Consider the following data:

---- 16 PARAMETER data input data start length cost job1 2 5 47 job2 19 5 20 job3 11 2 38 job4 5 2 14 job5 5 8 40 job6 3 10 26 job7 6 3 68 job8 19 5 61 job9 10 80 job10 10 4 37 job11 22 2 70 job12 12 7 78 job13 22 2 67 job14 17 7 35 job15 1 4 17 job16 13 4 19 job17 1 8 68 job18 4 9 59 job19 14 8 12 job20 8 6 82

Note: zeros are not printed.

The problem is:

Find a subset of jobs that don't overlap and cover each hour 0 through 23, and that minimize cost.

This is slightly different than the data in [1], in order to make the problem feasible.

#### Set Partitioning Model

Before we can work on the model itself, we need to develop some derived data. We introduce the boolean parameter \(\mathit{Cover}_{i,t}\) defined by: \[\mathit{Cover}_{i,t}=\begin{cases} 1 & \text{if job $i$ covers hour $t$}\\ 0 & \text{otherwise}\end{cases}\] This parameter looks like:

---- 16 PARAMETER cover coverage of hours by jobs h00 h01 h02 h03 h04 h05 h06 h07 h08 job1 1 1 1 1 1 job4 1 1 job5 1 1 1 1 job6 1 1 1 1 1 1 job7 1 1 1 job9 1 1 1 1 1 1 1 1 1 job15 1 1 1 1 job17 1 1 1 1 1 1 1 1 job18 1 1 1 1 1 job20 1 + h09 h10 h11 h12 h13 h14 h15 h16 h17 job3 1 1 job5 1 1 1 1 job6 1 1 1 1 job9 1 job10 1 1 1 1 job12 1 1 1 1 1 1 job14 1 job16 1 1 1 1 job18 1 1 1 1 job19 1 1 1 1 job20 1 1 1 1 1 + h18 h19 h20 h21 h22 h23 job2 1 1 1 1 1 job8 1 1 1 1 1 job11 1 1 job12 1 job13 1 1 job14 1 1 1 1 1 1 job19 1 1 1 1

We further introduce the binary decision variables: \[x_i = \begin{cases} 1 & \text{if job $i$ is selected} \\ 0 & \text{otherwise}\end{cases}\] With this we can formulate:

Set Partitioning Model |
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\[\begin{align}\min & \sum_i \color{darkblue}{\mathit{Cost}}_i \cdot \color{darkred}x_i \\ & \sum_i \color{darkblue}{\mathit{Cover}}_{i,t} \cdot \color{darkred}x_i = 1 &&\forall t \\ & \color{darkred}x_i \in \{0,1\} \end{align}\] |

When we solve this problem using the above data set we get the solution:

---- 31 VARIABLE x.L selected jobs job9 1, job10 1, job13 1, job19 1 ---- 31 VARIABLE tcost.L = 196 total cost

It is noted that the model can also be stated using a set representation of the coverage data. Such a model can look like:

Set Partitioning Model 2 |
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\[\begin{align}\min & \sum_i \color{darkblue}{\mathit{Cost}}_i \cdot \color{darkred}x_i \\ & \sum_{i|\color{darkblue}{\mathit{Cover}}(i,t)} \color{darkred}x_i = 1 &&\forall t \\ & \color{darkred}x_i \in \{0,1\} \end{align}\] |

Although this looks somewhat different, it is really exactly the same. This is actually the form I use most of the time.

#### Infeasibilities

It is quite easy to see data sets that cause the problem to be infeasible. Indeed, the data set shown in [1] is actually yielding a model that is not feasible.

There are two ways in which the model can be infeasible. The first is the easy one: we have no jobs at all that cover a time period \(t\). Consider the data:

---- 16 PARAMETER data input data start length cost job1 2 12 63 job2 19 5 85 job3 11 2 31 job4 5 8 70 job5 5 2 80 job6 3 4 37 job7 6 9 20 job8 19 5 55 job9 5 24 job10 10 5 89 job11 22 2 34 job12 12 2 36 ---- 16 PARAMETER cover coverage of hours by jobs h00 h01 h02 h03 h04 h05 h06 h07 h08 job1 1 1 1 1 1 1 1 job4 1 1 1 1 job5 1 1 job6 1 1 1 1 job7 1 1 1 job9 1 1 1 1 1 + h09 h10 h11 h12 h13 h14 h19 h20 h21 job1 1 1 1 1 1 job2 1 1 1 job3 1 1 job4 1 1 1 1 job7 1 1 1 1 1 1 job8 1 1 1 job10 1 1 1 1 1 job12 1 1 + h22 h23 job2 1 1 job8 1 1 job11 1 1

We see that hours 15, 16, 17, and 18 are not covered by any job. This can be easily checked in advance. Even GAMS is complaining when generating the model:

**** Exec Error at line 24: Equation infeasible due to rhs value **** INFEASIBLE EQUATIONS ... ---- ecover =E= ecover(h15).. 0 =E= 1 ; (LHS = 0, INFES = 1 ****) ecover(h16).. 0 =E= 1 ; (LHS = 0, INFES = 1 ****) ecover(h17).. 0 =E= 1 ; (LHS = 0, INFES = 1 ****) ecover(h18).. 0 =E= 1 ; (LHS = 0, INFES = 1 ****)

There is another form of infeasibility that is more difficult to diagnose. When we use the data set:

---- 16 PARAMETER data input data start length cost job1 2 6 67 job2 19 5 52 job3 11 5 47 job4 5 2 20 job5 5 2 38 job6 3 8 14 job7 6 10 40 job8 19 3 26 job9 8 68 job10 10 10 61 job11 22 2 80 job12 12 2 37 job13 22 2 70 job14 17 2 78 job15 1 11 67 job16 13 4 35 job17 1 4 17 job18 4 8 19 job19 14 9 68

we get:

S O L V E S U M M A R Y MODEL m OBJECTIVE tcost TYPE MIP DIRECTION MINIMIZE SOLVER CPLEX FROM LINE 28 **** SOLVER STATUS 1 Normal Completion **** MODEL STATUS 10 Integer Infeasible **** OBJECTIVE VALUE NA

There is not much in diagnostics for most solvers. Cplex at least reports:

Infeasibility row 'ecover(h08)': 0 = 1.

This is better than most solvers, but may be difficult to digest for an end-user. In applications, it may be better to make sure the problem is never infeasible. Basically, allow the constraint to become infeasible but at a price. This is sometimes called an

**elastic formulation**. There are two ways to do this for this model:

- use a data-driven method: introduce expensive emergency jobs used to make the constraint feasible
- change the constraint directly

#### Data-driven Elastic model

We can add expensive filler-jobs to our data set as follows:

---- 21 PARAMETER data input data start length cost job1 2 6 67 job2 19 5 52 job3 11 5 47 job4 5 2 20 job5 5 2 38 job6 3 8 14 job7 6 10 40 job8 19 3 26 job9 8 68 job10 10 10 61 job11 22 2 80 job12 12 2 37 job13 22 2 70 job14 17 2 78 job15 1 11 67 job16 13 4 35 job17 1 4 17 job18 4 8 19 job19 14 9 68 notcov00 1 999 notcov01 1 1 999 notcov02 2 1 999 notcov03 3 1 999 notcov04 4 1 999 notcov05 5 1 999 notcov06 6 1 999 notcov07 7 1 999 notcov08 8 1 999 notcov09 9 1 999 notcov10 10 1 999 notcov11 11 1 999 notcov12 12 1 999 notcov13 13 1 999 notcov14 14 1 999 notcov15 15 1 999 notcov16 16 1 999 notcov17 17 1 999 notcov18 18 1 999 notcov19 19 1 999 notcov20 20 1 999 notcov21 21 1 999 notcov22 22 1 999 notcov23 23 1 999

When we solve this, we see the following solution:

---- 36 VARIABLE x.L selected jobs job12 1, job15 1, job19 1, notcov00 1, notcov23 1 ---- 36 VARIABLE tcost.L = 2170 total cost

So we have trouble covering 2 hours with jobs. If we allow then to remain like that, the best solution is to use jobs 12, 15 and 19 for the other hours.

#### Elastic model: change constraint

Instead of changing the data, we can also do the same by changing the covering constraint:

Set Partitioning Model, elastic version |
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\[\begin{align}\min & \sum_i \color{darkblue}{\mathit{Cost}}_i \cdot \color{darkred}x_i + \color{darkblue}{\mathit{Penalty}} \cdot \sum_t\color{darkred}{\mathit{Uncov}}_t \\ & \sum_i \color{darkblue}{\mathit{Cover}}_{i,t} \cdot \color{darkred}x_i + \color{darkred}{\mathit{Uncov}}_t = 1 &&\forall t \\ & \color{darkred}x_i \in \{0,1\} \\ & \color{darkred}{\mathit{Uncov}}_t \in [0,1] \end{align}\] |

You can see we added a slack variable \({\mathit{Uncov}}_t\), and included this in the objective with a penalty (999). When we run this model, we see:

---- 44 VARIABLE x.L selected jobs job12 1, job15 1, job19 1 ---- 44 VARIABLE uncov.L uncovered hours h00 1, h23 1 ---- 44 VARIABLE tcost.L = 2170 total cost (objective) PARAMETER jobcost = 172 cost related to running jobs PARAMETER penalties = 1998 for uncovered hours

#### References

- Profit maximising job scheduling using Python, https://stackoverflow.com/questions/62297792/profit-maximising-job-scheduling-using-python
- Garfinkel, R. S., and G. L. Nemhauser. “
*The Set-Partitioning Problem: Set Covering with Equality Constraints.*” Operations Research, vol. 17, no. 5, 1969, pp. 848–856.

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