or_example.py

# We define our solver.
solver = pywraplp.Solver('StudentProjectGridCBC', pywraplp.Solver.CBC_MIXED_INTEGER_PROGRAMMING)

# We define the set of assignments that represent a solution; each cell is either 0 or 1.
matches = {}
for student in STUDENTS:
    for project in PROJECTS:
        matches[student, project] = solver.IntVar(0, 1, 'matches[%s,%s]' % (student, project))

# We define the objective function we want to minimize.
z = solver.Sum(PREFERENCES[project][student]] *  matches[student, project]
                 for student in STUDENTS
                 for project in PROJECTS])

# Constraint: 1 person per project.
for student in STUDENTS:
    solver.Add(solver.Sum([matches[student, project] for project in PROJECTS]) == 1)

# Constraint: at least 1 international student per project.
for project in PROJECTS:
    solver.Add(solver.Sum([matches[student, project] * INTERNATIONAL[student]) for student in STUDENTS]) >= 1)

# Constraint: good gender diversity.
for project in PROJECTS:
    solver.Add(solver.Sum([matches[student, project] * GENDER[student] for student in STUDENTS]) >= 2)

# Constraint: balanced teams.
for project in PROJECTS:
    solver.Add(solver.Sum([matches[student, project] for student in STUDENTS]) >= 3)
    solver.Add(solver.Sum([matches[student, project] for student in STUDENTS]) <= RATIO)
      
# Constraint: hard-coded assignments.
hard_coded_assignments = {"Arnaud": "Crowd Manager", "Neil": "Crowd Manager"}
for student in STUDENTS:
    if student in hard_coded_assignments:
        solver.Add(matches[student, hard_coded_assignments[student]] == 1)

objective = solver.Minimize(z)
solver.Solve()