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| 1 | +from pyscipopt import Model |
| 2 | + |
| 3 | +# Define categorical data |
| 4 | +shifts = {"Morning": 0, "Afternoon": 1, "Night": 2} |
| 5 | +employees = ["Alice", "Bob", "Charlie"] |
| 6 | + |
| 7 | +# Employees have different salary demands |
| 8 | +cost = { |
| 9 | + "Alice": [2,4,1], |
| 10 | + "Bob": [3,2,7], |
| 11 | + "Charlie": [3,3,3] |
| 12 | +} |
| 13 | + |
| 14 | +# Employee availability |
| 15 | +availability = { |
| 16 | + "Alice": ["Morning", "Afternoon"], |
| 17 | + "Bob": ["Afternoon", "Night"], |
| 18 | + "Charlie": ["Morning", "Night"] |
| 19 | +} |
| 20 | + |
| 21 | +# Transform availability into integer values |
| 22 | +availability_int = { |
| 23 | + emp: [shifts[shift] for shift in available_shifts] |
| 24 | + for emp, available_shifts in availability.items() |
| 25 | +} |
| 26 | + |
| 27 | +# Create the model |
| 28 | +model = Model("Shift Assignment") |
| 29 | + |
| 30 | +# x[e, s] = 1 if employee e is assigned to shift s |
| 31 | +x = {} |
| 32 | +for e in employees: |
| 33 | + for s in shifts.values(): |
| 34 | + x[e, s] = model.addVar(vtype="B", name=f"x({e},{s})") |
| 35 | + |
| 36 | +# Each shift must be assigned to exactly one employee |
| 37 | +for s in shifts.values(): |
| 38 | + model.addCons(sum(x[e, s] for e in employees) == 1) |
| 39 | + |
| 40 | +# Employees can only work shifts they are available for |
| 41 | +for e in employees: |
| 42 | + for s in shifts.values(): |
| 43 | + if s not in availability_int[e]: |
| 44 | + model.addCons(x[e, s] == 0) |
| 45 | + |
| 46 | +# Minimize shift assignment cost |
| 47 | +model.setObjective( |
| 48 | + sum(cost[e][s]*x[e, s] for e in employees for s in shifts.values()), "minimize" |
| 49 | +) |
| 50 | + |
| 51 | +# Solve the problem |
| 52 | +model.optimize() |
| 53 | + |
| 54 | +# Display the results |
| 55 | +print("\nOptimal Shift Assignment:") |
| 56 | +for e in employees: |
| 57 | + for s, s_id in shifts.items(): |
| 58 | + if model.getVal(x[e, s_id]) > 0.5: |
| 59 | + print("%s is assigned to %s" % (e, s)) |
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