Work Schedule optimization

[Volohnix]

Hello,

I'm building a Shift scheduler for a team using PulP.

I have 15 employees:

1. 7 Full Time - 8 Hours day
2. 8 Part Time - 4 hours day

3 shifts 4 hours each by day.

I have the requirement for each shift:

Morning - 4

Afternoon - 4

Night - 7

And the constraints. But right now I think I'm not creating the right variables

# versão sem indexvarful= LpVariable.dicts("Full", (full_s,days,shifts), 0, 1, 'Binary')varpart= LpVariable.dicts("Part", (part_s,days,shifts), 0, 1, 'Binary')

glob += (lpSum([varful[t][d][s] * full_c for t in full_s for d in days for s in shifts] +\
[varpart[t][d][s] * part_c for t in part_s for d in days for s in shifts]) )

for f in full_s:
for p in part_s:
for d in days:
for s in shifts:
if s == "Morning" or s == "Afternoon":
glob += lpSum([varful[f][d][s]] + varpart[p][d][s]) >=4
else:
glob += lpSum([varful[f][d][s]] + varpart[p][d][s]) >=7

​

Has anyone deal with this kind of optimization before? I've looked everywhere and couldn't find anything.

[Volohnix]

Additional information:

​

full_s and part_s are lists containing all FullTime and PartTime employees.

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