# Part 9: Cumulative Scheduling¶

We ask you not to publish your solutions on a public repository. The instructors interested to get the source code of our solutions can contact us.

Cumulative

## Cumulative Constraint: Decomposition¶

The Cumulative constraint models a scheduling resource with fixed capacity. It has the following signature:

public Cumulative(IntVar[] start, int[] duration, int[] requirement, int capa)


where capa is the capacity of the resource and start, duration, and requirement are arrays of equal size representing the following properties of the activities:

• start[i] is the variable specifying the start time of activity i,
• duration[i] is the duration of activity i, and
• requirement[i] is the resource requirement of activity i.

The constraint ensures that the cumulative resource requirement of activities (also called the requirement profile) never exceeds the capacity: The following example depicts three activities and their corresponding requirement profile. As can be observed, the profile never exceeds the capacity 4: It corresponds to the instantiation of the following Cumulative constraint:

Cumulative(start = [1, 2, 3], duration = [8, 3, 3], requirement = [1, 2, 2], capa = 4)


Implement CumulativeDecomposition.java. This is a decomposition or reformulation of the Cumulative constraint in terms of simple arithmetic and logical constraints as used in the equation above to describe its semantics.

At any point in time t the BoolVar overlaps[i] designates whether activity i overlaps, potentially being performed at, t or not. The overall resource requirement at t can then be obtained by: First make sure you understand the following code, and then add the few lines in its TODO task in order to make sure overlaps has the intended meaning:

public void post() throws InconsistencyException {

int min = Arrays.stream(start).map(s -> s.getMin()).min(Integer::compare).get();
int max = Arrays.stream(end).map(e -> e.getMax()).max(Integer::compare).get();

for (int t = min; t < max; t++) {

BoolVar[] overlaps = new BoolVar[start.length];
for (int i = 0; i < start.length; i++) {
overlaps[i] = makeBoolVar(cp);

// TODO
// post the constraints to enforce
// that overlaps[i] is true iff start[i] <= t && t < start[i] + duration[i]
// hint: use IsLessOrEqual, introduce BoolVar, use views minus, plus, etc.
//       logical constraints (such as logical and can be modeled with sum)

}

IntVar[] overlapHeights = makeIntVarArray(cp, start.length, i -> mul(overlaps[i], requirement[i]));
IntVar cumHeight = sum(overlapHeights);
cumHeight.removeAbove(capa);

}


Check that your implementation passes the tests CumulativeDecompTest.java.

## Cumulative Constraint: Time-Table Filtering¶

The timetable filtering algorithm introduced in [TT2015] is an efficient yet simple filtering algorithm for Cumulative.

It is a two-stage algorithm:

1. Build an optimistic profile of the resource requirement and check that it does not exceed the capacity.
2. Filter the earliest start of the activities such that they are not in conflict with the profile.

Consider in the next example the depicted activity that can be executed anywhere between the two solid brackets. It cannot execute at its earliest start since this would violate the capacity of the resource. We thus need to postpone the activity until a point in time where it can execute over its entire duration without being in conflict with the profile and the capacity. The earliest point in time is 7: Profiles

We provide a class Profile.java that is able to efficiently build a resource profile given an array of rectangles as input. A rectangle has three attributes: start, end, and height, as shown next: Indeed, a profile is nothing more than a sequence of rectangles. An example profile is given next. It is built from three input rectangles provided to the constructor of Profile.java.

The profile consists of 7 contiguous rectangles. The first rectangle, R0, starts at Integer.MIN_VALUE with a height of zero, and the last rectangle, R6, ends at Integer.MAX_VALUE, also with a height of zero. These two dummy rectangles are convenient because they guarantee that there exists a rectangle in the profile for any point in time: Make sure you understand how to build and manipulate Profile.java.

Have a look at ProfileTest.java for some examples of profile construction.

Filtering

Implement Cumulative.java. You have three TODO tasks:

1. Build the optimistic profile from the mandatory parts.
2. Check that the profile is not exceeding the capacity.
3. Filter the earliest start of activities.

TODO 1 is to build the optimistic profile from the mandatory parts of the activities. As can be seen in the next example, the mandatory part of an activity is a part that is always executed whatever the start time of the activity will be in its current domain. It is the rectangle starting at start[i].getMax() that ends in start[i].getMin()+duration[i] with a height equal to the resource requirement of the activity. Be careful because not every activity has a mandatory part: TODO 2 is to check that the profile is not exceeding the capacity. You can check that each rectangle of the profile is not exceeding the capacity; otherwise you throw an InconsistencyException.

TODO 3 is to filter the earliest start of unfixed activities by postponing each activity (if needed) to the earliest slot when it can be executed without exceeding the capacity.

for (int i = 0; i < start.length; i++) {
if (!start[i].isFixed()) {
// j is the index of the profile rectangle overlapping t
int j = profile.rectangleIndex(start[i].getMin());
// TODO 3: postpone i to a later point in time
// hint:
// Check that at every point in the interval
// [start[i].getMin() ... start[i].getMin()+duration[i]-1]
// there is enough remaining capacity.
// You may also have to check the following profile rectangle(s).
// Note that the activity you are currently postponing
// may have contributed to the profile.
}
}


Check that your implementation passes the tests CumulativeTest.java.

 [TT2015] Gay, S., Hartert, R., & Schaus, P. (2015). Simple and scalable time-table filtering for the cumulative constraint. International Conference on Principles and Practice of Constraint Programming, pp. 149-157. Springer. (PDF)

## The Resource-Constrained Project Scheduling Problem (RCPSP)¶

A set of activities must be executed on a set of resources.

Several instances of increasing size are available, with 30, 60, 90, and 120 activities. In order to test your model, note that the instance j30_1_1.rcp should have a minimum makespan of 43. Do not expect to prove optimality for large-size instances, but you should reach it easily for 30 activities.