TAMC Scheduling CP-SAT Constraint Model

Reference for the Google OR-Tools CP-SAT solver implementation for faculty and call scheduling.

Architecture Note

Excel is the communication layer; CP-SAT is the engine. Excel provides the configuration (who's available, what rotations, constraints), and the solver optimizes within those constraints. The code has "more nitty gritty" logic than what Excel shows.

Build Order

The solver should process in this order:

Preload phase - Lock FMIT, leave, conferences, holidays
Call assignment - Distribute with equity tracking
Resident schedules - Apply rotation patterns
Faculty C/AT - Fill remaining slots
Admin time - GME/DFM for remaining faculty slots

Architecture Overview

1. PRELOAD phase: Lock non-negotiable assignments
2. SOLVE phase: CP-SAT optimizes remaining slots
3. VALIDATE phase: Verify all constraints satisfied

Decision Variables

Faculty Schedule Variables

python

# For each faculty f, day d, slot s (AM/PM):
faculty_assigned[f, d, s] = model.NewBoolVar(f'fac_{f}_{d}_{s}')

# Assignment type (mutually exclusive)
is_clinic[f, d, s] = model.NewBoolVar(f'clinic_{f}_{d}_{s}')
is_admin[f, d, s] = model.NewBoolVar(f'admin_{f}_{d}_{s}')  # GME or DFM
is_sm[f, d, s] = model.NewBoolVar(f'sm_{f}_{d}_{s}')  # Tagawa only

Call Variables

python

# For each faculty f, night n:
on_call[f, n] = model.NewBoolVar(f'call_{f}_{n}')

# Post-call blocking
post_call_am[f, d] = model.NewBoolVar(f'pcat_{f}_{d}')
post_call_pm[f, d] = model.NewBoolVar(f'do_{f}_{d}')

Auxiliary Variables

python

# Weekly clinic counts (for cap enforcement)
weekly_clinic_count[f, w] = model.NewIntVar(0, MAX_CLINIC, f'wk_clinic_{f}_{w}')

# Sunday call counts (for equity)
sunday_call_count[f] = model.NewIntVar(0, 4, f'sun_call_{f}')
weekday_call_count[f] = model.NewIntVar(0, 20, f'wd_call_{f}')

Hard Constraints

0. ACGME Supervision Ratios (HIGHEST PRIORITY - CANNOT VIOLATE)

This constraint takes precedence over ALL others. ACGME minimum supervision.

python

# AT demand per resident type
AT_DEMAND = {
    'PGY1': 0.5,   # Interns need more supervision
    'PGY2': 0.25,
    'PGY3': 0.25,
    'PROC': 1.0,   # Procedures = dedicated AT
    'VAS': 1.0,    # Vascular = dedicated AT
}

# Codes that count as AT coverage
AT_COVERAGE_CODES = {'C', 'PCAT', 'CV'}  # Clinic, Post-Call Admin, Virtual

for day in DAYS:
    for slot in [AM, PM]:
        # Calculate demand
        demand = 0
        for resident in RESIDENTS:
            if resident_in_clinic(resident, day, slot):
                demand += AT_DEMAND[resident.pgy_level]
            if resident_in_proc(resident, day, slot):
                demand += AT_DEMAND['PROC']

        # Round up - can't have half a faculty
        demand_rounded = math.ceil(demand)

        # Count coverage
        coverage = sum(
            1 for f in FACULTY
            if faculty_assignment[f, day, slot] in AT_COVERAGE_CODES
        )

        # HARD CONSTRAINT: coverage >= demand
        model.Add(coverage >= demand_rounded)

This constraint is NON-NEGOTIABLE. If it cannot be satisfied, the schedule is INVALID.

Balancing AT: Two-Way Optimization

AT compliance is an equation: Coverage >= Demand

The solver can satisfy this by:

Increasing coverage - Assign more faculty C/PCAT
Decreasing demand - Reduce resident clinic load

python

# Resident clinic assignment is a DECISION VARIABLE, not fixed
resident_in_clinic[r, day, slot] = model.NewBoolVar(...)

# Can move residents C -> ADM to reduce demand
# PROC -> C reduces demand by 0.75 (1.0 -> 0.25)

# Soft constraint: prefer residents in clinic (educational value)
# But AT compliance is HARD - will pull residents if needed
model.Minimize(
    ... +
    10 * sum(not_in_clinic_penalty[r, d, s] for r, d, s in ...)
)

Priority order:

ACGME AT compliance (HARD - cannot violate)
Faculty weekly caps (soft - can exceed if needed)
Resident clinic time (soft - educational preference)
GME/admin preferences (soft - fill remaining)

1. Weekly Clinic Caps (C only, NOT AT)

python

# IMPORTANT: Caps apply to C (Clinic), NOT AT (Attending)
# C = Faculty seeing OWN patients → has MIN and MAX
# AT = Faculty supervising residents → NO cap (unlimited)

for faculty in FACULTY:
    for week in WEEKS:
        c_count = sum(is_clinic[faculty, d, s]
                      for d in week_days(week)
                      for s in [AM, PM])

        # MAX constraint (hard)
        model.Add(c_count <= faculty.max_clinic_per_week)

        # MIN constraint (hard - WARN if cannot meet)
        model.Add(c_count >= faculty.min_clinic_per_week)

Faculty C caps (MIN and MAX per week):

Faculty	MIN C/week	MAX C/week	AT?
Kinkennon	2	4	Unlimited
LaBounty	2	4	Unlimited
McRae	2	4	Unlimited
Montgomery	2	2	Unlimited
Lamoureux	2	2	Unlimited
McGuire	1	1	Unlimited
Tagawa	0 (SM only)	0	Can do AT when not on SM
Bevis, Dahl, Chu, Napierala, Van Brunt, Colgan	0	0	N/A

If MIN cannot be met: WARN - flag for PD review (conflicts with other constraints)

2. FMIT Blocking

python

for faculty in FACULTY:
    for day in faculty.fmit_days:
        # Cannot take clinic during FMIT
        model.Add(is_clinic[faculty, day, AM] == 0)
        model.Add(is_clinic[faculty, day, PM] == 0)

        # Cannot take Sun-Thu call during FMIT week
        if day.is_weekday or day.is_sunday:
            model.Add(on_call[faculty, day] == 0)

3. Post-Call PCAT/DO

python

for faculty in FACULTY:
    for night in NIGHTS:
        if not is_fmit_call(faculty, night):
            next_day = night + 1
            # If on call, next day must be PCAT/DO
            model.Add(post_call_am[faculty, next_day] >= on_call[faculty, night])
            model.Add(post_call_pm[faculty, next_day] >= on_call[faculty, night])

            # PCAT/DO blocks clinic
            model.Add(is_clinic[faculty, next_day, AM] + post_call_am[faculty, next_day] <= 1)
            model.Add(is_clinic[faculty, next_day, PM] + post_call_pm[faculty, next_day] <= 1)

4. FMIT Call Coverage

python

# FMIT week structure: Friday (start) -> Thursday (end)
# FMIT faculty covers Friday and Saturday call (first 2 nights of FMIT)
# PC = Friday after FMIT ends (day off)

for faculty in FACULTY:
    for fmit_start in faculty.fmit_fridays:  # Friday is FMIT start day
        # FMIT faculty must cover Fri/Sat call
        model.Add(on_call[faculty, fmit_start] == 1)  # Friday
        model.Add(on_call[faculty, fmit_start + 1] == 1)  # Saturday

        # Cannot be on call Sun-Thu during FMIT (on inpatient service)
        for day_offset in range(2, 7):  # Sun=+2, Mon=+3, ..., Thu=+6
            model.Add(on_call[faculty, fmit_start + day_offset] == 0)

        # PC on Friday after (day 7 from FMIT start)
        pc_friday = fmit_start + 7
        model.Add(assignment[faculty, pc_friday, AM] == 'PC')
        model.Add(assignment[faculty, pc_friday, PM] == 'PC')

        # Cannot be on call Saturday after FMIT (recovery, day 8)
        model.Add(on_call[faculty, fmit_start + 8] == 0)

5. No Back-to-Back Call (HARD CONSTRAINT)

python

# Faculty cannot have call on consecutive days - need gap days
# Example: Call on Mar 15 AND Mar 17 (1 day gap) = INVALID
# Example: Call on Mar 15 AND Mar 19 (3 day gap) = VALID

for faculty in FACULTY:
    for night in NIGHTS[:-1]:
        # No consecutive nights
        model.Add(on_call[faculty, night] + on_call[faculty, night + 1] <= 1)

    # Extended gap rule: prefer at least 2 days between calls
    for night in NIGHTS[:-2]:
        # Soft constraint: penalize 1-day gaps
        one_day_gap = model.NewBoolVar(f'gap1_{faculty}_{night}')
        model.Add(on_call[faculty, night] + on_call[faculty, night + 2] <= 1 + one_day_gap)
        # Add penalty to objective

6. One Faculty Per Call Night

python

for night in NIGHTS:
    model.Add(sum(on_call[f, night] for f in CALL_ELIGIBLE) == 1)

7. SM Requires Tagawa

python

TAGAWA = get_faculty('Tagawa')
for day in DAYS:
    for slot in [AM, PM]:
        # If any resident has SM, Tagawa must have SM
        resident_sm = sum(resident_is_sm[r, day, slot] for r in SM_RESIDENTS)
        model.Add(is_sm[TAGAWA, day, slot] >= resident_sm)

        # If Tagawa blocked, no resident can have SM
        if tagawa_blocked(day, slot):
            for r in SM_RESIDENTS:
                model.Add(resident_is_sm[r, day, slot] == 0)

8. Physical Clinic Capacity (Max 6 Clinical Workers)

python

# Staffing constraint: max 6 people doing clinical work per half-day
# AT (supervision) does NOT count - they precept, don't generate patients

CLINICAL_CODES = {'C', 'CV', 'PR', 'VAS'}  # Codes that generate patient load

for day in DAYS:
    for slot in [AM, PM]:
        # Count residents doing clinical work
        resident_clinical = sum(
            1 for r in RESIDENTS
            if resident_assignment[r, day, slot] in CLINICAL_CODES
        )

        # Count faculty doing clinic (not AT supervision)
        faculty_clinical = sum(
            is_clinic[f, day, slot] for f in FACULTY
        )

        # HARD CONSTRAINT: total clinical workers <= 6
        model.Add(resident_clinical + faculty_clinical <= 6)

Note: This constraint is due to staffing limitations (MAs, check-in, etc.). More than 6 providers generating patients overwhelms support staff.

9. Intern Continuity Clinic (Wednesday AM)

python

# HARD CONSTRAINT: PGY-1 interns have Continuity Clinic on Wednesday mornings
# This is protected time for panel patients - cannot be moved

WED_AM_COLS = [18, 32, 46, 60]  # Wednesday AM columns in Block Template2

for intern in PGY1_RESIDENTS:
    for wed_am_col in WED_AM_COLS:
        rotation = get_rotation(intern, wed_am_col)

        # Exceptions - these rotations don't have Wed AM clinic
        if rotation in ['NF', 'Peds NF', 'Hilo', 'Kapiolani L and D', 'OB', 'Transitional']:
            continue  # Use rotation-specific code

        # All other PGY-1 rotations → Continuity Clinic
        model.Add(resident_assignment[intern, wed_am_col] == CLINIC_CODE)

Intern clinic codes by experience level:

Block Range	Code	Appointment Length
Block 1-6	C60	60 minutes (new intern)
Block 7-13	C40	40 minutes (experienced)
ER rotation	C30	30 minutes

Senior residents (PGY-2/3) do NOT have this constraint - their Wed AM varies by rotation.

10. Night Float Timing

python

# Night Float Structure:
# - Starts: Thursday
# - Ends: Wednesday (following week)
# - Post-call: Thursday after (inter-block day)
# - C-N code: Thursday PM for oncoming NF (preserves continuity)

for resident in NF_RESIDENTS:
    nf_start_day = resident.nf_start_date  # Must be Thursday
    assert nf_start_day.weekday() == THURSDAY

    # Thursday PM: C-N (oncoming clinic)
    model.Add(resident_assignment[resident, nf_start_day, PM] == 'C-N')

    # Friday through Wednesday: NF pattern (OFF AM, NF PM)
    for day_offset in range(1, 7):  # Fri=1, Sat=2, ..., Wed=6
        day = nf_start_day + day_offset
        model.Add(resident_assignment[resident, day, AM] == 'OFF')
        model.Add(resident_assignment[resident, day, PM] == 'NF')

    # Thursday after: POST-CALL (inter-block)
    post_call_day = nf_start_day + 7
    model.Add(resident_assignment[resident, post_call_day, AM] == 'OFF')
    model.Add(resident_assignment[resident, post_call_day, PM] == 'OFF')

11. Last Wednesday of Block

python

# Final Wednesday has special rules - NO morning clinic
# AM: Lecture (LEC)
# PM: Advising (ADV)

LAST_WED_COL = 60  # Apr 8 AM in Block 10

for resident in RESIDENTS:
    # AM = LEC (protected)
    model.Add(resident_assignment[resident, LAST_WED_COL] == 'LEC')
    # PM = ADV
    model.Add(resident_assignment[resident, LAST_WED_COL + 1] == 'ADV')

12. IM Rotation Last Wednesday Exception

python

# IM residents get Tuesday PM clinic instead of final Wednesday
# Preserves 4 weeks of continuity (otherwise only 3)

for resident in IM_RESIDENTS:
    last_tue_pm = get_last_tuesday_pm(resident.rotation_end)

    # Move clinic from Wed to Tue PM
    model.Add(resident_assignment[resident, last_tue_pm] == 'C')

    # Last Wed follows standard Last Wednesday rules (LEC/ADV)

13. Resident Clinic Caps

python

# Clinic caps by PGY level (per week)
CLINIC_CAPS = {
    'PGY1': (1, 2),   # min=1, max=2 (ideally 1)
    'PGY2': (2, 3),   # min=2, max=3
    'PGY3': (3, 4),   # min=3, max=4
}

# R2 on Sports Med: max 3 (hard cap)
for resident in R2_ON_SM:
    for week in WEEKS:
        clinic_count = sum(
            resident_in_clinic[resident, d, s]
            for d in week_days(week)
            for s in [AM, PM]
        )
        model.Add(clinic_count <= 3)

# Flex time requirements
FLEX_REQUIRED = {
    'PGY1': 2,  # 2 half-days FLX
    'PGY2': 1,  # 1 half-day FLX
    'PGY3': 1,  # At least 1 half-day FLX (FMC)
}

14. Kapiolani L&D Schedule (Intern)

python

# Kapiolani is off-site L&D for interns
# Special schedule: Mon PM-Tue OFF, Wed AM clinic, Thu-Sun KAP

for intern in KAP_INTERNS:
    for week in WEEKS:
        mon = get_monday(week)

        # Mon PM through Tue: OFF (travel/recovery)
        model.Add(resident_assignment[intern, mon, PM] == 'OFF')
        model.Add(resident_assignment[intern, mon + 1, AM] == 'OFF')  # Tue AM
        model.Add(resident_assignment[intern, mon + 1, PM] == 'OFF')  # Tue PM

        # Wed AM: Continuity clinic (NOT KAP)
        model.Add(resident_assignment[intern, mon + 2, AM] == 'C')

        # Thu-Sun: KAP (4 shifts)
        for day_offset in range(3, 7):  # Thu=3, Fri=4, Sat=5, Sun=6
            day = mon + day_offset
            model.Add(resident_assignment[intern, day, AM] == 'KAP')
            model.Add(resident_assignment[intern, day, PM] == 'KAP')

15. L&D Night Float (R2)

python

# R2 L&D nights have FRIDAY morning clinic (not Wednesday!)

for r2 in LDNF_RESIDENTS:
    for week in WEEKS:
        fri = get_friday(week)

        # Friday AM: Clinic
        model.Add(resident_assignment[r2, fri, AM] == 'C')
        # Friday PM: OFF
        model.Add(resident_assignment[r2, fri, PM] == 'OFF')

        # Mon-Thu: OFF AM, LDNF PM
        for day in [get_monday(week), get_tuesday(week), get_wednesday(week), get_thursday(week)]:
            model.Add(resident_assignment[r2, day, AM] == 'OFF')
            model.Add(resident_assignment[r2, day, PM] == 'LDNF')

16. Houseless Clinic (HLC) and CLC

python

# HLC: Monday PM for R2/R3 only
# One resident per slot, every Monday

MONDAY_PM_COLS = [15, 29, 43, 57]  # Monday PM columns

for mon_pm in MONDAY_PM_COLS:
    # Exactly one R2/R3 assigned HLC
    hlc_assigned = [
        resident_assignment[r, mon_pm] == 'HLC'
        for r in R2_R3_RESIDENTS
    ]
    model.Add(sum(hlc_assigned) == 1)

# CLC: Thursday PM, weeks 2 and 4 (NOT back-to-back)
# 2nd Thursday PM and 4th Thursday PM
WEEK2_THU_PM = 21  # Mar 19 PM
WEEK4_THU_PM = 49  # Apr 2 PM

for thu_pm in [WEEK2_THU_PM, WEEK4_THU_PM]:
    # Residents scheduled for CLC
    for r in CLC_ELIGIBLE_RESIDENTS:
        model.Add(resident_assignment[r, thu_pm] == 'CLC')

17. Faculty Weekly Target Distribution

python

# Target: 10 half-days/week per faculty
# 3 C + 2-3 GME + 3-4 AT + 1 PCAT/DO (if call)

WEEKLY_TARGETS = {
    'C': (2, 4),      # MIN 2, MAX 4 (faculty-specific caps override)
    'GME': (2, 3),    # Admin time
    'AT': (3, 4),     # Supervision (soft target, no cap)
    'PCAT_DO': (0, 1) # Only if took call
}

# Soft constraint: prefer full-day C (AM+PM same day)
for faculty in FACULTY:
    for day in WEEKDAYS:
        am_clinic = is_clinic[faculty, day, AM]
        pm_clinic = is_clinic[faculty, day, PM]

        # Penalize split days (only one half has C)
        split_penalty = model.NewBoolVar(f'split_{faculty}_{day}')
        # XOR detection: exactly one is true
        model.Add(am_clinic + pm_clinic == 1).OnlyEnforceIf(split_penalty)

# Add to objective with small weight
model.Minimize(... + 1 * sum(split_penalties))

18. Preloaded Slots Locked

python

for (faculty, day, slot, code) in PRELOADED:
    if code == 'FMIT':
        model.Add(is_fmit[faculty, day, slot] == 1)
    elif code == 'LV':
        model.Add(is_leave[faculty, day, slot] == 1)
    elif code == 'PC':
        model.Add(is_post_fmit[faculty, day, slot] == 1)
    # ... etc

Soft Constraints / Objectives

1. Call Equity (Primary)

python

# Minimize variance in call counts
max_call = model.NewIntVar(0, 30, 'max_call')
min_call = model.NewIntVar(0, 30, 'min_call')

for faculty in CALL_ELIGIBLE:
    total_call = sum(on_call[faculty, n] for n in NIGHTS)
    model.Add(max_call >= total_call)
    model.Add(min_call <= total_call)

# Objective: minimize spread
model.Minimize(max_call - min_call)

2. Sunday Equity (Secondary)

python

# Track Sunday calls separately
max_sunday = model.NewIntVar(0, 4, 'max_sun')
min_sunday = model.NewIntVar(0, 4, 'min_sun')

for faculty in CALL_ELIGIBLE:
    sun_calls = sum(on_call[faculty, n] for n in SUNDAY_NIGHTS)
    model.Add(max_sunday >= sun_calls)
    model.Add(min_sunday <= sun_calls)

# Add to objective with weight
model.Minimize(
    10 * (max_call - min_call) +  # Weekday equity
    5 * (max_sunday - min_sunday)  # Sunday equity
)

3. Tagawa SM Target (3-4/week)

python

TAGAWA = get_faculty('Tagawa')
for week in WEEKS:
    sm_count = sum(is_sm[TAGAWA, d, AM] for d in week_days(week))

    # Soft constraint: penalize deviation from 3-4
    under_target = model.NewIntVar(0, 3, f'sm_under_{week}')
    over_target = model.NewIntVar(0, 4, f'sm_over_{week}')

    model.Add(sm_count + under_target >= 3)
    model.Add(sm_count - over_target <= 4)

# Add penalty to objective
model.Minimize(
    ... +
    2 * sum(under_target[w] + over_target[w] for w in WEEKS)
)

4. AT Coverage Balance

python

# Aim for consistent clinic coverage across days
for day in WEEKDAYS:
    clinic_count = sum(is_clinic[f, day, s] for f in FACULTY for s in [AM, PM])

    # Penalize deviation from target
    target = AT_DEMAND[day]
    deviation = model.NewIntVar(0, 10, f'at_dev_{day}')
    model.Add(clinic_count - target <= deviation)
    model.Add(target - clinic_count <= deviation)

model.Minimize(... + sum(deviation[d] for d in WEEKDAYS))

5. Full-Day Clinic Preference

python

# Faculty prefer full-day clinic (C AM + C PM) over split days
# This also increases chances of getting full-day GME

for faculty in FACULTY:
    for day in WEEKDAYS:
        # Penalty for having clinic in only one half
        has_am_clinic = is_clinic[faculty, day, AM]
        has_pm_clinic = is_clinic[faculty, day, PM]

        # XOR: penalty when exactly one is true
        split_day = model.NewBoolVar(f'split_{faculty}_{day}')
        model.Add(has_am_clinic + has_pm_clinic - 2 * split_day <= 1)
        model.Add(has_am_clinic + has_pm_clinic - 2 * split_day >= 0)

# Add to objective with small weight (soft preference)
model.Minimize(
    ... +
    1 * sum(split_day[f, d] for f in FACULTY for d in WEEKDAYS)
)

Special Faculty Rules (Encoded as Constraints)

Chu (Week-on/Week-off FMIT)

python

CHU = get_faculty('Chu')
for week in CHU.off_weeks:
    # No Sun-Thu call during off weeks
    for night in week_nights(week):
        if night.day_of_week in [SUN, MON, TUE, WED, THU]:
            model.Add(on_call[CHU, night] == 0)

# Not immediately available after FMIT
for fmit_end in CHU.fmit_end_dates:
    for day in range(fmit_end + 1, fmit_end + 3):  # Skip 2 days
        model.Add(on_call[CHU, day] == 0)

Bevis (Post-FMIT Availability)

python

BEVIS = get_faculty('Bevis')
# Available starting next week after FMIT
fmit_end_week = get_week(BEVIS.fmit_end_date)
for night in NIGHTS:
    if get_week(night) <= fmit_end_week:
        model.Add(on_call[BEVIS, night] == 0)

LaBounty (Pre-FMIT Restriction)

python

LABOUNTY = get_faculty('LaBounty')
fmit_start_week = get_week(LABOUNTY.fmit_start_date)
# No call in week immediately before FMIT
for night in NIGHTS:
    if get_week(night) == fmit_start_week - 1:
        model.Add(on_call[LABOUNTY, night] == 0)

Preload Phase

Before solving, lock these codes:

python

PRELOAD_CODES = {
    'FMIT',   # FM Inpatient Team
    'LV',     # Leave
    'W',      # Weekend (in schedule, not call)
    'PC',     # Post-FMIT Friday
    'LEC',    # Lecture (Wed PM)
    'SIM',    # Simulation
    'HAFP',   # Hawaii AFP conference
    'USAFP',  # USAFP conference
    'BLS',    # BLS training
    'DEP',    # Deployed
    'PI',     # Process Improvement
    'MM',     # M&M conference
    'HOL',    # Holiday
    'TDY',    # Temporary Duty (residents)
}

Solver Configuration

python

from ortools.sat.python import cp_model

model = cp_model.CpModel()
solver = cp_model.CpSolver()

# Time limit
solver.parameters.max_time_in_seconds = 60.0

# Parallelism
solver.parameters.num_search_workers = 8

# Solution callback for progress
class SolutionCallback(cp_model.CpSolverSolutionCallback):
    def on_solution_callback(self):
        print(f'Solution {self.solution_count}: obj={self.ObjectiveValue()}')

status = solver.Solve(model, SolutionCallback())

if status == cp_model.OPTIMAL:
    print('Optimal solution found')
elif status == cp_model.FEASIBLE:
    print('Feasible solution found (may not be optimal)')
else:
    print('No solution found')

Validation Checklist

After solving, verify:

ACGME supervision ratios satisfied (HIGHEST PRIORITY)
No faculty exceeds weekly clinic cap (MIN and MAX)
All call nights have exactly one faculty assigned
PCAT/DO applied for all non-FMIT call
No back-to-back call for any faculty (need gap days)
SM slots have both Tagawa and resident
FMIT faculty not on Sun-Thu call
Sunday call distributed (max 1-2 per faculty)
Physical clinic capacity ≤6 per slot
PGY-1 interns have Continuity Clinic on Wednesday AM
Preloaded codes not overwritten
Night float: starts Thursday, ends Wednesday, C-N on oncoming day
Last Wednesday: AM=LEC, PM=ADV (no morning clinic)
IM rotation: Tuesday PM clinic instead of final Wednesday
R2 clinic caps: max 3 per week
HLC assigned Monday PM (one R2/R3)
CLC on 2nd and 4th Thursday PM
Flex time requirements met by PGY level
Kapiolani interns: Wed AM clinic, Mon PM-Tue OFF, Thu-Sun KAP
L&D NF (R2): Friday AM clinic (not Wednesday!)
Faculty full-day C preferred (AM+PM same day)

Integration with Excel

python

from openpyxl import load_workbook

def apply_solution_to_excel(solver, variables, workbook_path):
    wb = load_workbook(workbook_path)
    sheet = wb['Block Template2']

    for (faculty, day, slot), var in variables.items():
        if solver.Value(var):
            row = FACULTY_ROWS[faculty]
            col = day_slot_to_col(day, slot)

            if solver.Value(is_clinic[faculty, day, slot]):
                sheet.cell(row=row, column=col).value = 'C'
            elif solver.Value(is_sm[faculty, day, slot]):
                sheet.cell(row=row, column=col).value = 'SM'
            # ... etc

    # Apply call assignments
    for (faculty, night), var in on_call.items():
        if solver.Value(var):
            col = night_to_col(night)
            sheet.cell(row=4, column=col).value = faculty.upper()

    wb.save(workbook_path)

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SKILL.md

TAMC Scheduling CP-SAT Constraint Model

Architecture Note

Build Order

Architecture Overview

Decision Variables

Faculty Schedule Variables

Call Variables

Auxiliary Variables

Hard Constraints

0. ACGME Supervision Ratios (HIGHEST PRIORITY - CANNOT VIOLATE)

Balancing AT: Two-Way Optimization

1. Weekly Clinic Caps (C only, NOT AT)

2. FMIT Blocking

3. Post-Call PCAT/DO

4. FMIT Call Coverage

5. No Back-to-Back Call (HARD CONSTRAINT)

6. One Faculty Per Call Night

7. SM Requires Tagawa

8. Physical Clinic Capacity (Max 6 Clinical Workers)

9. Intern Continuity Clinic (Wednesday AM)

10. Night Float Timing

11. Last Wednesday of Block

12. IM Rotation Last Wednesday Exception

13. Resident Clinic Caps

14. Kapiolani L&D Schedule (Intern)

15. L&D Night Float (R2)

16. Houseless Clinic (HLC) and CLC

17. Faculty Weekly Target Distribution

18. Preloaded Slots Locked

Soft Constraints / Objectives

1. Call Equity (Primary)

2. Sunday Equity (Secondary)

3. Tagawa SM Target (3-4/week)

4. AT Coverage Balance

5. Full-Day Clinic Preference

Special Faculty Rules (Encoded as Constraints)

Chu (Week-on/Week-off FMIT)

Bevis (Post-FMIT Availability)

LaBounty (Pre-FMIT Restriction)

Preload Phase

Solver Configuration

Validation Checklist

Integration with Excel