# 2.2. More Complex Models¶

In the last section we introduced the basic structure of a MiniZinc model. In this section we introduce the array and set data structures, enumerated types and more complex constraints.

## 2.2.1. Arrays and Sets¶

Almost always we are interested in building models where the number of constraints and variables is dependent on the input data. In order to do so we will usually use arrays.

Consider a simple finite element model for modelling temperatures on a
rectangular sheet of metal. We approximate the temperatures across the
sheet by breaking the sheet into a finite number of elements in a
two-dimensional matrix.
A model is shown in Listing 2.2.1.
It declares the width `w` and height `h`
of the finite element model.
The declaration

```
set of int: HEIGHT = 0..h;
set of int: CHEIGHT = 1..h-1;
set of int: WIDTH = 0..w;
set of int: CWIDTH = 1..w-1;
array[HEIGHT,WIDTH] of var float: t; % temperature at point (i,j)
```

declares four fixed sets of integers describing the dimensions of the finite
element model: `HEIGHT` is the whole height of the model, while `CHEIGHT` is
the centre of the height omitting the top and bottom,
`WIDTH` is the whole width of the model, while
`CWIDTH` is the centre of the width omitting the left and rightsides,
Finally a two dimensional array of float variables `t`
with rows numbered
\(0\) to \(h\) (`HEIGHT`) and columns \(0\) to \(w\) (`WIDTH`),
to represent the temperatures at each
point in the metal plate.
We can access the element of the array in the \(i^{th}\) row and \(j^{th}\) column
using an expression `t[i,j]`

.

Laplace’s equation states that when the plate reaches a steady state the temperature at each internal point is the average of its orthogonal neighbours. The constraint

```
% Laplace equation: each internal temp. is average of its neighbours
constraint forall(i in CHEIGHT, j in CWIDTH)(
4.0*t[i,j] = t[i-1,j] + t[i,j-1] + t[i+1,j] + t[i,j+1]);
```

ensures that each internal point \((i,j)\) is the average of its four orthogonal neighbours. The constraints

```
% edge constraints
constraint forall(i in CHEIGHT)(t[i,0] = left);
constraint forall(i in CHEIGHT)(t[i,w] = right);
constraint forall(j in CWIDTH)(t[0,j] = top);
constraint forall(j in CWIDTH)(t[h,j] = bottom);
```

restrict the temperatures on each edge to be equal, and
gives these temperatures names: `left`, `right`, `top`
and `bottom`.
While the constraints

```
% corner constraints
constraint t[0,0]=0.0;
constraint t[0,w]=0.0;
constraint t[h,0]=0.0;
constraint t[h,w]=0.0;
```

ensure that the corners (which are irrelevant) are set to 0.0. We can determine the temperatures in a plate broken into 5 \(\times\) 5 elements with left, right and bottom temperature 0 and top temperature 100 with the model shown in Listing 2.2.1.

```
int: w = 4;
int: h = 4;
% arraydec
set of int: HEIGHT = 0..h;
set of int: CHEIGHT = 1..h-1;
set of int: WIDTH = 0..w;
set of int: CWIDTH = 1..w-1;
array[HEIGHT,WIDTH] of var float: t; % temperature at point (i,j)
var float: left; % left edge temperature
var float: right; % right edge temperature
var float: top; % top edge temperature
var float: bottom; % bottom edge temperature
% equation
% Laplace equation: each internal temp. is average of its neighbours
constraint forall(i in CHEIGHT, j in CWIDTH)(
4.0*t[i,j] = t[i-1,j] + t[i,j-1] + t[i+1,j] + t[i,j+1]);
% sides
% edge constraints
constraint forall(i in CHEIGHT)(t[i,0] = left);
constraint forall(i in CHEIGHT)(t[i,w] = right);
constraint forall(j in CWIDTH)(t[0,j] = top);
constraint forall(j in CWIDTH)(t[h,j] = bottom);
% corners
% corner constraints
constraint t[0,0]=0.0;
constraint t[0,w]=0.0;
constraint t[h,0]=0.0;
constraint t[h,w]=0.0;
left = 0.0;
right = 0.0;
top = 100.0;
bottom = 0.0;
solve satisfy;
output [ show_float(6, 2, t[i,j]) ++
if j == h then "\n" else " " endif |
i in HEIGHT, j in WIDTH
];
```

Running the command

```
$ minizinc --solver osicbc laplace.mzn
```

gives the output

```
-0.00 100.00 100.00 100.00 -0.00
-0.00 42.86 52.68 42.86 -0.00
-0.00 18.75 25.00 18.75 -0.00
-0.00 7.14 9.82 7.14 -0.00
-0.00 -0.00 -0.00 -0.00 -0.00
----------
```

Sets

Set variables are declared with a declaration of the form

```
set of <type-inst> : <var-name> ;
```

where sets of integers, enums (see later), floats or Booleans are allowed. The only type allowed for decision variable sets are variable sets of integers or enums. Set literals are of the form

```
{ <expr-1>, ..., <expr-n> }
```

or are range expressions over either integers, enums or floats of the form

```
<expr-1> .. <expr-2>
```

The standard set operations
are provided: element membership
(`in`

),
(non-strict) subset relationship (`subset`

),
(non-strict) superset relationship (`superset`

), union
(`union`

),
intersection (`intersect`

),
set difference (`diff`

),
symmetric set difference (`symdiff`

)
and the number of elements in the
set (`card`

).

As we have seen set variables and set literals (including ranges) can be used as an implicit type in variable declarations in which case the variable has the type of the elements in the set and the variable is implicitly constrained to be a member of the set.

Our cake baking problem is an example of a very simple kind of production planning problem. In this kind of problem we wish to determine how much of each kind of product to make to maximise the profit where manufacturing a product consumes varying amounts of some fixed resources. We can generalise the MiniZinc model in Listing 2.1.3 to handle this kind of problem with a model that is generic in the kinds of resources and products. The model is shown in Listing 2.2.2 and a sample data file (for the cake baking example) is shown in Listing 2.2.3.

```
% Products to be produced
enum Products;
% profit per unit for each product
array[Products] of int: profit;
% Resources to be used
enum Resources;
% amount of each resource available
array[Resources] of int: capacity;
% units of each resource required to produce 1 unit of product
array[Products, Resources] of int: consumption;
constraint assert(forall (r in Resources, p in Products)
(consumption[p,r] >= 0), "Error: negative consumption");
% bound on number of Products
int: mproducts = max (p in Products)
(min (r in Resources where consumption[p,r] > 0)
(capacity[r] div consumption[p,r]));
% Variables: how much should we make of each product
array[Products] of var 0..mproducts: produce;
array[Resources] of var 0..max(capacity): used;
% Production cannot use more than the available Resources:
constraint forall (r in Resources) (
used[r] = sum (p in Products)(consumption[p, r] * produce[p])
);
constraint forall (r in Resources) (
used[r] <= capacity[r]
);
% Maximize profit
solve maximize sum (p in Products) (profit[p]*produce[p]);
output [ "\(p) = \(produce[p]);\n" | p in Products ] ++
[ "\(r) = \(used[r]);\n" | r in Resources ];
```

```
% Data file for simple production planning model
Products = { BananaCake, ChocolateCake };
profit = [400, 450]; % in cents
Resources = { Flour, Banana, Sugar, Butter, Cocoa };
capacity = [4000, 6, 2000, 500, 500];
consumption= [| 250, 2, 75, 100, 0,
| 200, 0, 150, 150, 75 |];
```

The new feature in this model is the use of enumerated types. These allow us to treat the choice of resources and products as parameters to the model. The first item in the model

```
enum Products;
```

declares `Products` as an *unknown* set of products.

Enumerated Types

Enumerated types, which we shall refer to as `enums`,
are declared with a declaration of the form

```
enum <var-name> ;
```

An enumerated type is defined by an assignment of the form

```
enum <var-name> = { <var-name-1>, ..., <var-name-n> } ;
```

where `<var-name-1>`

, …, `<var-name-n>`

are the elements of
the enumerated type, with name `<var-name>`

.
Each of the elements of the enumerated type is also effectively declared by
this definition as a new constant of that type.
The declaration and definition can be combined into one line as usual.

The second item declares an array of integers:

```
array[Products] of int: profit;
```

The index set
of the array `profit` is `Products`.
This means that only elements of the set `Products`
can be used to index the array.

The elements of an enumerated type of \(n\) elements act very similar to the integers \(1\dots n\). They can be compared, they are ordered, by the order they appear in the enumerated type definition, they can be interated over, they can appear as indices of arrays, in fact they can appear anywhere an integer can appear.

In the example data file we have initialized the array using a list of integers

```
Products = { BananaCake, ChocolateCake };
profit = [400,450];
```

meaning the profit for a banana cake is 400, while for a chocolate cake it
is 450. Internally `BananaCake` will be treated like the integer 1,
while `ChocolateCake` will be treated like the integer 2.
While MiniZinc does not provide an explicit list type, one-dimensional
arrays with an index set `1..n`

behave like lists, and we will sometimes
refer to them as lists.

In a similar fashion, in the next two items we declare a set of resources
`Resources`, and an array `capacity` which gives the amount of
each resource that is available.

More interestingly, the item

```
array[Products, Resources] of int: consumption;
```

declares a 2-D array `consumption`. The value of
`consumption[p,r]`

is the amount of resource `r`

required to
produce one unit of product `p`

. Note that the first index is the row
and the second is the column.

The data file contains an example initialization of a 2-D array:

```
consumption= [| 250, 2, 75, 100, 0,
| 200, 0, 150, 150, 75 |];
```

Notice how the delimiter `|` is used to separate rows.

Arrays

Thus, MiniZinc provides one- and multi-dimensional arrays which are declared using the type:

```
array [ <index-set-1>, ..., <index-set-n> ] of <type-inst>
```

MiniZinc requires that the array declaration contains the index set of each dimension and that the index set is either an integer range, a set variable initialised to an integer range, or an enumeration type. Arrays can contain any of the base types: integers, enums, Booleans, floats or strings. These can be fixed or unfixed except for strings which can only be parameters. Arrays can also contain sets but they cannot contain arrays.

One-dimensional array literals are of form

```
[ <expr-1>, ..., <expr-n> ]
```

while two-dimensional array literals are of form

```
[| <expr-1-1>, ..., <expr-1-n> |
... |
<expr-m-1>, ..., <expr-m-n> |]
```

where the array has `m` rows and `n` columns.

The family of built-in functions `array1d`

,
`array2d`

, etc,
can be used to initialise an array of any dimension from a list (or more
exactly a one-dimensional array). The call:

```
array<n>d(<index-set-1>, ..., <index-set-n>, <list>)
```

returns an `n` dimensional array with index sets given by the first `n`
arguments and the last argument contains the elements of the array. For
instance, `array2d(1..3, 1..2, [1, 2, 3, 4, 5, 6])`

is equivalent to
`[|1, 2 |3, 4 |5, 6|]`

.

Array elements are accessed in the usual way: `a[i,j]`

gives the
element in the \(i^{th}\) row and \(j^{th}\) column.

The concatenation operator `++`
can be used to concatenate two
one-dimensional arrays together. The result is a list, i.e. a
one-dimensional array whose elements are indexed from 1. For instance
`[4000, 6] ++ [2000, 500, 500]`

evaluates to `[4000, 6, 2000, 500, 500]`

.
The built-in function
`length`

returns the length
of a one-dimensional array.

The next item in the model defines the parameter `mproducts`

. This is
set to an upper-bound on the number of products of any type that can be
produced. This is quite a complex example of nested
array comprehensions and aggregation operators. We shall introduce these
before we try to understand this item and the rest of the model.

First, MiniZinc provides list comprehensions similar to those provided in
many functional programming languages, or Python. For example, the list comprehension
`[i + j | i, j in 1..3 where j < i]`

evaluates to `[1 + 2, 1 + 3, 2 + 3]`

which is `[3, 4, 5]`

. Of course `[3, 4, 5]`

is
simply an array with index set `1..3`

.

MiniZinc also provides set comprehensions which have a similar syntax: for
instance, `{i + j | i, j in 1..3 where j < i}`

evaluates to the set
`{3, 4, 5}`

.

List and Set Comprehensions

The generic form of a list comprehension is

```
[ <expr> | <generator-exp> ]
```

The expression `<expr>`

specifies how to construct elements in the
output list from the elements generated by `<generator-exp>`

.
The generator `<generator-exp>`

consists of a comma separated sequence of
generator expressions optionally followed by a Boolean expression. The two forms are

```
<generator>
<generator> where <bool-exp>
```

The optional `<bool-exp>`

in the second form acts as a filter on
the generator expression: only elements satisfying the Boolean expression
are used to construct elements in the output list. A generator
`<generator>`

has the form

```
<identifier>, ..., <identifier> in <array-exp>
```

Each identifier is an *iterator*
which takes the values
of the array expression in turn, with the last identifier varying most rapidly.

The generators of a list comprehension and `<bool-exp>`

usually do not involve decision variables.
If they do involve decision variables
then the list produced is a list of `var opt <T>`

where `<T>`

is the type
of the `<expr>`

. See the discussion of option types
in Option Types for more details.

Set comprehensions
are almost identical to list comprehensions: the only
difference is the use of `{` and `}` to enclose the
expression rather than `[` and `]`.
The elements generated by a set comprehension must be
fixed, i.e. free of decision variables.
Similarly the generators and optional `<bool-exp>`

for set comprehensions must be fixed.

Second, MiniZinc provides a number of built-in functions that take a
one-dimensional array and aggregate the elements. Probably the most useful
of these is :mzn:`forall`.
This takes an array of Boolean expressions
(that is, constraints) and returns a single Boolean expression which is the
logical conjunction of the Boolean expressions in the array.

For example, consider the expression

```
forall( [a[i] != a[j] | i,j in 1..3 where i < j])
```

where `a` is an arithmetic array with index set `1..3`. This
constrains the elements in `a` to be pairwise different. The list
comprehension evaluates to `[ a[1] != a[2], a[1] != a[3], a[2] != a[3] ]`

and so the `forall`

function returns the logical
conjunction `a[1] != a[2] /\ a[1] != a[3] /\ a[2] != a[3]`

.

Aggregation functions

The *aggregation functions* for arithmetic arrays are:
`sum`

which adds the elements, `product`

which multiplies them together,
and `min`

and `max`

which respectively return the least and
greatest element in the array. When applied to an empty array, `min`

and
`max`

give a run-time error, `sum`

returns 0 and `product`

returns 1.

MiniZinc provides four aggregation functions for arrays containing Boolean
expressions. As we have seen, the first of these,
`forall`

, returns
a single constraint which is the logical conjunction of the
constraints.
The second function, `exists`

,
returns the logical
disjunction of the constraints. Thus, `forall`

enforces that all
constraints in the array hold, while `exists`

ensures that at least
one of the constraints holds.
The third function, `xorall`

,
ensures that an odd number of constraints hold.
The fourth function, `iffall`

,
ensures that an even number of constraints holds.

The third, and final, piece in the puzzle is that MiniZinc allows a special syntax for aggregation functions when used with an array comprehension. Instead of writing

```
forall( [a[i] != a[j] | i,j in 1..3 where i < j])
```

the modeller can instead write the more mathematical looking

```
forall (i,j in 1..3 where i < j) (a[i] != a[j])
```

The two expressions are completely equivalent: the modeller is free to use whichever they feel looks most natural.

Generator call expressions

A *generator call expression* has form

```
<agg-func> ( <generator-exp> ) ( <exp> )
```

The round brackets around the generator expression
`<generator-exp>`

and the constructor expression
`<exp>`

are not optional: they must be there. This is
equivalent to writing

```
<agg-func> ( [ <exp> | <generator-exp> ] )
```

The aggregation function `<agg-func>`

is any MiniZinc
function expecting a single array as argument.

We are now in a position to understand the rest of the simple production
planning model shown in Listing 2.2.2. For the moment ignore
the item defining `mproducts`

. The item afterwards:

```
array[Products] of var 0..mproducts: produce;
```

defines a one-dimensional array `produce`

of decision variables. The
value of `produce[p]`

will be set to the amount of product `p`

in the optimal solution.
The next item

```
array[Resources] of var 0..max(capacity): used;
```

defines a set of auxiliary variables that record how much of each resource is used. The next two constraints

```
constraint forall (r in Resources)
(used[r] = sum (p in Products) (consumption[p, r] * produce[p]));
constraint forall (r in Resources)(used[r] <= capacity[r] );
```

compute in `used[r]`

the total consumption of the
resource `r`

and ensure it is less than the available amount.
Finally, the item

```
solve maximize sum (p in Products) (profit[p]*produce[p]);
```

indicates that this is a maximisation problem and that the objective to be maximised is the total profit.

We now return to the definition of `mproducts`

. For each product
`p`

the expression

```
(min (r in Resources where consumption[p,r] > 0)
(capacity[r] div consumption[p,r]))
```

determines the maximum amount of `p`

that can be produced taking into
account the amount of each resource `r`

and how much of `r`

is
required to produce the product. Notice the use of the filter
`where consumption[p,r] > 0`

to ensure that only resources required to make the
product are considered so as to avoid a division by zero error. Thus, the
complete expression

```
int: mproducts = max (p in Products)
(min (r in Resources where consumption[p,r] > 0)
(capacity[r] div consumption[p,r]));
```

computes the maximum amount of *any* product that can be produced, and
so this can be used as an upper bound on the domain of the decision
variables in `produce`

.

Finally notice the output item is more complex, and uses list comprehensions to create an understandable output. Running

```
$ minizinc --solver gecode prod-planning.mzn prod-planning-data.dzn
```

results in the output

```
BananaCake = 2;
ChocolateCake = 2;
Flour = 900;
Banana = 4;
Sugar = 450;
Butter = 500;
Cocoa = 150;
----------
==========
```

## 2.2.2. Global Constraints¶

MiniZinc includes a library of global constraints which can also be used
to define models. An example is the `alldifferent`

constraint which requires
all the variables appearing in its argument to be pairwise different.

```
include "alldifferent.mzn";
var 1..9: S;
var 0..9: E;
var 0..9: N;
var 0..9: D;
var 1..9: M;
var 0..9: O;
var 0..9: R;
var 0..9: Y;
constraint 1000 * S + 100 * E + 10 * N + D
+ 1000 * M + 100 * O + 10 * R + E
= 10000 * M + 1000 * O + 100 * N + 10 * E + Y;
constraint alldifferent([S,E,N,D,M,O,R,Y]);
solve satisfy;
output [" \(S)\(E)\(N)\(D)\n",
"+ \(M)\(O)\(R)\(E)\n",
"= \(M)\(O)\(N)\(E)\(Y)\n"];
```

The SEND+MORE=MONEY problem requires assigning a different
digit to each letter so that the arithmetic constraint holds.
The model shown in Listing 2.2.4 uses the constraint expression
`alldifferent([S,E,N,D,M,O,R,Y])`

to ensure that each letter takes a different digit value.
The global constraint is made available
in the model using include item

```
include "alldifferent.mzn";
```

which makes
the global constraint `alldifferent`

usable by the model.
One could replace this line by

```
include "globals.mzn";
```

which includes all globals.

A list of all the global constraints defined for MiniZinc is included in the release documentation. See Global Constraints for a description of some important global constraints.

## 2.2.3. Conditional Expressions¶

MiniZinc provides a conditional *if-then-else-endif*
expression.
An example of its use is

```
int: r = if y != 0 then x div y else 0 endif;
```

which sets `r`

to `x`

divided by `y`

unless `y`

is zero in which case
it sets it to zero.

Conditional expressions

The form of a conditional expression is

```
if <bool-exp> then <exp-1> else <exp-2> endif
```

It is a true expression rather than a control flow statement and so can be used in other expressions.
It evaluates to `<exp-1>`

if the Boolean expression `<bool-exp>`

is true and
`<exp-2>`

otherwise. The type of the conditional expression is that of
`<exp-1>`

and `<exp-2>`

which must have the same
type.

If the `<bool-exp>`

contains decision variables, then the
type-inst
of the expression is `var <T>`

where `<T>`

is the type of
`<exp-1>`

and `<exp-2>`

even if both
expressions are fixed.

```
include "alldifferent.mzn";
int: S;
int: N = S * S;
int: digs = ceil(log(10.0,int2float(N))); % digits for output
set of int: PuzzleRange = 1..N;
set of int: SubSquareRange = 1..S;
array[1..N,1..N] of 0..N: start; %% initial board 0 = empty
array[1..N,1..N] of var PuzzleRange: puzzle;
% fill initial board
constraint forall(i,j in PuzzleRange)(
if start[i,j] > 0 then puzzle[i,j] = start[i,j] else true endif );
% All different in rows
constraint forall (i in PuzzleRange) (
alldifferent( [ puzzle[i,j] | j in PuzzleRange ]) );
% All different in columns.
constraint forall (j in PuzzleRange) (
alldifferent( [ puzzle[i,j] | i in PuzzleRange ]) );
% All different in sub-squares:
constraint
forall (a, o in SubSquareRange)(
alldifferent( [ puzzle[(a-1) *S + a1, (o-1)*S + o1] |
a1, o1 in SubSquareRange ] ) );
solve satisfy;
output [ show_int(digs,puzzle[i,j]) ++ " " ++
if j mod S == 0 then " " else "" endif ++
if j == N then
if i != N then
if i mod S == 0 then "\n\n" else "\n" endif
else "" endif else "" endif
| i,j in PuzzleRange ] ++ ["\n"];
```

```
S=3;
start=[|
0, 0, 0, 0, 0, 0, 0, 0, 0|
0, 6, 8, 4, 0, 1, 0, 7, 0|
0, 0, 0, 0, 8, 5, 0, 3, 0|
0, 2, 6, 8, 0, 9, 0, 4, 0|
0, 0, 7, 0, 0, 0, 9, 0, 0|
0, 5, 0, 1, 0, 6, 3, 2, 0|
0, 4, 0, 6, 1, 0, 0, 0, 0|
0, 3, 0, 2, 0, 7, 6, 9, 0|
0, 0, 0, 0, 0, 0, 0, 0, 0|];
```

Conditional expressions are very useful in building complex models, or
complex output. Consider the model of Sudoku problems shown in
Listing 2.2.5. The initial board positions are given by the
`start`

parameter where 0 represents an empty
board position. This is converted to constraints on the decision variables
`puzzle`

using the conditional expression

```
constraint forall(i,j in PuzzleRange)(
if start[i,j] > 0 then puzzle[i,j] = start[i,j] else true endif );
```

Conditional expressions are also very useful for defining complex output. In the Sudoku model of Listing 2.2.5 the expression

```
if j mod S == 0 then " " else "" endif
```

inserts an extra space between groups of size `S`

.
The output expression also uses conditional expressions to
add blank lines
after each `S`

lines. The resulting output is highly readable.

The remaining constraints ensure that the numbers appearing in each row and column and \(S \times S\) subsquare are all different.

One can use MiniZinc to search
for all solutions to a satisfaction problem (`solve satisfy`

)
by using the flag `-a`
or `--all-solutions`. Running

```
$ minizinc --solver gecode --all-solutions sudoku.mzn sudoku.dzn
```

results in

```
5 9 3 7 6 2 8 1 4
2 6 8 4 3 1 5 7 9
7 1 4 9 8 5 2 3 6
3 2 6 8 5 9 1 4 7
1 8 7 3 2 4 9 6 5
4 5 9 1 7 6 3 2 8
9 4 2 6 1 8 7 5 3
8 3 5 2 4 7 6 9 1
6 7 1 5 9 3 4 8 2
----------
==========
```

The line `==========`
is output when the system has output all possible
solutions, here verifying that there is exactly one.

## 2.2.4. Enumerated Types¶

Enumerated types allows us to build models that depend on a set of objects which are part of the data, or are named in the model, and hence make models easier to understand and debug. We have introduce enumerated types or enums briefly, in this subsection we will explore how we can use them more fully, and show some of the built in functions for dealing with enumerated types.

Let’s revisit the problem of coloring the graph of Australia from Basic Modelling in MiniZinc.

```
enum Color;
var Color: wa;
var Color: nt;
var Color: sa;
var Color: q;
var Color: nsw;
var Color: v;
var Color: t;
constraint wa != nt /\ wa != sa /\ nt != sa /\ nt != q /\ sa != q;
constraint sa != nsw /\ sa != v /\ q != nsw /\ nsw != v;
solve satisfy;
```

The model shown in Listing 2.2.7 declares an enumerated type
`Color`

which must be defined in the data file. Each of the state
variables is declared to take a value from this enumerated type.
Running this program using

```
$ minizinc --solver gecode -D"Color = { red, yellow, blue };" aust-enum.mzn
```

might result in output

```
wa = blue;
nt = yellow;
sa = red;
q = blue;
nsw = yellow;
v = blue;
t = red;
```

Enumerated Type Variable Declarations

An enumerated type parameter is declared as either:

```
<enum-name> : <var-name>
<l>..<u> : <var-name>
```

where `<enum-name>`

is the name of a enumerated type, and
`<l>`

and `<u>`

are fixed enumerated type expressions of the same
enumerated type.

An enumerated type decision variable is declared as either:

```
var <enum-name> : <var-name>
var <l>..<u> : <var-name>
```

where `<enum-name>`

is the name of a enumerated type, and
`<l>`

and `<u>`

are fixed enumerated type expressions of the same
enumerated type.

A key behaviour of enumerated types is that they are automatically coerced to integers when they are used in a position expecting an integer. For example, this allows us to use global constraints defined on integers, such as

```
global_cardinality_low_up([wa,nt,sa,q,nsw,v,t],
[red,yellow,blue],[2,2,2],[2,2,3]);
```

This requires at least two states to be colored each color and three to be colored blue.

Enumerated Type Operations

There are a number of built in operations on enumerated types:

`enum_next(X,x)`

: returns the next value in after`x`

in the enumerated type`X`

. This is a partial function, if`x`

is the last value in the enumerated type`X`

then the function returns \(\bot\) causing the Boolean expression containing the expression to evaluate to`false`

.`enum_prev(X,x)`

: returns the previous value before`x`

in the enumerated type`X`

. Similarly`enum_prev`

is a partial function.`to_enum(X,i)`

: maps an integer expression`i`

to an enumerated type value in type`X`

or evaluates to \(\bot\) if`i`

is less than or equal to 0 or greater than the number of elements in`X`

.

Note also that a number of standard functions are applicable to enumerated types:

`card(X)`

: returns the cardinality of an enumerated type`X`

.`min(X)`

: returns the minimum element of of an enumerated type`X`

.`max(X)`

: returns the maximum element of of an enumerated type`X`

.

## 2.2.5. Complex Constraints¶

Constraints are the core of the MiniZinc model.
We have seen simple relational expressions but constraints can be considerably more powerful than this.
A constraint is allowed to be any Boolean expression.
Imagine a scheduling problem in which we have
two tasks that cannot overlap in time.
If `s1`

and `s2`

are the corresponding start times
and `d1`

and `d2`

are the corresponding
durations
we can express this as:

```
constraint s1 + d1 <= s2 \/ s2 + d2 <= s1;
```

which ensures that the tasks do not overlap.

Booleans

Boolean expressions in MiniZinc can be written using a standard mathematical syntax.
The Boolean literals are `true`

and
`false`

and the Boolean
operators
are
conjunction, i.e. and (`/\`),
disjunction, i.e. or (`\/`),
only-if (`<-`

),
implies (`->`

),
if-and-only-if (`<->`

) and
negation (`not`

).
Booleans can be automatically coerced to integers, but to make this
coercion explicit the
built-in function `bool2int`

can be used: it
returns 1 if its argument is true and 0 otherwise.

```
enum JOB;
enum TASK;
TASK: last = max(TASK);
array [JOB,TASK] of int: d; % task durations
int: total = sum(i in JOB, j in TASK)(d[i,j]);% total duration
int: digs = ceil(log(10.0,int2float(total))); % digits for output
array [JOB,TASK] of var 0..total: s; % start times
var 0..total: end; % total end time
constraint %% ensure the tasks occur in sequence
forall(i in JOB) (
forall(j in TASK where j < last)
(s[i,j] + d[i,j] <= s[i,enum_next(TASK,j)]) /\
s[i,last] + d[i,last] <= end
);
constraint %% ensure no overlap of tasks
forall(j in TASK) (
forall(i,k in JOB where i < k) (
s[i,j] + d[i,j] <= s[k,j] \/
s[k,j] + d[k,j] <= s[i,j]
)
);
solve minimize end;
output ["end = \(end)\n"] ++
[ show_int(digs,s[i,j]) ++ " " ++
if j == last then "\n" else "" endif |
i in JOB, j in TASK ];
```

```
JOB = anon_enum(5);
TASK = anon_enum(5);
d = [| 1, 4, 5, 3, 6
| 3, 2, 7, 1, 2
| 4, 4, 4, 4, 4
| 1, 1, 1, 6, 8
| 7, 3, 2, 2, 1 |];
```

The job shop scheduling model given in Listing 2.2.8
gives a realistic example of the use of this disjunctive modelling
capability. In job shop scheduling we have a set of jobs, each consisting
of a sequence of tasks on separate machines: so task `[i,j]`

is the
task in the \(i^{th}\) job performed on the \(j^{th}\) machine.
Each sequence of tasks must be completed in order,
and no two tasks on the same machine can overlap in time.
Even small instances of this problem can be quite challenging to find
optimal solutions.

The command

```
$ minizinc --solver gecode --all-solutions jobshop.mzn jdata.dzn
```

solves a small job shop scheduling problem, and illustrates the behaviour of texttt{all-solutions} for optimisation problems. Here the solver outputs each better solutions as it finds it, rather than all possible optimal solutions. The output from this command is:

```
end = 39
5 9 13 22 30
6 13 18 25 36
0 4 8 12 16
4 8 12 16 22
9 16 25 27 38
----------
end = 37
4 8 12 17 20
5 13 18 26 34
0 4 8 12 16
8 12 17 20 26
9 16 25 27 36
----------
end = 34
0 1 5 10 13
6 10 15 23 31
2 6 11 19 27
1 5 10 13 19
9 16 22 24 33
----------
end = 30
5 9 13 18 21
6 13 18 25 27
1 5 9 13 17
0 1 2 3 9
9 16 25 27 29
----------
==========
```

indicating an optimal solution with end time 30 is finally found,
and proved optimal.
We can generate all *optimal solutions*
by adding a constraint that
`end = 30`

and changing the solve item to `solve satisfy`

and then executing

```
$ minizinc --solver gecode --all-solutions jobshop.mzn jobshop.dzn
```

For this problem there are 3,444,375 optimal solutions.

```
int: n;
enum Men = anon_enum(n);
enum Women = anon_enum(n);
array[Women, Men] of int: rankWomen;
array[Men, Women] of int: rankMen;
array[Men] of var Women: wife;
array[Women] of var Men: husband;
% assignment
constraint forall (m in Men) (husband[wife[m]]=m);
constraint forall (w in Women) (wife[husband[w]]=w);
% ranking
constraint forall (m in Men, o in Women) (
rankMen[m,o] < rankMen[m,wife[m]] ->
rankWomen[o,husband[o]] < rankWomen[o,m] );
constraint forall (w in Women, o in Men) (
rankWomen[w,o] < rankWomen[w,husband[w]] ->
rankMen[o,wife[o]] < rankMen[o,w] );
solve satisfy;
output ["wives= \(wife)\nhusbands= \(husband)\n"];
```

```
n = 5;
rankWomen =
[| 1, 2, 4, 3, 5,
| 3, 5, 1, 2, 4,
| 5, 4, 2, 1, 3,
| 1, 3, 5, 4, 2,
| 4, 2, 3, 5, 1 |];
rankMen =
[| 5, 1, 2, 4, 3,
| 4, 1, 3, 2, 5,
| 5, 3, 2, 4, 1,
| 1, 5, 4, 3, 2,
| 4, 3, 2, 1, 5 |];
```

Another powerful modelling feature in MiniZinc is
that decision variables
can be used for array access.
As an example, consider the
(old-fashioned) *stable marriage problem*. We have `n`

(straight) women and `n`

(straight)
men. Each man has a ranked list of women and vice versa. We want to find a
husband/wife for each women/man so that all marriages are *stable* in
the sense that:

- whenever
`m`

prefers another women`o`

to his wife`w`

,`o`

prefers her husband to`m`

, and - whenever
`w`

prefers another man`o`

to her husband`m`

,`o`

prefers his wife to`w`

.

This can be elegantly modelled in MiniZinc. The model and sample data is shown in Listing 2.2.10 and Listing 2.2.11.

The first three items in the model declare the number of men/women and the
set of men and women. Here we introduce the use of *anonymous enumerated types*.
Both `Men`

and `Women`

are sets of size
`n`

, but we do not wish to mix them up so we use an anonymous enumerated
type. This allows MiniZinc to detect modelling errors where we use
`Men`

for `Women`

or vice versa.

The matrices `rankWomen`

and `rankMen`

,
respectively, give the women’s ranking of the men and the men’s ranking of the women.
Thus, the entry `rankWomen[w,m]`

gives the ranking by woman `w`

of man `m`

. The lower the number in the ranking, the more the man or women is preferred.

There are two arrays of decision variables: `wife`

and
`husband`

. These, respectively, contain the wife of each man and the
husband of each women.

The first two constraints

```
constraint forall (m in Men) (husband[wife[m]]=m);
constraint forall (w in Women) (wife[husband[w]]=w);
```

ensure that the assignment of husbands and wives is consistent: `w`

is the
wife of `m`

implies `m`

is the husband of `w`

and vice versa. Notice how in
`husband[wife[m]]`

the index expression `wife[m]`

is a
decision variable, not a parameter.

The next two constraints are a direct encoding of the stability condition:

```
constraint forall (m in Men, o in Women) (
rankMen[m,o] < rankMen[m,wife[m]] ->
rankWomen[o,husband[o]] < rankWomen[o,m] );
constraint forall (w in Women, o in Men) (
rankWomen[w,o] < rankWomen[w,husband[w]] ->
rankMen[o,wife[o]] < rankMen[o,w] );
```

This natural modelling of the stable marriage problem is made possible by
the ability to use decision variables as array indices and to construct
constraints using the standard Boolean connectives.
The alert reader may
be wondering at this stage, what happens if the array index variable takes a
value that is outside the index set of the array. MiniZinc treats this as
failure: an array access `a[e]`

implicitly adds the constraint
`e in index_set(a)`

to the closest surrounding Boolean context where
`index_set(a)`

gives the index set of `a`

.

Anonymous Enumerated Types

An *anonymous enumerated type*
is of the form `anon_enum(<n>)`

where `<n>`

is a fixed integer
expression defining the size of the enumerated type.

An anonymous enumerated type is just like any other enumerated type except that we have no names for its elements. When printed out, they are given unique names based on the enumerated type name.

Thus for example, consider the variable declarations

```
array[1..2] of int: a= [2,3];
var 0..2: x;
var 2..3: y;
```

The constraint `a[x] = y`

will succeed with \(x=1 \wedge y=2\) and \(x=2 \wedge y=3\).
And the constraint `not a[x] = y`

will succeed with
\(x=0 \wedge y=2\), \(x=0 \wedge y=3\), \(x=1 \wedge y=3\) and \(x=2 \wedge y=2\).

In the case of invalid array accesses by a parameter, the formal semantics of MiniZinc treats this as failure so as to ensure that the treatment of parameters and decision variables is consistent, but a warning is issued since it is almost always an error.

```
int: n;
array[0..n-1] of var 0..n: s;
constraint forall(i in 0..n-1) (
s[i] = (sum(j in 0..n-1)(bool2int(s[j]=i))));
solve satisfy;
output [ "s = \(s);\n" ] ;
```

The coercion function
`bool2int`

can be called with any Boolean
expression. This allows the MiniZinc
modeller to use so called *higher order constraints*.
As a simple example consider the *magic series problem*:
find a list of numbers \(s= [s_0,\ldots,s_{n-1}]\)
such that \(s_i\) is the number
of occurrences of \(i\) in \(s\). An example is \(s = [1,2,1,0]\).

A MiniZinc
model for this problem
is shown in Listing 2.2.12. The use of
`bool2int`

allows us to sum up the number of times the constraint
`s[j]=i`

is satisfied.
Executing the command

```
$ minizinc --solver gecode --all-solutions magic-series.mzn -D "n=4;"
```

leads to the output

```
s = [1, 2, 1, 0];
----------
s = [2, 0, 2, 0];
----------
==========
```

indicating exactly two solutions to the problem.

Note that MiniZinc will automatically coerce Booleans to integers and integers to floats when required. We could replace the the constraint item in Listing 2.2.12 with

```
constraint forall(i in 0..n-1) (
s[i] = (sum(j in 0..n-1)(s[j]=i)));
```

and get identical results, since the Boolean expression
`s[j] = i`

will be automatically coerced to an
integer, effectively by the MiniZinc system automatically adding the
missing `bool2int`

.

Coercion

In MiniZinc one can *coerce* a Boolean value to
an integer value using the `bool2int`

function.
Similarly one can coerce an integer value to a float value using
`int2float`

.
The instantiation of the coerced value is the same as the argument,
e.g. `par bool`

is coerced to `par int`

, while
`var bool`

is coerced to `var int`

.

MiniZinc automatically coerces Boolean expressions to integer
expressions and integer expressions to float expressions,
by inserting `bool2int`

and `int2float`

in the model
appropriately.
Note that it will also coerce Booleans to floats using two steps.

## 2.2.6. Set Constraints¶

Another powerful modelling feature of MiniZinc is that it allows sets containing integers to be decision variables: this means that when the model is evaluated the solver will find which elements are in the set.

As a simple example, consider the *0/1 knapsack problem*. This is a
restricted form of the knapsack problem in which we can either choose to
place the item in the knapsack or not. Each item has a weight and a profit
and we want to find which choice of items leads to the maximum profit
subject to the knapsack not being too full.

It is natural to model this in MiniZinc with a single decision variable: `var set of ITEM: knapsack`

where `ITEM`

is the set of possible items. If the arrays
`weight[i]`

and `profit[i]`

respectively give the weight and
profit of item `i`

, and the maximum weight the knapsack can carry is
given by `capacity`

then a naural model is given in
Listing 2.2.13.

```
enum ITEM;
int: capacity;
array[ITEM] of int: profits;
array[ITEM] of int: weights;
var set of ITEM: knapsack;
constraint sum (i in knapsack) (weights[i]) <= capacity;
solve maximize sum (i in knapsack) (profits[i]) ;
output ["knapsack = \(knapsack)\n"];
```

Notice that the `var`

keyword comes before the `set`

declaration indicating that the
set itself is the decision variable.
This contrasts with an array in which the `var`

keyword
qualifies the elements in the array rather than the array itself since the
basic structure of the array is fixed, i.e. its index set.

```
include "partition_set.mzn";
int: weeks; set of int: WEEK = 1..weeks;
int: groups; set of int: GROUP = 1..groups;
int: size; set of int: SIZE = 1..size;
int: ngolfers = groups*size;
set of int: GOLFER = 1..ngolfers;
array[WEEK,GROUP] of var set of GOLFER: Sched;
% constraints
constraint
forall (i in WEEK, j in GROUP) (
card(Sched[i,j]) = size
/\ forall (k in j+1..groups) (
Sched[i,j] intersect Sched[i,k] = {}
)
) /\
forall (i in WEEK) (
partition_set([Sched[i,j] | j in GROUP], GOLFER)
) /\
forall (i in 1..weeks-1, j in i+1..weeks) (
forall (x,y in GROUP) (
card(Sched[i,x] intersect Sched[j,y]) <= 1
)
);
% symmetry
constraint
% Fix the first week %
forall (i in GROUP, j in SIZE) (
((i-1)*size + j) in Sched[1,i]
) /\
% Fix first group of second week %
forall (i in SIZE) (
((i-1)*size + 1) in Sched[2,1]
) /\
% Fix first 'size' players
forall (w in 2..weeks, p in SIZE) (
p in Sched[w,p]
);
solve satisfy;
output [ show(Sched[i,j]) ++ " " ++
if j == groups then "\n" else "" endif |
i in WEEK, j in GROUP ];
```

As a more complex example of set constraint consider the social golfers
problem shown in Listing 2.2.14.
The aim is to schedule a golf tournament over `weeks`

using `groups`

\(\times\) `size`

golfers. Each week we have to
schedule `groups`

different groups each of size `size`

.
No two pairs of golfers should ever play in two groups.

The variables in the model are sets of golfers `Sched[i,j]`

for the \(i^{th}\) week and `j^{th}`

group.

The constraints shown in lines 11-32
first enforces an ordering on the first
set in each week to remove symmetry in swapping weeks. Next they
enforce an ordering on the sets in each week, and make each set have a
cardinality of `size`

.
They then ensure that each week is a partition of the set of golfers
using the global constraint
`partition_set`

.
Finally the last constraint ensures that no two players play in two
groups together (since the cardinality of the intersection of any two groups
is at most 1).

There are also symmetry breaking
initialisation constraints shown in lines 34-46:
the first week
is fixed to have all players in order; the second week is made up of the
first players of each of the first groups in the first week; finally the
model forces the first `size`

players to appear in their corresponding
group number for the remaining weeks.

Executing the command

```
$ minizinc --solver gecode social-golfers.mzn social-golfers.dzn
```

where the data file defines a problem with 4 weeks, with 4 groups of size 3 leads to the output

```
1..3 4..6 7..9 10..12
{1,4,7} {2,9,12} {3,5,10} {6,8,11}
{1,6,9} {2,8,10} {3,4,11} {5,7,12}
{1,5,8} {2,7,11} {3,6,12} {4,9,10}
----------
```

Notice hows sets which are ranges may be output in range format.

## 2.2.7. Putting it all together¶

We finish this section with a complex example illustrating most of the features introduced in this chapter including enumerated types, complex constraints, global constraints, and complex output.

```
enum Guests = { bride, groom, bestman, bridesmaid, bob, carol,
ted, alice, ron, rona, ed, clara};
set of int: Seats = 1..12;
set of int: Hatreds = 1..5;
array[Hatreds] of Guests: h1 = [groom, carol, ed, bride, ted];
array[Hatreds] of Guests: h2 = [clara, bestman, ted, alice, ron];
set of Guests: Males = {groom, bestman, bob, ted, ron,ed};
set of Guests: Females = {bride,bridesmaid,carol,alice,rona,clara};
array[Guests] of var Seats: pos; % seat of guest
array[Hatreds] of var Seats: p1; % seat of guest 1 in hatred
array[Hatreds] of var Seats: p2; % seat of guest 2 in hatred
array[Hatreds] of var 0..1: sameside; % seats of hatred on same side
array[Hatreds] of var Seats: cost; % penalty of hatred
include "alldifferent.mzn";
constraint alldifferent(pos);
constraint forall(g in Males)( pos[g] mod 2 == 1 );
constraint forall(g in Females)( pos[g] mod 2 == 0 );
constraint not (pos[ed] in {1,6,7,12});
constraint abs(pos[bride] - pos[groom]) <= 1 /\
(pos[bride] <= 6 <-> pos[groom] <= 6);
constraint forall(h in Hatreds)(
p1[h] = pos[h1[h]] /\
p2[h] = pos[h2[h]] /\
sameside[h] = bool2int(p1[h] <= 6 <-> p2[h] <= 6) /\
cost[h] = sameside[h] * abs(p1[h] - p2[h]) +
(1 - sameside[h]) * (abs(13 - p1[h] - p2[h]) + 1) );
solve maximize sum(h in Hatreds)(cost[h]);
output [ show(g)++" " | s in Seats,g in Guests where fix(pos[g]) == s]
++ ["\n"];
```

The model of Listing 2.2.15 arranges seats at the wedding table. The table has 12 numbered seats in order around the table, 6 on each side. Males must sit in odd numbered seats, and females in even. Ed cannot sit at the end of the table because of a phobia, and the bride and groom must sit next to each other. The aim is to maximize the distance between known hatreds. The distance between seats is the difference in seat number if on the same side, otherwise its the distance to the opposite seat + 1.

Note that in the output statement we consider each seat `s`

and search for a
guest `g`

who is assigned to that seat. We make use of the built in function
`fix`

which checks if a decision variable is fixed and returns its
fixed value, and otherwise aborts.
This is always safe to use in output statements, since by the
time the output statement is run all decision variables should be fixed.

Running

```
$ minizinc --solver gecode wedding.mzn
```

Results in the output

```
ted bride groom rona ed carol ron alice bob bridesmaid bestman clara
----------
==========
```

The resulting table placement is illustrated in Fig. 2.2.2 where the lines indicate hatreds. The total distance is 22.

Fix

In output items the built-in function `fix`

checks that
the value of a decision variable is fixed
and coerces the instantiation from
decision variable to parameter.