
• End-user connection via PCs
• Data base
• Local on UNIX machine
• ORACLE SQL standard
• Forms/reports facilities
• Multi-user tool
• Multiple schedulers for parts of production
• Inventory control
• Production
• Multiple scenarios one schedule

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Constraint solver

• Constraints handled
• Machine choice
• Sequence/ machine dependent set-up
• Manpower limits
• Component consumer constraints
• Finished product producer constraint
• Producer/consumer constraints for intermediate
  products
• Production planning for batch part
• Number of batches for intermediate products
• Optimum batch sizing
• Limited stock levels

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The Cumulative global constraint

• Cumulative constraint
• Resource limits over periods of time
• Upper/lower limits
• Soft/hard limits
• Gradual constraint relaxation
• Application
• Resource restrictive scheduling, producer
  consumer constraints, disjunctive schedule,
  manpower constraints, overtime

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Cumulative

• Methods
• obligatory parts
• task intervals
• available space
• many more (25000 lines of C code)
• Concepts
• one constraint may be used for different purposes

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The Diffn global constraint

• Diffn constraint
• non overlapping areas on n-dimensional rectangles
• distances between rectangles
• limit use of areas
• relaxation
• Application
• layout, packing, resource assignment, setup,
  distribution planning, time-tabling

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Diffn

• Methods
• obligatory parts
• region intervals
• max flow on assignment
• available space
• many others (32000 lines of C code)
• Concepts

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The Cycle global constraint

• Cycle constraint
• Finds cycles in directed graphs with minimal cost
• Assign resources, find compatible start dates
• Applications
• Tour planning, personnel rotation, distribution
  problems, production sequencing

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Cycle

• Methods
• connected components
• bi-partite matching
• non-oriented graph concepts
• shortest/longest paths
• micro-rules
• many others (38000 lines of C code)
• Concepts

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ATLAS - Summary

• End-user system
• Direct manipulation
• Constraints not visible to user
• Changes/strategies expressed via interface
• Complex scheduling problem
• Real-life, not pure problem
• Interaction/conflicts between constraints
• Incremental expression with constraints
• Development effort (250 days)
• Integration
• Data base reporting
• Graphical interface
• Problem solver

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The CHIP system Constraints and ...
Application
XGIP
QUIC
Objects
Solvers
CHIP
Application
CHIP
CHIP
EMC/CLIC
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Behind the scene

• CHIP kernel 175000 lines
• Prolog 15000
• Core Constraints 20000
• Global Constraints 140000
• Interfaces 5000 lines
• CLIC
• EMC
• QUIC
• XGIP graphics 20000 lines
• Graphics libraries 17000 lines

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A typical decision support application
Solver
GUI
DATA
Reporting
dB
Libraries
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Application framework

• Building blocks - data management and GUI
• description, upload, download, windows, Gantt
  chart, lists, diagrams, panels
• Built on top of comprehensive data model
• single point of reference, maintenance,
  extensions
• General enough for different application domains
• manufacturing, transport, chemical, human
  resources
• Obvious benefits
• code size, application quality, development
  speed, decreased maintenance, etc.

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Data model

• Single point of reference
• Object model expressed in CHIP
• Meta program generates dB model, hooks to GUI
• Provides I/O to save and load object model

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Needs of an industrial system

• Lines of code for a live application
• Development speed, tools, risk
• Integration
• User interface
• Solver a must

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Benefits of CLP approach

• Short development time
• Fast prototyping
• Refining of modelling
• Same tool used for prototyping/production
• Compact code size
• Ease of understanding
• Maintenance
• Compositional development
• Changing requirements are localized
• No need to understand all aspects of problem to
  add/change constraint
• Good performance
• Fast answer
• Good results
• Optimal solutions rarely required
• Adapt solution to problem, not vice versa
• Bespoke application development

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The Future Constraint Application Generators

• CHIP Factory

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Approach

• CHIP Factory
• Program generator for C
• CHIP constraint library
• Generates (complete) application from
  specification
• data model
• user interface
• data base
• solver

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Problem Description

• Example application from LISCOS Project (BASF)
• Two stage, continuous process scheduling problem
• First stage production
• intermediate products (10)
• batch based process
• campaign based schedule
• setup times/costs on one line
• Intermediate product storage
• producer/consumer constraints
• Second stage production
• finished products (100)
• machine choice, machine restrictions
• variable production rates on machines

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Problem Description (2)
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Object Model
61
GUI Design
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Constraint Model

• DisjTask constraints
• MachineChoiceTask constraints
• DisjMachine resource constraint
• Machine choice resource constraint
• Producer/Consumer constraint

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DisjTask constraints

• post(end_var start_var duration_var)
• post(duration_var solver.convertDurationToDomai
  n(duration))

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MachineChoice constraint

• diffn_entries(entries)
• forall(task,machine_choice_task,
• entries.add(task.start_var,task.resource_var,task
  .duration_var,1)
• )
• diffn_post(entries)

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Finished Application
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Summary

• Large scale problems can be solved with CLP
• Very high level language framework allows rapid
  development
• in industrial terms
• Complex, customer specific conditions can be
  handled
• Global constraints allow
• Expression
• Resolution of complex problems
• Most important
• Ease of modelling
• Speed of development
• Reactivity to customer demands

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Some pointers

• Constraint Archive
• www.cs.unh.edu/ccc/archive
• Constraints Journal
• www.wkap.nl/journals/constraints
• Principles and Practice of Constraint Programming
• www.cs.ualberta.ca/ai/cp
• CP99 in Washington,DC/ CP2000 in Singapore
• Practical Application of Constraint Technologies
  and Logic Programming
• www.practical-applications.co.uk/PACLP99
• On-line guide to Constraint Programming (R.
  Bartak)
• http//kti.mff.cuni.cz/bartak/constraints
• K. Marriott, P.J. Stuckey
• Programming with Constraints An Introduction,
  MIT Press, 1998