1. Examples for Discrete Constraint Programming
Belaid MOA
UVIC, SHRINC Project

2. Examples Map coloring Problem Cryptarithmetic N-queens problem
Magic sequence
Magic square
Zebra puzzle
Uzbekian puzzle
A tiny transportation problem
Knapsack problem
graceful labeling problem

3. Map Coloring Problem (MCP)
Given a map and given a set of colors, the problem is how to color the map so that the regions sharing a boundary line don’t have the same color.

4. Map Coloring Problem (MCP)
MCP can be viewed as a graph coloring problem: a b b c a d c d

5. Modeling MCP MCP can be Modeled as a CSP:
A set of variables representing the color of each region
The domain of the variables is the set of the colors used.
A set of constraints expressing the fact that countries that share the same boundary are not colored with the same color.

6. Modeling (MCP) Example - Australia Variables = {a,b,c,d,e,f,g}
Domain = {red, blue, green}
Constraints:
a!=b, a!=c
b!=c, b!=d
c!=e, c!=f
e!=d, e!=f a b c d f e g

7. Coding MCP OPL studio - Ilog Solver: 54 solutions found
enum Country={a,b,c,d,e,f,g};
enum Color = {red, green, blue};
var Color color[Country];
solve{
color[a]<>color[b];color[a]<>color[c];
color[b]<>color[c];color[b]<>color[d];
color[c]<>color[e];color[c]<>color[f];
color[e]<>color[d];color[e]<>color[f]; };

8. Coding MCP

9. Coding MCP ECLiPSe :- lib(fd). coloured(Countries) :-
Countries=[A,B,C,D,E,F,G],
Countries :: [red,green,blue],
ne(A,B), ne(A,C),
ne(B,C), ne(B,D),
ne(C,E), ne(C,F),
ne(E,D), ne(E,F),
labeling(Countries).
ne(X,Y) :- X##Y.

10. Coding MCP

11. Cryptarithmetic
The problem is to find the digits corresponding to the letters involved in the following puzzle:
SEND +
MORE
MONEY

12. Cryptarithmetic Modeling
A CSP model for Cryptarithmetic problem:
Variables={S,E,N,D,M,O,R,Y}
Domain={0,1,2,3,4,5,6,7,8,9}
Constraints
The variables are all different
S!=0, M!=0
S*103+E*102+N*10+D {SEND}
+ M*103+O*102+R*10+E {MORE}= M*104+O*103+N*102+E*10+Y {MONEY}

13. Coding Cryptarithmetic
OPL Studio - ILog Solver
enum letter = {S,E,N,D,M,O,R,Y};
range Digit 0..9;
var Digit value[letter];
solve{
alldifferent(value);
value[S]<>0;
value[M] <>0;
value[S]*1000+value[E]*100+value[N]*10+value[D]+
value[M]*1000+value[O]*100+value[R]*10+value[E]
= value[M]*10000+value[O]*1000+value[N]*100+value[E]*10+value[Y] };

14. Coding Cryptarithmetic

15. Coding Cryptarithmetic
ECLiPSe
:- lib(fd).
sendmore(Digits) :-
Digits = [S,E,N,D,M,O,R,Y],
Digits :: [0..9],
alldifferent(Digits),
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(Digits).

16. Coding Cryptarithmetic

17. N-Queens Problem
The problem is to put N queens on a board of NxN such that no queen attacks any other queen.
A queen moves vertically, horizontally and diagonally

18. Modeling N-Queens Problem
The queens problem can be modeling via the following CSP
Variables={Q1,Q2,Q3,Q4,...,QN}.
Domain={1,2,3,…,N} represents the column in which the variables can be.
Constraints
Queens not on the same row: already taken care off by the good modeling of the variables.
Queens not on the same column: Qi != Qj
Queens not on the same diagonal: |Qi-Qj| != |i-j|

19. Coding N-Queens Problem
OPL Studio - ILog Solver
int n << "number of queens:";
range Domain 1..n;
var Domain queens[Domain];
solve {
alldifferent(queens);
forall(ordered i,j in Domain) {
abs(queens[i]-queens[j])<> abs(i-j) ; };

20. Coding N-Queens Problem

21. Coding N-Queens Problem
ECLiPSe
:-lib(fd).
nqueens(N, Q):-
length(Q,N),
Q::1..N,
alldifferent(Q),
( fromto(Q, [Q1|Cols], Cols, []) do
( foreach(Q2, Cols), param(Q1), count(Dist,1,_) do
Q2 - Q1 #\= Dist, Q1 - Q2 #\= Dist ) ),
search(Q).
search([]).
search(Q):-
deleteff(Var,Q,R),
indomain(Var), search(R).

22. Coding N-Queens Problem

23. Magic sequence problem
Given a finite integer n, the problem consists of finding a sequence S = (s0,s1,…,sn), such that si represents the number of occurrences of i in S.
Example:
(2, 0, 2, 0)
(1,2,1,0)

24. Modeling MSP MSP can be modeled by the following CSP
variables: s0,s1, …,sn-1
Domain:{0,1,…,n}
constraints:
number of occurences of i in (s0,s1, …,sn-1) is si.
Redundant constraints:
the sum of s0,s1, …,sn-1 is n
the sum of i*Si, i in [0..n-1] is n

25. Coding MSP OPL - ILog Solver int n << "Number of Variables:";
range Range 0..n-1;
range Domain 0..n;
int value[i in Range ] = i;
var Domain s[Range];
solve {
distribute(s,value,s); //global constraint s[i]=sum(j in Range)(s[j]=i)
sum(i in Range) s[i] = n; //redundant constraint
sum(i in Range) s[i]*i = n; //redundant constraint };

26. Coding MSP ECLiPSe :- lib(fd). :- lib(fd_global). :- lib(fd_search).
solve(N, Sequence) :-
length(Sequence, N),
Sequence :: 0..N-1,
( for(I,0,N-1),
foreach(Xi, Sequence),
foreach(I, Range), param(Sequence) do
occurrences(I, Sequence, Xi) ),
N #= sum(Sequence), % two redundant constraints
N #= Sequence*Range,
search(Sequence, 0, first_fail, indomain, complete, []).%search procedure

27. Magic square problem
A magic square of size N is an NxN square filled with the numbers from 1 to N2 such that the sums of each row, each column and the two main diagonals are equal.
Example:

28. Modeling Magic square pb
Magic square problem can be viewed as a CSP with the following properties:
Variables: the elements of the matrix representing the square
Domain: 1..N*N
Constraints:
magic sum = sum of the columns = sum of the rows = sum of the down diagonal = sum of the up diagonal
Remove symmetries
Redundant constraint:
magic sum = N(N2+1)/2

29. Coding Magic square pb OPL Studio:
int N << "The length of the square:";
range Dimension 1..N;
range Values 1..N*N;
int msum = N*(N*N+1)/2; //magic sum
var Values square[1..N,1..N];
solve {
//The elements of the square are all different
alldifferent(square);
//the sum of the diagonal is magic sum
msum = sum(i in Dimension)square[i,i];
//the sum of the up diagonal is magic sum

30. Coding Magic square pb msum = sum(i in Dimension)square[i,N-i+1];
//the sum of the rows and columns are all magic sum
forall(i in Dimension){
msum = sum(j in Dimension)square[i,j];
msum = sum(k in Dimension)square[k,i]; };
//remove symmetric behavior of the square
square[N,1] < square[1,N];
square[1,1] < square[1,N];
square[1,1] < square[N,N];
square[1,1] < square[N,1];

31. Magic square problem

32. Coding Magic square pb ECLiPSe: :- lib(fd). magic(N) :- Max is N*N,
Magicsum is N*(Max+1)//2,
dim(Square, [N,N]),
Square[1..N,1..N] :: 1..Max,
Rows is Square[1..N,1..N],
flatten(Rows, Vars),
alldifferent(Vars),

33. Coding Magic square pb %constraints on rows ( for(I,1,N),
foreach(U,UpDiag),
foreach(D,DownDiag),
param(N,Square,Sum) do
Magicsum #= sum(Square[I,1..N]),
Magicsum #= sum(Square[1..N,I]),
U is Square[I,I],
D is Square[I,N+1-I] ),
%constraints on diagonals
Magicsum #= sum(UpDiag),
Magicsum #= sum(DownDiag),

34. Coding Magic square pb %remove symmetry Square[1,1] #< Square[1,N],
Square[1,1] #< Square[N,N],
Square[1,1] #< Square[N,1],
Square[1,N] #< Square[N,1],
%search
labeling(Vars),
print(Square).
Unfortunately, ECLiPSe ran for a long time without providing any answer. ECLiPSe had also the same behavior for a similar code from ECLiPSe website.

35. A Tiny Transportation problem
How much should be shipped from several sources to several destinations
Demand Qty
Supply cpty
Source
Qty Shipped
Destination a 1 x 11 1 b 1 1 x 12 x 1n x 21 a 2 2 2 b 2 x 22 x 2n : : : : a m n b m n

36. A Tiny Transportation problem
3 plants with known capacities, 4 clients with known demands and transport costs per unit between them.
200
500 1 1
400 2
Find qty shipped? 2
300
300 3 3
400 4
100

37. Modeling TTP TTP can be modeled by a CSP with optimization as follows:
Variables: A1,A2,A3,B1,B2,B3,C1, C2, C3,D1,D2,D3
Domain: 0.0..Inf
constraints:
Demand constraints:
A1+A2+A3=200; B1+B2+B3=400; C1+C2+C3=300; D1+D2+D3=100;
Capacity constraints:
A1+B1+C1+D1500; A2+B2+C2+D2300; A3+B3+C3+D3400;
Minimization of the objective function:
10*A1 + 7*A2 + 11*A3 + 8*B1 + 5*B2 + 10*B3 +
5*C1 + 5*C2 + 8*C3 + 9*D1 + 3*D2 + 7*D3

38. Coding TTP OPL Studio - Cplex Solver
var float+ productA[Range];var float+ productB[Range];
var float+ productC[Range]; var float+ productD[Range];
minimize
10*productA[1] + 7*productA[2] + 11*productA[3] +
8*productB[1] + 5*productB[2] + 10*productB[3] +
5*productC[1] + 5*productC[2] + 8*productC[3] +
9*productD[1] + 3*productD[2] + 7*productD[3]
subject to {
productA[1] + productA[2] + productA[3] = 200;
productB[1] + productB[2] + productB[3] = 400;
productC[1] + productC[2] + productC[3] = 300;
productD[1] + productD[2] + productD[3] = 100;
productA[1] + productB[1] + productC[1] + productD[1]<=500
productA[2] + productB[2] + productC[2]+productC[2] <= 300;
productA[3] + productB[3] + productC[3] + productD[3] <= 400; };

39. Coding TTP OPL Studio - Cplex Solver: Solution found
Optimal Solution with Objective Value:
productA[1] =
productA[2] =
productA[3] =
productB[1] =
productB[2] =
productB[3] =
productC[1] =
productC[2] =
productC[3] =
productD[1] =
productD[2] =
productD[3] =

40. Coding TTP It didn’t run!
ECLiPSe
:- lib(eplex_cplex).
main1(Cost, Vars) :-
Vars = [A1, A2, A3, B1, B2, B3, C1, C2, C3, D1, D2, D3],
Vars :: 0.0..inf,
A1 + A2 + A3 $= 200, B1 + B2 + B3 $= 400,
C1 + C2 + C3 $= 300, D1 + D2 + D3 $= 100,
A1 + B1 + C1 + D1 $=< 500, A2 + B2 + C2 + D2 $=< 300,
A3 + B3 + C3 + D3 $=< 400,
optimize(min(10*A1 + 7*A2 + 11*A3 + 8*B1 + 5*B2 + 10*B3 +
5*C1 + 5*C2 + 8*C3 +9*D1 + 3*D2 + 7*D3), Cost).
It didn’t run!
calling an undefined procedure cplex_prob_init(…)

41. Zebra puzzle
Zebra is a well known puzzle in which five men with different nationalities live in the first five house of a street. They practice five distinct professions, and each of them has a favorite animal and a favorite drink, all of them different. The five houses are painted in different colors. The puzzle is to find who owns Zebra.
Zebra puzzle has different statements. We’ll handle two.
This kind of Puzzle is usually treated as an instance of the class of tabular constraint satisfaction problems in which we express the problem using tables.
In this presentation we show how to solve it using CSP languages: OPL studio and ECLiPSe.

42. Modeling Zebra puzzle
In crucial step in Modeling Zebra puzzle is finding the decision variables. In our case, we consider:
variables: persons, colors, pets, drinks, tobaccos.
Domain: 1..5 (representing the five houses)
Constraints:
Expressed directly from the statement of the puzzle.

43. Coding Zebra puzzle OPL Studio - ILog Solver
enum people {Englishman, Spaniard, Ukrainian, Norwegian, Japanese};
enum drinks {coffee, tea, milk, orange, water};
enum pets {dog, fox, zebra, horse, snails};
enum tabacco {winston, kools, chesterfields, luckyStrike, parliaments };
enum colors {ivory, yellow, red , green, blue};
range houses 1..5;//{first, second, third, fourth, fifth};
var houses hseClr[colors];
var houses hsePple[people];
var houses hsePts[pets];
var houses hseDrks[drinks];
var houses hseTaba[tabacco];

44. Coding Zebra puzzle solve{
hsePple[Englishman] = hseClr[red]; hsePple[Spaniard]=hsePts[dog];
hseDrks[coffee] = hseClr[green]; hsePple[Ukrainian] = hseDrks[tea];
hseClr[green] = hseClr[ivory]+1; hseTaba[winston] = hsePts[snails];
hseTaba[kools]=hseClr[yellow]; hseDrks[milk]=3;
hsePple[Norwegian] = 1;
hseTaba[chesterfields]=hsePts[fox]+1 \/ hseTaba[chesterfields]=hsePts[fox]-1;
hseTaba[kools] = hsePts[horse]+1 \/ hseTaba[kools] = hsePts[horse]-1 ;
hseTaba[ luckyStrike] = hseDrks[orange];
hsePple[ Japanese] = hseTaba[parliaments];
hsePple[Norwegian] = hseClr[blue]+1 \/ hsePple[Norwegian] = hseClr[blue]-1;
//slient constraints-Global constraint alldifferent
alldifferent(hsePple);//forall(ordered i,j in people) hsePple[i] <> hsePple[j];
alldifferent(hsePts);//forall(ordered i,j in pets) hsePts[i] <> hsePts[j];
alldifferent(hseClr);//forall(ordered i,j in colors) hseClr[i] <> hseClr[j];
alldifferent(hseDrks);//forall(ordered i,j in drinks) hseDrks[i] <> hseDrks[j];
alldifferent(hseTaba);//forall(ordered i,j in tabacco) hseTaba[i] <> hseTaba[j]; };

45. Coding Zebra puzzle % The Englishman lives in a red house.
% The Spaniard owns a dog.
% The Japanese is a painter.
% The Italian drinks tea.
% The Norwegian lives in the first house on the left.
% The owner of the green house drinks coffee.
% The green house is on the right of the white one.
% The sculptor breeds snails.
% The diplomat lives in the yellow house.
% Milk is drunk in the middle house.
% The Norwegian's house is next to the blue one.
% The violinist drinks fruit juice.
% The fox is in a house next to that of the doctor.
% The horse is in a house next to that of the diplomat. %
% Who owns a Zebra, and who drinks water?

46. Coding Zebra puzzle :- lib(fd). zebra :-
% we use 5 lists of 5 variables each
Nat = [English, Spaniard, Japanese, Italian, Norwegian],
Color = [Red, Green, White, Yellow, Blue],
Profession = [Painter, Sculptor, Diplomat, Violinist, Doctor],
Pet = [Dog, Snails, Fox, Horse, Zebra],
Drink = [Tea, Coffee, Milk, Juice, Water],
% domains: all the variables range over house numbers 1 to 5
Nat :: 1..5,
Color :: 1..5,
Profession :: 1..5,
Pet :: 1..5,
Drink :: 1..5,

47. Coding Zebra puzzle % the values in each list are exclusive
alldifferent(Nat),
alldifferent(Color),
alldifferent(Profession),
alldifferent(Pet),
alldifferent(Drink),
% and here follow the actual constraints
English = Red, Spaniard = Dog,
Japanese = Painter, Italian = Tea,
Norwegian = 1, Green = Coffee,
Green #= White + 1, Sculptor = Snails,
Diplomat = Yellow, Milk = 3,
Dist1 #= Norwegian - Blue, Dist1 :: [-1, 1],
Violinist = Juice,
Dist2 #= Fox - Doctor, Dist2 :: [-1, 1],
Dist3 #= Horse - Diplomat, Dist3 :: [-1, 1],

48. Coding Zebra puzzle Answer from ECLiPSe after 0.02s
% put all the variables in a single list
flatten([Nat, Color, Profession, Pet, Drink], List),
% search: label all variables with values
labeling(List),
% print the answers: we need to do some decoding
NatNames = [English-english, Spaniard-spaniard, Japanese-japanese,
Italian-italian, Norwegian-norwegian],
memberchk(Zebra-ZebraNat, NatNames),
memberchk(Water-WaterNat, NatNames),
printf("The %w owns the zebra%n", [ZebraNat]),
printf("The %w drinks water%n", [WaterNat]).
Answer from ECLiPSe after 0.02s
The japanese owns the zebra
The norwegian drinks water

49. Uzbekian Puzzle
An uzbekian sales man met five traders who live in five different cities. The five traders are:
{Abdulhamid, Kurban,Ruza, Sharaf, Usman}
The five cities are :
{Bukhara, Fergana, Kokand, Samarkand, Tashkent}
Find the order in which he visited the cities given the following information:
He met Ruza before Sharaf after visiting Samarkand,
He reached Fergana after visiting Samarkand followed by other two cities,
The third trader he met was Tashkent,
Immediately after his visit to Bukhara, he met Abdulhamid
He reached Kokand after visiting the city of Kurban followed by other two cities;

50. Modeling Uzbekian Puzzle
The uzbekian puzzle can formulated within the CSP framework as follows:
Variables: order in which he visited each city and met each trader
Domain:1..5
constraints:
He met Ruza before Sharaf after visiting Samarkand,
He reached Fergana after visiting Samarkand followed by other two cities,
The third trader he met was Tashkent,
Immediately after his visit to Bukhara, he met Abdulhamid
He reached Kokand after visiting the city of Kurban followed by other two cities;

51. Coding Uzbekian Puzzle in OPL
enum cities {Bukhara, Fergana, Kokand, Samarkand, Tashkent};
enum traders {Abdulhamid, Kurban, Ruza, Sharaf, Usman};
range order 1..5;
var order mtTrader[traders];
var order vstCty[cities];
solve{
mtTrader[Ruza] < mtTrader[Sharaf];
mtTrader[Ruza] > vstCty[Samarkand];
vstCty[Fergana] = vstCty[Samarkand] + 2;
vstCty[Tashkent] = 3;
mtTrader[Abdulhamid] = vstCty[Bukhara] + 1;
vstCty[Kokand] = mtTrader[Kurban] + 2;
alldifferent(mtTrader);
alldifferent(vstCty); };
Solution [1]
mtTrader[Abdulhamid] = 2
mtTrader[Kurban] = 3
mtTrader[Ruza] = 4
mtTrader[Sharaf] = 5
mtTrader[Usman] = 1
vstCty[Bukhara] = 1
vstCty[Fergana] = 4
vstCty[Kokand] = 5
vstCty[Samarkand] = 2
vstCty[Tashkent] = 3

52. Knapsack problem
We have a knapsack with a fixed capacity and a number of items. Each item has a weight and a value. The problem consists of filling the knapsack without exceeding its capacity, while maximizing the overall value of its contents.
Knapsack problem is an example of Mixed integer programming.

53. Modeling Knapsack problem
A CSP model for knapsack problem is given by:
Variables: For each item, we associate a variable that gives the quantity of such an item we can put in the knapsack.
Domain: 0..Capacity of the knapsack
Constraints:
sum of the weights in the knapsack is less than the capacity
Objective function to maximize:
sum of the values in the knapsack

54. Coding Knapsack Pb in OPL
range items 1..10;
int MaxCapacity= 1000;
int value[items] = [29,30,25,27, 28,21,23,19,18,17];
int weight[items] =[30,32,33,31,34,40,45,44,39,35];
var int take[items] in 0..MaxCapacity;
maximize
sum(i in items) value[i] * take[i]
subject to
sum(i in items) weight[i] * take[i] <= MaxCapacity;
integer programming (CPLEX MIP)
displaying solution ...
take[1] = 28 take[2] = 5 take[i] = 0, i=3..10

55. Graceful labeling problem
Given a tree T with m+1 nodes, and m edges, find the possible labels in {0,1,…,m} for the nodes such that the absolute value of the difference of the labels of the nodes related to each edge are all different.
Formally, let f be a function from V ----> {0,…,m}, where V is the set of the vertices of T.
f is said to be a graceful labeling of T iff
|f(vi)-f(vj)| are all different for any edge vivj in T.

56. Modeling Graceful labeling Pb
Graceful labeling problem can be formulated into the following CSP:
variables: labels to put on each node of T
Domain: 0..m
constraints:
the absolute value of the difference between the labels of any edge are all different

57. Coding Graceful labeling Pb
OPL program for graceful labeling problem
int nbreOfNodes = ...;
//set the range of the labels of the nodes
range nodes 0..nbreOfNodes-1; range indices 1..nbreOfNodes;
int adjacencyMatrix[indices, indices] = ...;
var nodes label[indices];
solve {
alldifferent(label);
forall(ordered i, j in indices){
if(adjacencyMatrix[i,j] <> 0) then
forall(ordered k, h in indices){
if(adjacencyMatrix[k,h] <> 0 & not(k=i & h=j)) then
abs(label[i]-label[j])<>abs(label[h]-label[k])
endif; }
endif; };};

58. Coding Graceful labeling Pb1
Data file
nbreOfNodes = 7;
adjacencyMatrix = [[ 0, 1, 0, 0, 0, 0, 0],
[ 1, 0, 1, 0, 0, 0, 0 ],
[ 0, 1, 0, 1, 0, 1, 0 ],
[ 0, 0, 1, 0, 1, 0, 0 ],
[ 0, 0, 0, 1, 0, 0, 0 ],
[ 0, 0, 1, 0, 0, 0, 1 ],
[ 0, 0, 0, 0, 0, 1, 0 ]];
Solution Example: 2 3 4 6 5 7
label[1] = 0
label[2] = 6
label[3] = 1
label[4] = 3
label[5] = 4
label[6] = 5
label[7] = 2