""" Unit tests for constraint builder. Tests each constraint individually. """ import pytest from ortools.sat.python import cp_model import sys from pathlib import Path from zoneinfo import ZoneInfo # Add parent directory to path sys.path.insert(0, str(Path(__file__).parent.parent)) from constraint_builder import ( availability_covers_shift, build_constraints, is_overnight_shift, parse_time, shift_interval, ) def test_parse_time(): """Test time parsing.""" assert parse_time("00:00") == 0 assert parse_time("09:00") == 540 assert parse_time("12:30") == 750 assert parse_time("23:59") == 1439 def test_is_overnight_shift(): """Test overnight shift detection.""" assert is_overnight_shift("22:00", "06:00") is True assert is_overnight_shift("09:00", "17:00") is False assert is_overnight_shift("23:00", "01:00") is True def test_shift_interval_preserves_repeated_fall_back_hour_from_utc_instants(): start, end = shift_interval( { "date": "2026-11-01", "endDate": "2026-11-01", "startTime": "01:30", "endTime": "01:30", "durationHours": 1.0, "startDateTimeUtc": "2026-11-01T06:30:00+00:00", "endDateTimeUtc": "2026-11-01T07:30:00+00:00", "storeTimezone": "America/Chicago", } ) assert end.timestamp() - start.timestamp() == 3600 local_start = start.astimezone(ZoneInfo("America/Chicago")) local_end = end.astimezone(ZoneInfo("America/Chicago")) assert local_start.strftime("%Y-%m-%d %H:%M") == "2026-11-01 01:30" assert local_end.strftime("%Y-%m-%d %H:%M") == "2026-11-01 01:30" def test_c5_repeated_fall_back_hour_cannot_bypass_empty_availability(): shift = { "date": "2026-11-01", "endDate": "2026-11-01", "startTime": "01:30", "endTime": "01:30", "durationHours": 1.0, "startDateTimeUtc": "2026-11-01T06:30:00+00:00", "endDateTimeUtc": "2026-11-01T07:30:00+00:00", "storeTimezone": "America/Chicago", } assert availability_covers_shift(shift, []) is False def test_c5_repeated_fall_back_hour_requires_enough_positive_availability(): shift = { "date": "2026-11-01", "endDate": "2026-11-01", "startTime": "01:30", "endTime": "01:30", "durationHours": 1.0, "startDateTimeUtc": "2026-11-01T06:30:00+00:00", "endDateTimeUtc": "2026-11-01T07:30:00+00:00", "storeTimezone": "America/Chicago", } availability = [ {"dayOfWeek": 7, "startTime": "01:30", "endTime": "01:31"}, ] assert availability_covers_shift(shift, availability) is False def test_c5_full_day_availability_covers_repeated_fall_back_hour(): shift = { "date": "2026-11-01", "endDate": "2026-11-01", "startTime": "01:30", "endTime": "01:30", "durationHours": 1.0, "startDateTimeUtc": "2026-11-01T06:30:00+00:00", "endDateTimeUtc": "2026-11-01T07:30:00+00:00", "storeTimezone": "America/Chicago", } availability = [ {"dayOfWeek": 7, "startTime": "00:00", "endTime": "23:59"}, ] assert availability_covers_shift(shift, availability) is True def test_c1_one_employee_per_shift(): """Test C1: Each shift assigned to at most 1 employee.""" model = cp_model.CpModel() problem = { "employees": [ {"userId": 1, "role": 4, "hoursMax": 40, "currentPeriodHours": 0, "shiftsThisWeek": 0, "availability": [], "timeOff": [], "allowedPositionIds": []}, {"userId": 2, "role": 4, "hoursMax": 40, "currentPeriodHours": 0, "shiftsThisWeek": 0, "availability": [], "timeOff": [], "allowedPositionIds": []} ], "shifts": [ {"shiftId": 1, "minRoleId": 4, "date": "2026-02-16", "dayOfWeek": 1, "startTime": "09:00", "endTime": "17:00", "durationHours": 8.0, "isLocked": False} ], "lockedShifts": [] } x = { (1, 1): model.NewBoolVar("x_1_1"), (2, 1): model.NewBoolVar("x_2_1") } metadata = build_constraints(model, problem, x) # Constraint should be added assert 1 in metadata["c1_one_per_shift"] # Try to solve - both employees assigned should be infeasible model.Add(x[(1, 1)] == 1) model.Add(x[(2, 1)] == 1) solver = cp_model.CpSolver() solver.parameters.log_search_progress = False status = solver.Solve(model) assert status == cp_model.INFEASIBLE def test_c2_locked_shifts_immutable(): """Test C2: Locked shifts stay with assigned employee.""" model = cp_model.CpModel() problem = { "employees": [ { "userId": 1, "role": 4, "hoursMax": 40, "currentPeriodHours": 0, "shiftsThisWeek": 0, "availability": [{"dayOfWeek": 1, "startTime": "08:00", "endTime": "22:00"}], "timeOff": [], "allowedPositionIds": [] }, { "userId": 2, "role": 4, "hoursMax": 40, "currentPeriodHours": 0, "shiftsThisWeek": 0, "availability": [{"dayOfWeek": 1, "startTime": "08:00", "endTime": "22:00"}], "timeOff": [], "allowedPositionIds": [] } ], "shifts": [ {"shiftId": 1, "minRoleId": 4, "date": "2026-02-16", "dayOfWeek": 1, "startTime": "09:00", "endTime": "17:00", "durationHours": 8.0, "isLocked": True} ], "lockedShifts": [ {"shiftId": 1, "employeeId": 1, "date": "2026-02-16", "startTime": "09:00", "endTime": "17:00", "durationHours": 8.0} ] } x = { (1, 1): model.NewBoolVar("x_1_1"), (2, 1): model.NewBoolVar("x_2_1") } metadata = build_constraints(model, problem, x) assert (1, 1) in metadata["c2_locked_shifts"] # Add dummy objective to make model solvable model.Maximize(0) solver = cp_model.CpSolver() solver.parameters.log_search_progress = False status = solver.Solve(model) assert status in [cp_model.OPTIMAL, cp_model.FEASIBLE] assert solver.Value(x[(1, 1)]) == 1 assert solver.Value(x[(2, 1)]) == 0 def test_c3_explicit_keyholder_qualification(): """Test C3: an explicit keyholder shift requires a keyholder employee.""" model = cp_model.CpModel() problem = { "employees": [ {"userId": 1, "role": 4, "isKeyholder": False, "hoursMax": 40, "currentPeriodHours": 0, "shiftsThisWeek": 0, "availability": [], "timeOff": [], "allowedPositionIds": []}, {"userId": 2, "role": 4, "isKeyholder": True, "hoursMax": 40, "currentPeriodHours": 0, "shiftsThisWeek": 0, "availability": [], "timeOff": [], "allowedPositionIds": []} ], "shifts": [ {"shiftId": 1, "minRoleId": 5, "requiresKeyholder": True, "date": "2026-02-16", "dayOfWeek": 1, "startTime": "09:00", "endTime": "17:00", "durationHours": 8.0, "isLocked": False} ], "lockedShifts": [] } x = { (1, 1): model.NewBoolVar("x_1_1"), (2, 1): model.NewBoolVar("x_2_1") } metadata = build_constraints(model, problem, x) assert any(item[0] == 1 and item[1] == 1 for item in metadata["c3_keyholder_qualification"]) assert not any(item[0] == 2 and item[1] == 1 for item in metadata["c3_keyholder_qualification"]) solver = cp_model.CpSolver() solver.parameters.log_search_progress = False status = solver.Solve(model) assert status in [cp_model.OPTIMAL, cp_model.FEASIBLE] assert solver.Value(x[(1, 1)]) == 0 # Not qualified # Employee 2 can be assigned (or not, but not blocked by role) def test_c3_legacy_role_requirement_remains_keyholder_fallback(): """Legacy minRoleId <= 3 continues to imply a keyholder requirement.""" model = cp_model.CpModel() problem = { "employees": [ {"userId": 1, "role": 4, "hoursMax": 40, "currentPeriodHours": 0, "shiftsThisWeek": 0, "availability": [], "timeOff": [], "allowedPositionIds": []}, # Not qualified for role 3 shifts {"userId": 2, "role": 3, "hoursMax": 40, "currentPeriodHours": 0, "shiftsThisWeek": 0, "availability": [], "timeOff": [], "allowedPositionIds": []} # Qualified ], "shifts": [ {"shiftId": 1, "minRoleId": 3, "date": "2026-02-16", "dayOfWeek": 1, "startTime": "07:00", "endTime": "15:00", "durationHours": 8.0, "isLocked": False} # Requires role <= 3 ], "lockedShifts": [] } x = { (1, 1): model.NewBoolVar("x_1_1"), (2, 1): model.NewBoolVar("x_2_1") } metadata = build_constraints(model, problem, x) assert any(item[0] == 1 and item[1] == 1 for item in metadata["c3_keyholder_qualification"]) assert not any(item[0] == 2 and item[1] == 1 for item in metadata["c3_keyholder_qualification"]) solver = cp_model.CpSolver() solver.parameters.log_search_progress = False status = solver.Solve(model) assert status in [cp_model.OPTIMAL, cp_model.FEASIBLE] assert solver.Value(x[(1, 1)]) == 0 # Role 4 can't work shifts requiring role <= 3 def test_c4_opening_shift_requires_keyholder_even_without_legacy_role(): """An opener flag must be enforced even when minRoleId is unrestricted.""" model = cp_model.CpModel() problem = { "employees": [ {"userId": 1, "role": 4, "isKeyholder": False, "hoursMax": 40, "currentPeriodHours": 0, "shiftsThisWeek": 0, "availability": [{"dayOfWeek": 1, "startTime": "00:00", "endTime": "23:59"}], "timeOff": [], "allowedPositionIds": []}, {"userId": 2, "role": 4, "isKeyholder": True, "hoursMax": 40, "currentPeriodHours": 0, "shiftsThisWeek": 0, "availability": [{"dayOfWeek": 1, "startTime": "00:00", "endTime": "23:59"}], "timeOff": [], "allowedPositionIds": []}, ], "shifts": [ {"shiftId": 1, "minRoleId": 5, "requiresKeyholder": False, "isOpener": True, "date": "2026-02-16", "dayOfWeek": 1, "startTime": "07:00", "endTime": "15:00", "durationHours": 8.0, "isLocked": False} ], "lockedShifts": [], } x = { (1, 1): model.NewBoolVar("x_1_1"), (2, 1): model.NewBoolVar("x_2_1"), } metadata = build_constraints(model, problem, x) model.Maximize(x[(1, 1)] + x[(2, 1)]) solver = cp_model.CpSolver() status = solver.Solve(model) assert status in [cp_model.OPTIMAL, cp_model.FEASIBLE] assert solver.Value(x[(1, 1)]) == 0 assert solver.Value(x[(2, 1)]) == 1 assert (1, 1, "not_keyholder") in metadata["c4_opening_closing"] def test_c5_availability_windows(): """Test C5: Shifts must fall within availability windows.""" model = cp_model.CpModel() problem = { "employees": [ { "userId": 1, "role": 4, "hoursMax": 40, "currentPeriodHours": 0, "shiftsThisWeek": 0, "availability": [ {"dayOfWeek": 1, "startTime": "12:00", "endTime": "20:00"} ], "timeOff": [], "allowedPositionIds": [] } ], "shifts": [ {"shiftId": 1, "minRoleId": 4, "date": "2026-02-16", "dayOfWeek": 1, "startTime": "09:00", "endTime": "17:00", "durationHours": 8.0, "isLocked": False} # Before availability ], "lockedShifts": [] } x = { (1, 1): model.NewBoolVar("x_1_1") } metadata = build_constraints(model, problem, x) # Should block assignment assert any(item[0] == 1 and item[1] == 1 for item in metadata["c5_availability"]) solver = cp_model.CpSolver() solver.parameters.log_search_progress = False status = solver.Solve(model) assert status in [cp_model.OPTIMAL, cp_model.FEASIBLE] assert solver.Value(x[(1, 1)]) == 0 def test_c6_time_off(): """Test C6: Time-off days are respected.""" model = cp_model.CpModel() problem = { "employees": [ { "userId": 1, "role": 4, "hoursMax": 40, "currentPeriodHours": 0, "shiftsThisWeek": 0, "availability": [{"dayOfWeek": 1, "startTime": "08:00", "endTime": "22:00"}], "timeOff": [{"date": "2026-02-16", "reason": "Vacation"}], "allowedPositionIds": [] } ], "shifts": [ {"shiftId": 1, "minRoleId": 4, "date": "2026-02-16", "dayOfWeek": 1, "startTime": "09:00", "endTime": "17:00", "durationHours": 8.0, "isLocked": False} ], "lockedShifts": [] } x = { (1, 1): model.NewBoolVar("x_1_1") } metadata = build_constraints(model, problem, x) assert any(item[0] == 1 and item[1] == 1 for item in metadata["c6_time_off"]) solver = cp_model.CpSolver() solver.parameters.log_search_progress = False status = solver.Solve(model) assert status in [cp_model.OPTIMAL, cp_model.FEASIBLE] assert solver.Value(x[(1, 1)]) == 0 def test_c6_time_off_blocks_overnight_shift_on_every_touched_date(): """Approved leave on the second calendar day blocks an overnight shift.""" model = cp_model.CpModel() problem = { "employees": [ { "userId": 1, "role": 4, "hoursMax": 40, "currentPeriodHours": 0, "shiftsThisWeek": 0, "availability": [ {"dayOfWeek": 1, "startTime": "00:00", "endTime": "23:59"}, {"dayOfWeek": 2, "startTime": "00:00", "endTime": "23:59"}, ], "timeOff": [{"date": "2026-02-17", "reason": "Vacation"}], "allowedPositionIds": [], } ], "shifts": [ { "shiftId": 1, "minRoleId": 4, "date": "2026-02-16", "dayOfWeek": 1, "startTime": "22:00", "endTime": "02:00", "durationHours": 4.0, "isLocked": False, } ], "lockedShifts": [], } x = {(1, 1): model.NewBoolVar("x_1_1")} metadata = build_constraints(model, problem, x) assert (1, 1, "2026-02-17") in metadata["c6_time_off"] solver = cp_model.CpSolver() assert solver.Solve(model) in [cp_model.OPTIMAL, cp_model.FEASIBLE] assert solver.Value(x[(1, 1)]) == 0 def test_c7_hours_max_hard_cap(): """Test C7: hoursMax is never exceeded.""" model = cp_model.CpModel() problem = { "employees": [ { "userId": 1, "role": 4, "hoursMax": 16.0, "currentPeriodHours": 8.0, "shiftsThisWeek": 1, "availability": [{"dayOfWeek": i, "startTime": "08:00", "endTime": "22:00"} for i in range(1, 8)], "timeOff": [], "allowedPositionIds": [] } ], "shifts": [ {"shiftId": 1, "minRoleId": 4, "date": "2026-02-17", "dayOfWeek": 2, "startTime": "09:00", "endTime": "17:00", "durationHours": 8.0, "isLocked": False}, {"shiftId": 2, "minRoleId": 4, "date": "2026-02-18", "dayOfWeek": 3, "startTime": "09:00", "endTime": "17:00", "durationHours": 8.0, "isLocked": False} ], "lockedShifts": [] } x = { (1, 1): model.NewBoolVar("x_1_1"), (1, 2): model.NewBoolVar("x_1_2") } metadata = build_constraints(model, problem, x) assert (1, 16.0) in metadata["c7_hours_max"] solver = cp_model.CpSolver() solver.parameters.log_search_progress = False status = solver.Solve(model) assert status in [cp_model.OPTIMAL, cp_model.FEASIBLE] # Employee can take at most 1 shift (8h current + 8h = 16h max) assigned = solver.Value(x[(1, 1)]) + solver.Value(x[(1, 2)]) assert assigned <= 1 def test_c8_one_shift_per_day(): """Test C8: One shift per employee per day.""" model = cp_model.CpModel() problem = { "employees": [ { "userId": 1, "role": 4, "hoursMax": 40, "currentPeriodHours": 0, "shiftsThisWeek": 0, "availability": [{"dayOfWeek": 1, "startTime": "08:00", "endTime": "22:00"}], "timeOff": [], "allowedPositionIds": [] } ], "shifts": [ {"shiftId": 1, "minRoleId": 4, "date": "2026-02-16", "dayOfWeek": 1, "startTime": "09:00", "endTime": "13:00", "durationHours": 4.0, "isLocked": False}, {"shiftId": 2, "minRoleId": 4, "date": "2026-02-16", "dayOfWeek": 1, "startTime": "14:00", "endTime": "18:00", "durationHours": 4.0, "isLocked": False} ], "lockedShifts": [] } x = { (1, 1): model.NewBoolVar("x_1_1"), (1, 2): model.NewBoolVar("x_1_2") } metadata = build_constraints(model, problem, x) assert any(item[0] == 1 and item[1] == "2026-02-16" for item in metadata["c8_one_per_day"]) solver = cp_model.CpSolver() solver.parameters.log_search_progress = False status = solver.Solve(model) assert status in [cp_model.OPTIMAL, cp_model.FEASIBLE] # Employee can take at most 1 shift per day assigned = solver.Value(x[(1, 1)]) + solver.Value(x[(1, 2)]) assert assigned <= 1 def test_c9_open_shift_cannot_overlap_locked_overnight_shift(): """An open next-day shift cannot overlap an employee's locked overnight shift.""" model = cp_model.CpModel() problem = { "employees": [ { "userId": 1, "role": 4, "hoursMax": 40, "currentPeriodHours": 8, "shiftsThisWeek": 1, "availability": [ {"dayOfWeek": 1, "startTime": "00:00", "endTime": "22:00"}, {"dayOfWeek": 7, "startTime": "20:00", "endTime": "23:59"}, ], "timeOff": [], "allowedPositionIds": [], } ], "shifts": [ { "shiftId": 2, "minRoleId": 4, "date": "2026-02-16", "dayOfWeek": 1, "startTime": "01:00", "endTime": "05:00", "durationHours": 4.0, "isLocked": False, } ], "lockedShifts": [ { "shiftId": 1, "employeeId": 1, "date": "2026-02-15", "dayOfWeek": 7, "startTime": "22:00", "endTime": "02:00", "durationHours": 4.0, } ], } x = {(1, 2): model.NewBoolVar("x_1_2")} metadata = build_constraints(model, problem, x) model.Maximize(x[(1, 2)]) solver = cp_model.CpSolver() status = solver.Solve(model) assert status in [cp_model.OPTIMAL, cp_model.FEASIBLE] assert solver.Value(x[(1, 2)]) == 0 assert (1, 2, 1) in metadata["c9_no_overlap"] def test_c9_canonical_locked_dst_interval_never_mixes_aware_and_naive_datetimes(): model = cp_model.CpModel() canonical = { "endDate": "2026-11-01", "startDateTimeUtc": "2026-11-01T06:30:00+00:00", "endDateTimeUtc": "2026-11-01T07:30:00+00:00", "storeTimezone": "America/Chicago", } problem = { "employees": [{"userId": 1, "role": 4, "hoursMax": 40, "currentPeriodHours": 1, "shiftsThisWeek": 1, "availability": [], "timeOff": [], "allowedPositionIds": []}], "shifts": [{"shiftId": 2, "minRoleId": 4, "date": "2026-11-01", "dayOfWeek": 7, "startTime": "01:45", "endTime": "02:15", "durationHours": 1.5, "isLocked": False, **canonical}], "lockedShifts": [{"shiftId": 1, "employeeId": 1, "date": "2026-11-01", "dayOfWeek": 7, "startTime": "01:30", "endTime": "01:30", "durationHours": 1.0, **canonical}], } x = {(1, 2): model.NewBoolVar("x_1_2_dst")} metadata = build_constraints(model, problem, x) assert (1, 2, 1) in metadata["c9_no_overlap"] def test_c10_max_five_shifts_per_week(): """Test C10: Max 5 shifts per employee per week.""" model = cp_model.CpModel() problem = { "employees": [ { "userId": 1, "role": 4, "hoursMax": 50, "currentPeriodHours": 0, "shiftsThisWeek": 2, # Already has 2 shifts "availability": [{"dayOfWeek": i, "startTime": "08:00", "endTime": "22:00"} for i in range(1, 8)], "timeOff": [], "allowedPositionIds": [] } ], "shifts": [ {"shiftId": i, "minRoleId": 4, "date": f"2026-02-{16+i}", "dayOfWeek": 1+i, "startTime": "09:00", "endTime": "15:00", "durationHours": 6.0, "isLocked": False} for i in range(5) # 5 more shifts available ], "lockedShifts": [] } x = { (1, i): model.NewBoolVar(f"x_1_{i}") for i in range(5) } metadata = build_constraints(model, problem, x) assert (1, 2) in metadata["c10_max_shifts_per_week"] solver = cp_model.CpSolver() solver.parameters.log_search_progress = False status = solver.Solve(model) assert status in [cp_model.OPTIMAL, cp_model.FEASIBLE] # Employee can take at most 3 more shifts (2 + 3 = 5 total) assigned = sum(solver.Value(x[(1, i)]) for i in range(5)) assert assigned <= 3 def test_c12_position_eligible_allowed(): """Test C12: Employee with positions [1,3] assigned to shift with positionId=1 -> ALLOWED.""" model = cp_model.CpModel() problem = { "employees": [ { "userId": 1, "role": 4, "hoursMax": 40, "currentPeriodHours": 0, "shiftsThisWeek": 0, "availability": [{"dayOfWeek": 1, "startTime": "08:00", "endTime": "22:00"}], "timeOff": [], "allowedPositionIds": [1, 3] } ], "shifts": [ {"shiftId": 1, "minRoleId": 4, "date": "2026-02-16", "dayOfWeek": 1, "startTime": "09:00", "endTime": "17:00", "durationHours": 8.0, "isLocked": False, "positionId": 1} ], "lockedShifts": [] } x = { (1, 1): model.NewBoolVar("x_1_1") } metadata = build_constraints(model, problem, x) # Employee 1 should NOT be blocked (they have position 1) assert not any(item[0] == 1 and item[1] == 1 for item in metadata["c12_position_eligibility"]) # Add dummy objective to make model solvable model.Maximize(0) solver = cp_model.CpSolver() solver.parameters.log_search_progress = False status = solver.Solve(model) assert status in [cp_model.OPTIMAL, cp_model.FEASIBLE] # Employee can be assigned (not blocked by position) def test_c12_position_ineligible_blocked(): """Test C12: Employee with positions [1,3] assigned to shift with positionId=2 -> BLOCKED.""" model = cp_model.CpModel() problem = { "employees": [ { "userId": 1, "role": 4, "hoursMax": 40, "currentPeriodHours": 0, "shiftsThisWeek": 0, "availability": [{"dayOfWeek": 1, "startTime": "08:00", "endTime": "22:00"}], "timeOff": [], "allowedPositionIds": [1, 3] } ], "shifts": [ {"shiftId": 1, "minRoleId": 4, "date": "2026-02-16", "dayOfWeek": 1, "startTime": "09:00", "endTime": "17:00", "durationHours": 8.0, "isLocked": False, "positionId": 2} ], "lockedShifts": [] } x = { (1, 1): model.NewBoolVar("x_1_1") } metadata = build_constraints(model, problem, x) # Employee 1 should be blocked (they don't have position 2) assert any(item[0] == 1 and item[1] == 1 for item in metadata["c12_position_eligibility"]) solver = cp_model.CpSolver() solver.parameters.log_search_progress = False status = solver.Solve(model) assert status in [cp_model.OPTIMAL, cp_model.FEASIBLE] assert solver.Value(x[(1, 1)]) == 0 # Not qualified def test_c12_no_positions_blocked(): """Test C12: Employee with positions [] assigned to shift with positionId=1 -> BLOCKED.""" model = cp_model.CpModel() problem = { "employees": [ { "userId": 1, "role": 4, "hoursMax": 40, "currentPeriodHours": 0, "shiftsThisWeek": 0, "availability": [{"dayOfWeek": 1, "startTime": "08:00", "endTime": "22:00"}], "timeOff": [], "allowedPositionIds": [] } ], "shifts": [ {"shiftId": 1, "minRoleId": 4, "date": "2026-02-16", "dayOfWeek": 1, "startTime": "09:00", "endTime": "17:00", "durationHours": 8.0, "isLocked": False, "positionId": 1} ], "lockedShifts": [] } x = { (1, 1): model.NewBoolVar("x_1_1") } metadata = build_constraints(model, problem, x) # Employee 1 should be blocked (they have no positions) assert any(item[0] == 1 and item[1] == 1 for item in metadata["c12_position_eligibility"]) solver = cp_model.CpSolver() solver.parameters.log_search_progress = False status = solver.Solve(model) assert status in [cp_model.OPTIMAL, cp_model.FEASIBLE] assert solver.Value(x[(1, 1)]) == 0 # Not qualified def test_c12_null_position_any_employee(): """Test C12: Employee with positions [1,3] assigned to shift with positionId=None -> ALLOWED.""" model = cp_model.CpModel() problem = { "employees": [ { "userId": 1, "role": 4, "hoursMax": 40, "currentPeriodHours": 0, "shiftsThisWeek": 0, "availability": [{"dayOfWeek": 1, "startTime": "08:00", "endTime": "22:00"}], "timeOff": [], "allowedPositionIds": [1, 3] } ], "shifts": [ {"shiftId": 1, "minRoleId": 4, "date": "2026-02-16", "dayOfWeek": 1, "startTime": "09:00", "endTime": "17:00", "durationHours": 8.0, "isLocked": False, "positionId": None} ], "lockedShifts": [] } x = { (1, 1): model.NewBoolVar("x_1_1") } metadata = build_constraints(model, problem, x) # Employee 1 should NOT be blocked (shift has no position requirement) assert not any(item[0] == 1 and item[1] == 1 for item in metadata["c12_position_eligibility"]) # Add dummy objective to make model solvable model.Maximize(0) solver = cp_model.CpSolver() solver.parameters.log_search_progress = False status = solver.Solve(model) assert status in [cp_model.OPTIMAL, cp_model.FEASIBLE] # Employee can be assigned (no position requirement) def test_c12_null_position_no_positions_allowed(): """Test C12: Employee with positions [] assigned to shift with positionId=None -> ALLOWED.""" model = cp_model.CpModel() problem = { "employees": [ { "userId": 1, "role": 4, "hoursMax": 40, "currentPeriodHours": 0, "shiftsThisWeek": 0, "availability": [{"dayOfWeek": 1, "startTime": "08:00", "endTime": "22:00"}], "timeOff": [], "allowedPositionIds": [] } ], "shifts": [ {"shiftId": 1, "minRoleId": 4, "date": "2026-02-16", "dayOfWeek": 1, "startTime": "09:00", "endTime": "17:00", "durationHours": 8.0, "isLocked": False, "positionId": None} ], "lockedShifts": [] } x = { (1, 1): model.NewBoolVar("x_1_1") } metadata = build_constraints(model, problem, x) # Employee 1 should NOT be blocked (shift has no position requirement) assert not any(item[0] == 1 and item[1] == 1 for item in metadata["c12_position_eligibility"]) # Add dummy objective to make model solvable model.Maximize(0) solver = cp_model.CpSolver() solver.parameters.log_search_progress = False status = solver.Solve(model) assert status in [cp_model.OPTIMAL, cp_model.FEASIBLE] # Employee with no positions can still work shifts with no position requirement