1. Applications of Constraint Programming H.Simonis
9/21/2018

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

3. 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)
Different problems with common aspects
Planning
Scheduling
Resource allocation
Assignment
Placement
Logistics
Financial decision making
VLSI design

5. 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

6. 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

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

8. 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 (> 500 sites)

9. Example problem SEND +MORE ------ MONEY
Solve the cryptarithmetic puzzle
Each character represents a digit
Different characters have different values
Numbers do not start with 0
SEND
+MORE
------
MONEY

10. Example: Model top:- Variable definition Constraints between variables
[S, E, N, D, M, O, R, Y] :: 0..9,
alldifferent([S, E, N, D, M, O, R, Y]),
S \= 0, M \= 0,
1000 * S + 100*E + 10*N + D +
1000*M + 100*O + 10*R + E =
10000 *M *O + 100*N + 10*E + Y,
labeling([S, E, N, D, M, O, R, Y]).
Variable definition
Constraints between
variables
Search routine

11. Constraint reasoning Simplification (each variable occurs once)
1000*S in {1..9}+ 91*E in {0..9} + D in {0..9} + 10*R in {0..9} = 9000*M in {1..9} +900*O in {0..9} + 90*N in {0..9} + Y in {0..9}
Evaluation lhs/rhs
lhs in
rhs in
Merging of sides
constraint in
Consequence
M = 1
S = 9
O in {0..1}
Propagation of alldifferent
O = 0

12. Reasoning Re-evaluation of equality Re-evaluation, ...
1000*9+ 91*E in {2..8} + D in {2..8} + 10*R in {2..8} = 9000*1 +900*0 + 90*N in {2..8} + Y in {2..8}
lhs in , rhs in , eq in
N \= 2, E \= 8
Re-evaluation, ...
Continuing the process gives
M = 1, S = 9, O = 0, E in 4..7, N in 5..8, D in 2..8, R in 2..8, Y in 2..8

13. Starting labeling First variable is E, first value is 4
Propagation on equality gives
1000*9+ 91*4 + D in {2..8} + 10*R in {2..8} = 9000*1 +900*0 + 90*N in {5..8} + Y in {2..8}
results in N = 5, D = 8, R = 8, Y = 2
Propagation on alldifferent fails
Backtracking to last choice

14. First alternative Next value for E is 5 Propagation on equality gives
1000*9+ 91*5 + D in {2..8} + 10*R in {2..8} = 9000*1 +900*0 + 90*N in {5..8} + Y in {2..8}
results in N = 6, R = 8
Constraint propagation (alldifferent + equality) gives
D = 7, Y = 2
Problem solved

15. Points to remember
Even small problems create complex propagation chains
The same constraint can be woken several times in the same propagation loop
The order in which constraints are woken has an influence on the speed, but not on the result
Propagation continues until no further information is obtained
Search (under user control) required to find ground solution
Basic structure of finite domain program always the same
define variables
define constraints
user defined search
typical: pick variable and find value

16. Success Stories for Constraint Logic Programming
COSYTEC – IC-Parc
9/21/2018

17. 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

18. Five central topics Assignment Network Configuration Scheduling
Parking assignment
Platform allocation
Network Configuration
Scheduling
Production scheduling
Project planning
Transport
Lorry, train, airlines
Personnel assignment
Timetabling, Rostering
Train, airlines

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

20. AIR FRANCE - APACHE Stand allocation system Complex constraint problem
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

21. 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)

22. 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

23. FRANCE TELECOM - LOCARIM
Intelligent cabling system for large buildings
developed with Telesystemes for France Telecom
Application
input scanned drawing
specify requirements
Optimization
minimize cabling, drilling
Reduce switches
shortest path
Status
operational in 5 Telecom sites
generates quotations

24. 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

25. ATLAS (Monsanto) Scheduling system in chemical factory Life system
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 system
multiple schedulers
co-operative behavior Gr G F1 F2 Dr L1 L2 L3 L4
Packaging
Formulation
raw material
Holding
area
Out
finished
products

26. DASSAULT - PLANE Assembly line scheduling Two user system Optimisation
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

27. FINA - FORWARD Oil refinery production scheduling
joint development by TECHNIP and COSYTEC
incorporates ELF FORWARD LP tool
Schedules daily production
crude arrival -> processing -> 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

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

29. EDF- EVA Transportation of nuclear waste Constraints Status
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

30. SERVAIR - CREW Crew rostering system Problem solver Constraints Status
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%

31. Personnel Planning RAC (IC-Parc) OPTISERVICE (RFO)
Shifter (ERG Petroli)
Gymnaste (UCF)
MOSAR (Ministère de la JUSTICE)

32. RFO - OPTI SERVICE Assignment of technical staff to tasks Features
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+

33. Rostering Ministère de la JUSTICE Rostering of prison guards
Help to determine personnel needs
Simulation / Calculation of anonymous rosters
Provisional plans for one year
Development by Cisi and COSYTEC
10 regional sites for 200 prisons
Objectives :
planning obtained in minutes
was 3 weeks manually
reduction of overtime cost
improved personnel satisfaction
reduction of days lost

34. Case Study: A complex transportation problem
9/21/2018

35. TACT: Transport planning and resource scheduling
Transport planning and scheduling for SVF/Cargill UK
delivering chicken/turkeys from farms to factories
JIT arrival at factory
combined
planning
scheduling
assignment
multi-resource problem, interaction between resource types
Developed by COSYTEC
Operational since 2/95
Revising decision making process
formalizing current procedures
spread knowledge inside company

36. Problem Satisfy order book with available resources
Transport Qty of Type from Source to Sink
Schedule operations
Assign resources to activities
Depot
Tour generation
Start in depot
Connect orders to tours
Use of passive transport
End in depot

37. Integrated transport problem
Loading
Teams, Drivers, Forklifts, Lorries
Transport
Drivers, Lorries, Forklifts
Unloading
Drivers, Personnel, Storage Area, Lorries
Cleaning
Personnel, Lorries, Forklifts

38. Application Look
Menubar
List
Gantt chart
Details and status

39. Solver Architecture Orders How many trips? Plan
When to leave or to arrive?
Schedule
Who is doing what?
Assignment

40. Planning How many trips are required Merging of orders
lorry types
drag trailers
transport restriction
volume/weight limits
Merging of orders
same source/sink
same type
Fork lift requirements
resource needed for loading
take out with first trip
bring back with last trip
optimization

41. Scheduling On departure side On arrival side
all trips for same order must be scheduled together
no gaps
no concurrency
loading times depend on team, lorry type and product
On arrival side
JIT schedule
producer/consumer behavior
very limited storage area
limited storage time
minimum stock level required

42. Producer/Consumer Behavior

43. Assignment Find compatible resources for each activity
multiple resource problem
interaction between resources
conflict resolution
Respect constraints
working time
rest periods
work load
Balance use of resources of same type
teams
drivers
Minimize use of outside resources

44. User control The user is in command
modification of constraints
choice of strategies
relaxation of constraints
Different priorities of various users
in-time delivery
use of resources
need for overtime
balancing work load
Need for compromise
Improved reactivity
events
rescheduling

45. Problem solver Large scale, complex model
Inter-related problem solvers
Use of global constraints essential
cumulative
diffn
cycle
Explanation facilities
meta programming (Prolog feature)
relaxation of global constraints possible in CHIP
Re-scheduling/re-use of existing solution
Constraint solver / constraint check

46. 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

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

48. 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

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

50. 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

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

52. Evaluation Operational since spring 1995 Scheduling speed
8 hours manual work
15 min with TACT application
Return on investment (estimated by customer) in 6 month
transport fleet constant at increasing demand
Push button solution
little manual interaction
Application has stayed up-to-date for last five years
new types of lorries
changing status of catching teams
...

53. Statistics 10 month project duration 36000 lines of CHIP code
study, specification
development
implementation
36000 lines of CHIP code
complete application in CHIP
except AS/400 interface to ORACLE
7000 lines of code for solver
explanation facilities
automated constraint relaxation
specialized lower bound calculation

54. 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

55. Some pointers Constraint Archive Constraints Journal
Constraints Journal
Principles and Practice of Constraint Programming
CP99 in Washington,DC/ CP2000 in Singapore
Practical Application of Constraint Technologies and Logic Programming
On-line guide to Constraint Programming (R. Bartak)
K. Marriott, P.J. Stuckey
Programming with Constraints: An Introduction, MIT Press, 1998