My first year programming class culminates in a final week of group presentations. This is always a highlight of the teaching period as I get to see the awesome things my students have come up with. However, scheduling 30-40 group presentations every year could be a real nightmare. This is where mathematics comes to the rescue. I’ll describe in this post how I use linear programming implemented in the Python library Pulp to get the schedule easily.

I have previously done this using Sagemath but this year I decided to use Pulp (“A python Linear Programming API”). This probably follows from @DRMacIver’s talk at PyCon UK 2016: “Easy solutions to hard problems” in which I saw Pulp for the first time.

The first step to solving this problem is getting the availability of all the student groups. I use doodle for this just to get a quick poll with dates and times. Doodle has the ability to export a poll to excel so I open that up in spreadsheet and tidy things up a bit (deleting irrelevant rows/columns).

Once I’ve done that I can read in the file which I do using Pandas:

>>> import pandas as pd
>>> df = pd.read_excel("availability.xls")
>>> df = df.replace("OK", 1)
>>> df = df.fillna(0)
Company          Time slot
0               Alternative Maths  Wed 13:30 – 14:00
1                          Europa  Thu 13:00 – 13:30
2                  L.A.S.T Resort  Mon 12:30 – 13:00
3            DividedByZeroStudios  Mon 17:30 – 18:00
4                   Effervescence  Fri 17:30 – 18:00



I then transform that in to a numeric array:

>>> import numpy as np
>>> A = np.array(df)



Now I start using Pulp, first let’s create a problem:

>>> import pulp
>>> M, N = A.shape  # Dimensions
>>> prob = pulp.LpProblem("Scheduling")
>>> x = pulp.LpVariable.dicts("x", itertools.product(range(M), range(N)),
cat=pulp.LpBinary)  # Variables


Then we add constraints:

Ie $$x_{ij}$$ can be 1 iff team $$i$$ is available in slot $$j$$.

Here is how we add the constraint to our pulp prob:

>>> for index in x:
... 	try:
...         x[index].upBound = float(A[index])
...     except ValueError:  # Seemed to be an artifact in the matrix
...         x[index].upBound = 0



Next we need to make sure that any slot can only be used by one team at a time:

Ie slot $$j$$ can only be used by one team.

>>> for slot in range(N):
... 	prob += sum(x[(team, slot)] for team in range(M)) <= 1



I quite like this syntax: we’re “literally” adding the constraints to our problem.

We now need to make sure that each team appears in exactly one slot:

>>> for team in range(M):
...     prob += sum(x[(team, slot)] for slot in range(N)) == 1



Once we have done this we could add an objective function. I could for example write a mathematical function that chooses to minimise the number of days used but in practice I don’t need to do that so I’m just looking for a feasible solution to the problem:

>>> prob.solve(pulp.GLPK())
1



The 1 is the status which confirms in this case that a solution has been found.

So let us take a look at the solution:

>>> solution = []
>>> for company in range(M):
...     for slot in range(N):
...         if x[(company, slot)].value() == 1:
...         	solution.append([df.index[company], df.columns[slot]])
>>> df = pd.DataFrame(solution, columns=["Company", "Time slot"])
>>> df.sort_values(by="Company")
Company          Time slot
14                          Apollo  Tue 16:30 – 17:00
17                  Coding Cymraeg  Thu 13:30 – 14:00
23                       Complexus  Tue 10:00 – 10:30
3             DividedByZeroStudios  Mon 17:30 – 18:00
4                    Effervescence  Fri 17:30 – 18:00
1                           Europa  Thu 13:00 – 13:30
11                        F.E.J.L.  Tue 15:00 – 15:30
12                     Ferdie Amor  Wed 12:30 – 13:00
16                         Framtak  Tue 17:30 – 18:00
15                   FridgeVentory  Tue 17:00 – 17:30
30                   Generic Group  Tue 16:00 – 16:30
26                       GeoCampus  Tue 11:30 – 12:00
5       Green and Russian Standard  Fri 16:30 – 17:00
19                            JALE  Mon 16:00 – 16:30
27                           JEM'D  Fri 16:00 – 16:30
7                            J^2AG  Wed 16:30 – 17:00
2                   L.A.S.T Resort  Mon 12:30 – 13:00
29                MACT enterprises  Tue 12:30 – 13:00
10                            MBAS  Thu 17:30 – 18:00
21                            MRJL  Mon 15:30 – 16:00
8                            Merge  Wed 09:00 – 09:30
13                          MyTime  Tue 15:30 – 16:00
22                        Noteable  Mon 17:00 – 17:30
20                        Oakheart  Mon 15:00 – 15:30
24                         PoshFit  Tue 10:30 – 11:00
25                       ReCollect  Tue 11:00 – 11:30
0                Alternative Maths  Wed 13:30 – 14:00
6                           Room 1  Tue 14:00 – 14:30
9                     StockSensors  Fri 11:30 – 12:00
28                           Swigg  Tue 12:00 – 12:30
18                        The Cyps  Mon 16:30 – 17:00
31                    Ticket Tiger  Mon 13:00 – 13:30



This is a great example of using implemented applied mathematics to quickly solve a real world problem. The other benefit is that when/if I need to do this again in the feature I simply need to read in a different availability file.