# 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 right sides. 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.

Listing 2.2.1 Finite element plate model for determining steady state temperatures (laplace.mzn).
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 == w then "\n" else " " endif |
i in HEIGHT, j in WIDTH
];


Running the command

$minizinc --solver cbc 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. Listing 2.2.2 Model for simple production planning (prod-planning.mzn). % 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 ];  Listing 2.2.3 Example data file for the simple production planning problem (prod-planning-data.dzn). % 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 iterated 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. The expression [400,500] represents a literal one-dimensional array. In MiniZinc, the index set of literal arrays always starts at 1 (this is similar to other mathematically inspired languages such as MATLAB, Julia or Mathematica). 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. As for one-dimensional array literals, indexing of two-dimensional array literals also starts at 1. 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> ]  and their index sets are 1..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, with index sets 1..m for the first and 1..n for the second dimension. 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 using bracket syntax: a[i,j] gives the element at row index $$i^{th}$$ and column index $$j^{th}$$. 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 [2 + 1, 3 + 1, 3 + 2] 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 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 != a, a != a, a != a ] and so the forall function returns the logical conjunction a != a /\ a != a /\ a != a. 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.

Listing 2.2.4 Model for the cryptarithmetic problem SEND+MORE=MONEY (send-more-money.mzn)
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 an actual 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. Listing 2.2.5 Model for generalized Sudoku problem (sudoku.mzn) 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"];  Listing 2.2.6 Example data file for generalised Sudoku problem (sudoku.dzn) 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|]; Fig. 2.2.1 The problem represented by data file sudoku.dzn 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). In the MiniZinc IDE, this can be achieved by setting the number of solutions to zero in the “User-defined behavior” part of the solver configuration pane (see Section 3.2.2.2). On the command line, one can use 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. Listing 2.2.7 Model for coloring Australia using enumerated types (aust-enum.mzn). 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 an 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 an 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 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.

It is often useful to define an enumerated type by extending another enumerated type. A good example for this is adding a “don’t care” value to a type. For instance, consider a typical assignment problem, where we have to assign chores to flatmates. The names of the flatmates are given as an enumerated type Flatmates, and the chores as an enumerated type Chores.

In the case where not every flatmate needs to be assigned a job, and not every chore needs to be assigned to a flatmate, we need to introduce a value that signals “no assignment”. We can then use the alldifferent_except constraint to constrain the assignment.

Listing 2.2.8 Model for an assignment problem using extended enumerated types (assignment-extend-enum.mzn).
include "alldifferent_except.mzn";

enum Flatmates = { Anne, Bert, Ceci, Dave };
enum Chores = { Cooking, Vacuuming, Bathroom, Kitchen, Rubbish };

enum ChoreOrNothing = C(Chores) ++ { Nothing };

array[Flatmates] of var ChoreOrNothing: assignment;

constraint alldifferent_except(assignment, {Nothing});

output [ show(f)++":\t"++
if fix(assignment[f])=Nothing then "Nothing\n"
else show(C^-1(assignment[f]))++"\n" endif
| f in Flatmates];


The model is shown in Listing 2.2.8. It introduces a new enumerated type ChoreOrNothing, which is a combination of the Chores type and a new constructor Nothing. The Chores type is injected into the new type via a constructor function. In this case, we chose the name C for the constructor function, but you can use any valid identifier you like. The constructor function can then be used to coerce a value of type Chores into a value of the new type ChoreOrNothing. For example, we could write a constraint such as

constraint assignment[Bert] = C(Vacuuming);


This is required because the type ChoreOrNothing is considered different from the type Chores by the compiler, in order to avoid any ambiguities or accidental type conversions. The compiler also generates inverses of all constructor functions, to map back from the extended types into the base types. This can be seen in the output item, where C^-1 is used to map from ChoreOrNothing back to Chores. The inverse function can be written as ASCII using the ^-1 notation, or using the unicode symbols ⁻¹, as in C⁻¹.

In the example above, we could have used option types, instead of adding a new value that acts essentially like the absent value <>. However, extended enum types have some advantages in this scenario. For example, we can extend a type by multiple extra values (say, different reasons for not being assigned any chore). We can also combine multiple enumerated types into one. Consider the scenario in Listing 2.2.9 where we can assign chores to flatmates and/or robots. Flatmates have preferences for certain chores, while robots have integer benefits. The model also shows how to test if a value of the extended enum in fact belongs to the underlying enum: In the output item, the test w in F(Flatmates) uses the F constructor to translate the set Flatmates into the Worker type, which enables the use of the in operator.

Listing 2.2.9 Model for an assignment problem using combined enumerated types (assignment-combine-enums.mzn).
include "alldifferent_except.mzn";

enum Flatmates = { Anne, Bert, Ceci, Dave };
enum Robots = { R2D2, C3PO, Marvin };

% Higher number = higher preference
array[Flatmates,Chores] of int: preference =
[| 1, 2, 3, 4, 5
| 2, 1, 3, 4, 5
| 3, 5, 4, 1, 2
| 1, 5, 4, 2, 3 |];

array[Robots,Chores] of int: benefit =
[| 20, 100, 20, 100, 30
| 10, 120, 40, 40,  60
| 50, 500, 30, 10,  70 |];

enum Chores = { Cooking, Vacuuming, Bathroom, Kitchen, Rubbish };

enum Workers = F(Flatmates) ++ R(Robots);

enum ChoreOrNothing = C(Chores) ++ { Nothing };

% Create preference and benefit arrays with added values for Nothing

array[Flatmates,ChoreOrNothing] of int: prefWithNothing =
array2d(Flatmates,ChoreOrNothing,
[ if c=Nothing then 0 else preference[f,C^-1(c)] endif
| f in Flatmates, c in ChoreOrNothing]);

array[Robots,ChoreOrNothing] of int: benefitWithNothing =
array2d(Robots,ChoreOrNothing,
[ if c=Nothing then 0 else benefit[r,C^-1(c)] endif
| r in Robots, c in ChoreOrNothing]);

array[Workers] of var ChoreOrNothing: assignment;

constraint alldifferent_except(assignment, {Nothing});

var int: overall_preferences =
sum(f in Flatmates)(prefWithNothing[f, assignment[F(f)]]);

var int: overall_benefit =
sum(r in Robots)(benefitWithNothing[r, assignment[R(r)]]);

solve maximize overall_benefit + overall_preferences;

output [ if w in F(Flatmates) then show(F^-1(w)) else show(R^-1(w)) endif++":\t"++
if fix(assignment[w])=Nothing then "Nothing\n"
else show(C^-1(assignment[w]))++"\n" endif
| w in Workers];


## 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.

Listing 2.2.10 Model for job-shop scheduling problems (jobshop.mzn).
enum JOB;
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(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 ];  Listing 2.2.11 Data for job-shop scheduling problems (jdata.dzn). 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 2.2.10 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 for small instances of this problem it 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 --all-solutions for optimisation problems (note that when running this in the MiniZinc IDE, --all-solutions is the default behaviour for optimisation problems). Here the solver outputs increasingly better solutions as it finds them, 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. In the MiniZinc IDE, you can select “User-defined behavior” in the configuration editor and uncheck the option “Stop after this many solutions” in order to display all solutions of this satisfaction problem.

## 2.2.6. Array Access Constraints¶

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 2.2.12 and Listing 2.2.13.

Listing 2.2.12 Model for the stable marriage problem (stable-marriage.mzn).
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"];  Listing 2.2.13 Example data for the stable marriage problem (stable-marriage.dzn). 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 |];  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.

Listing 2.2.14 Model solving the magic series problem (magic-series.mzn).
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 2.2.14. 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 (the effect of --all-solutions can be achieved in the MiniZinc IDE using the “User-defined behavior” option in the solver configuration pane). Note that MiniZinc will automatically coerce Booleans to integers and integers to floats when required. We could replace the the constraint item in Listing 2.2.14 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. ## 2.2.7. Partiality and Relational Semantics¶ Since MiniZinc supports complex constraints, it is important to understand how partial functions are handled. For example, we would expect the following model to have the solution b=true; x=1, but also b=false; x=0 and b=false; x=1: var bool: b; var 0..10: x; constraint b -> 10 div x = 10;  However, division is a partial function, and 10 div 0 is undefined. So how exactly does MiniZinc handle partiality? MiniZinc implements the relational semantics, which means that any undefinedness “bubbles up” to the closest Boolean context and becomes false there. In the case above, the closest Boolean context of the division is the = operator, so the right hand side of the implication becomes false. This forces the left hand side to be false as well. MiniZinc has a number of built-in partial functions. The most common one is probably array access, since an expression x[i] is undefined if i is not in the index set of x. In order to “guard” against partiality, we can use conditional statements like if y=0 then 0 else x div y endif, or if i in index_set(x) then x[i] else 0 endif, which define a default value for the cases where the expression is undefined. A shorthand notation for this is the default operator, which allows you to write (x div y) default 0 or x[i] default 0 instead. ## 2.2.8. 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 natural model is given in Listing 2.2.15. Listing 2.2.15 Model for the 0/1 knapsack problem (knapsack.mzn). 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. Listing 2.2.16 Model for the social golfers problems (social-golfers.mzn). 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 2.2.16. 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.

## 2.2.9. 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.

Listing 2.2.17 Planning wedding seating using enumerated types (wedding.mzn).
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 2.2.17 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. Fig. 2.2.2 Seating arrangement at the wedding table

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.