# 2.4. Option Types¶

Option types are a powerful abstraction that allows for concise modelling. An
option type decision variable represents a decision that has another
possibility \(\top\), represented in MiniZinc as `<>`

indicating the variable is *absent*.
Option type decisions are useful for modelling problems where a decision is
not meaningful unless other decisions are made first.

## 2.4.1. Declaring and Using Option Types¶

Option type Variables

An option type variable is declared as:

```
var opt <type> : <var-name>
```

where `<type>`

is one of `int`

, `float`

or `bool`

or
a fixed range expression.
Option type variables can be parameters.

An option type variable can take the additional value
`<>`

indicating *absent*.

An option type variable behaves like a normal, non-optional variable, as long as it is not absent, i.e., as long as it is not equal to `<>`

.
An absent option type variable should behave as if the object it represents does not exist. This means different things for different functions and relations.

For example, when adding `<>`

to another variable `x`

, the result is just `x`

. Similarly, `all_different([x,y,z])`

should behave exactly like
`all_different([x,y])`

in case `z=<>`

, i.e., `z`

is absent.

Option type variables can be used like their non-optional versions with most operators on Booleans, integers and floats. Many functions and predicates in the MiniZinc library are also defined for option type variables. Here are a few examples of how different functions and operators treat option types:

```
% Expression: Equivalent to: Simplified:
<> + a = 0 + a = a
a * <> = a * 1 = a
sum([x1,<>,x2]) = sum([x1,x2])
sum([<>,<>]) = sum([]) = 0
product([x1,x2,<>]) = product([x1,x2])
product([<>,<>,<>]) = product([]) = 1
exists([b1,<>]) = exists([b1]) = b1
exists([<>,<>]) = exists([]) = false
forall([<>,b1,b2]) = forall([b1,b2])
forall([<>,<>]) = forall([]) = true
all_different([x,<>,y]) = all_different([x,y])
```

Comparison operators return `true`

if any of their arguments is absent. For instance, `3 <= <>`

is `true`

, as is `<> <= 3`

.
However, note that equality between option type expressions is only `true`

if both expressions have the same optionality: `<> = <>`

is `true`

,
but `3 = <>`

is `false`

. If you need the “weaker” version of equality, MiniZinc provides the `~=`

operator: `3 ~= <>`

is `true`

. The `~!=`

operator is the weak version of disequality, it is true is either side is absent or they are not equal. Note that `a ~!= b`

is different from `not (a ~= b)`

, because `<> ~!= <>`

is true, while `not (<> ~= <>)`

is false.

Similarly, it can sometimes be useful to have “weak” versions of the arithmetic operators that return `<>`

if any of their arguments is absent. MiniZinc provides the `~+`

, `~-`

, `~*`

, `~/`

and `~div`

operators for this purpose (e.g., `3 + <> =3`

, but `3 ~+ <> = <>`

).

Operations on option type variables

Two builtin functions and a binary operator are provided for option type variables:
`absent(v)`

returns `true`

iff option type variable `v`

takes the value
`<>`

,
`occurs(v)`

returns `true`

iff option type variable `v`

does *not* take the value
`<>`

,
and
`x default y`

returns `x`

if `x`

occurs, and `y`

otherwise.

In addition, the function
`deopt(v)`

returns the normal value of `v`

or fails if it takes the
value `<>`

. This function should not be used in normal models, but is required
to implement predicates over option type variables.

## 2.4.2. Option Types in Scheduling Problems¶

A common use of option types is for optional tasks in scheduling.
In the flexible job shop scheduling problem we have `n`

tasks to perform
on `k`

machines, and the time to complete each task on each machine
may be different. The aim is to minimize the completion time of all tasks.
A model using option types to encode the problem is given in
Listing 2.4.1. We model the problem using \(n \times k\) optional
tasks representing the possibility of each task run on each machine.
We require that start time of the task and its duration spans the optional
tasks that make it up, and require only one actually runs using the
`alternative`

global constraint.
We require that at most one task runs on any machine using the
`disjunctive`

global constraint extended to optional tasks.
Finally we constrain that at most `k`

tasks run at any time, a redundant
constraint that holds on the actual (not optional) tasks.

```
include "globals.mzn";
int: horizon; % time horizon
set of int: Time = 0..horizon;
enum Task;
enum Machine;
array[Task,Machine] of int: d; % duration on each machine
int: maxd = max([ d[t,m] | t in Task, m in Machine ]);
int: mind = min([ d[t,m] | t in Task, m in Machine ]);
array[Task] of var Time: S; % start time
array[Task] of var mind..maxd: D; % duration
array[Task,Machine] of var opt Time: O; % optional task start
constraint forall(t in Task)(alternative(S[t],D[t],
[O[t,m]|m in Machine],[d[t,m]|m in Machine]));
constraint forall(m in Machine)
(disjunctive([O[t,m]|t in Task],[d[t,m]|t in Task]));
constraint cumulative(S,D,[1|i in Task],card(Machine));
solve minimize max(t in Task)(S[t] + D[t]);
```

## 2.4.4. Option Type Parameters¶

Option type variables of fixed parameter type can be used to define optional model parameters. For example, a variable defined like this:

```
opt bool: enable_feature;
```

could be used to enable or disable a certain optional feature of a model.
In contrast to other parameter variables, these optional parameters do not
have to be assigned a value (e.g., the data file may omit an assignment to
`enable_feature`

). In that case, the variable is automatically assigned
the value `<>`

. In the model, such an optional parameter could be used
like this:

```
bool: enable_feature_enabled = enable_feature default false;
constraint if enable_feature_enabled then ... endif;
```