src/models/optimizer_real.py

# ============================================================
# SD_roster_real - Fixed Team Production Planning (Option A)
# - Uses config-style variable names from src/config/optimization_config.py
# - Team per product (simultaneous): UNICEF Fixed term / Humanizer
# - Line types via numeric ids: 6=long, 7=short
# - One product per (line, shift, day)
# - Weekly demand (across DATE_SPAN)
# ============================================================

from ortools.linear_solver import pywraplp
from math import ceil
from src.config.constants import ShiftType, LineType, KitLevel

# ---- config import (프로젝트 경로에 맞춰 조정) ----
from src.config.optimization_config import (
    DATE_SPAN,                # [1..N]
    get_product_list,         # DYNAMIC: list of products (e.g., ['A','B',...])
    get_employee_type_list,   # DYNAMIC: e.g., ['UNICEF Fixed term','Humanizer']
    get_active_shift_list,    # DYNAMIC: e.g., [1,2,3]
    get_line_list,            # DYNAMIC: e.g., [6,7]  (line type ids)
    get_line_cnt_per_type,    # DYNAMIC: {6: count_of_long_lines, 7: count_of_short_lines}
    get_demand_dictionary,    # DYNAMIC: {product: total_units_over_period}
    get_cost_list_per_emp_shift,  # DYNAMIC: {emp_type: {shift: cost_per_hour}}
    get_max_employee_per_type_on_day,    # DYNAMIC: {emp_type: {t: headcount}}
    MAX_HOUR_PER_PERSON_PER_DAY,     # e.g., 14
    get_max_hour_per_shift_per_person,   # DYNAMIC: {1: hours, 2: hours, 3: hours}
    get_per_product_speed,       # DYNAMIC: {6: cap_units_per_hour, 7: cap_units_per_hour}
    get_max_parallel_workers,        # DYNAMIC: {6: max_workers, 7: max_workers}
    FIXED_STAFF_CONSTRAINT_MODE,     # not used in fixed-team model (동시 투입이라 무의미)
    get_team_requirements,    # DYNAMIC: {emp_type: {product: team_size}} from Kits_Calculation.csv
    get_payment_mode_config,         # DYNAMIC: {shift: 'bulk'/'partial'} payment mode configuration
    KIT_LINE_MATCH_DICT,
    EVENING_SHIFT_MODE,
    EVENING_SHIFT_DEMAND_THRESHOLD,
    # Hierarchy variables for production ordering
    KIT_LEVELS,                      # {kit_id: level} where 0=prepack, 1=subkit, 2=master
    KIT_DEPENDENCIES,                # {kit_id: [dependency_list]}
    PRODUCTION_PRIORITY_ORDER,       # [kit_ids] sorted by production priority
    # Fixed staffing requirements
    get_fixed_min_unicef_per_day,    # DYNAMIC: Minimum UNICEF employees required per day
)



# 2) kit_line_match
KIT_LINE_MATCH_DICT
print("KIT_LINE_MATCH_DICT",KIT_LINE_MATCH_DICT)

# 3) If specific product is not produced on specific date, set it to 0
# ACTIVE will be built dynamically in solve function based on fresh PRODUCT_LIST
# Example: ACTIVE[2]['C'] = 0  # Disable product C on day 2


def build_lines():
    """List of line instances.
       L elements are (line_type_id, idx) tuples. e.g., (6,1), (6,2), (7,1), ...
    """
    L = []
    LINE_LIST = get_line_list()  # Dynamic call
    LINE_CNT_PER_TYPE = get_line_cnt_per_type()  # Dynamic call
    
    for lt in LINE_LIST:  # lt: 6 or 7
        cnt = int(LINE_CNT_PER_TYPE.get(lt, 0))
        for i in range(1, cnt + 1):
            L.append((lt, i))
    return L

L=build_lines()
print("L",L)
PER_PRODUCT_SPEED = get_per_product_speed()  # Dynamic call
print("PER_PRODUCT_SPEED",PER_PRODUCT_SPEED)

def sort_products_by_hierarchy(product_list):
    """
    Sort products by hierarchy levels and dependencies using topological sorting.
    Returns products in optimal production order: prepacks → subkits → masters
    Dependencies within the same level are properly ordered.
    """
    from collections import defaultdict, deque
    
    # Filter products that are in our production list and have hierarchy data
    products_with_hierarchy = [p for p in product_list if p in KIT_LEVELS]
    products_without_hierarchy = [p for p in product_list if p not in KIT_LEVELS]
    
    if products_without_hierarchy:
        print(f"[HIERARCHY] Products without hierarchy data: {products_without_hierarchy}")
    
    # Build dependency graph for products in our list
    graph = defaultdict(list)  # product -> [dependents]
    in_degree = defaultdict(int)  # product -> number of dependencies
    
    # Initialize all products
    for product in products_with_hierarchy:
        in_degree[product] = 0
    
    # Build edges based on actual dependencies
    # KIT_DEPENDENCIES = {product: [dependencies]} - "What does THIS product need?"
    # graph = {dependency: [products]} - "What depends on THIS dependency?"
    # 
    # Example transformation:
    # KIT_DEPENDENCIES = {'subkit_A': ['prepack_1'], 'master_B': ['subkit_A']}
    # After building: graph = {'prepack_1': ['subkit_A'], 'subkit_A': ['master_B']}
    # This means: prepack_1 is needed by subkit_A, subkit_A is needed by master_B
    #
    # Example:
    # 1. product='subkit_A', deps=['prepack_1']
    #    → graph['prepack_1'].append('subkit_A')
    #    → graph = {'prepack_1': ['subkit_A']}
    # 2. product='master_B', deps=['subkit_A'] 
    #    → graph['subkit_A'].append('master_B')
    #    → graph = {'prepack_1': ['subkit_A'], 'subkit_A': ['master_B']}
    
    
    for product in products_with_hierarchy:
        deps = KIT_DEPENDENCIES.get(product, []) #dependencies = products that has to be packed first
        for dep in deps:
            if dep in products_with_hierarchy:  # Only if dependency is in our production list
                # REVERSE THE RELATIONSHIP: 
                # KIT_DEPENDENCIES says: "product needs dep"
                # graph says: "dep is needed by product"
                graph[dep].append(product)  # dep -> product (reverse the relationship!)
                in_degree[product] += 1
    
    # Topological sort with hierarchy level priority
    sorted_products = []
    #queue = able to remove from both sides
    queue = deque()
    
    # Start with products that have no dependencies
    for product in products_with_hierarchy:
        if in_degree[product] == 0:
            queue.append(product)
    
    while queue:
        current = queue.popleft()
        sorted_products.append(current)
        
        # Process dependents - sort by hierarchy level first
        for dependent in sorted(graph[current], key=lambda p: (KIT_LEVELS.get(p, 999), p)):
            in_degree[dependent] -= 1 #decrement the in_degree of the dependent
            if in_degree[dependent] == 0: #if the in_degree of the dependent is 0, add it to the queue so that it can be processed
                queue.append(dependent)
    
    # Check for cycles (shouldn't happen with proper hierarchy)
    if len(sorted_products) != len(products_with_hierarchy):
        remaining = [p for p in products_with_hierarchy if p not in sorted_products]
        print(f"[HIERARCHY] WARNING: Potential circular dependencies detected in: {remaining}")
        # Add remaining products sorted by level as fallback
        remaining_sorted = sorted(remaining, key=lambda p: (KIT_LEVELS.get(p, 999), p))
        sorted_products.extend(remaining_sorted)
    
    # Add products without hierarchy information at the end
    sorted_products.extend(sorted(products_without_hierarchy))
    
    print(f"[HIERARCHY] Dependency-aware production order: {len(sorted_products)} products")
    for i, p in enumerate(sorted_products[:10]):  # Show first 10
        level = KIT_LEVELS.get(p, "unknown")
        level_name = KitLevel.get_name(level)
        deps = KIT_DEPENDENCIES.get(p, [])
        deps_in_list = [d for d in deps if d in products_with_hierarchy]
        print(f"  {i+1}. {p} (level {level}={level_name}, deps: {len(deps_in_list)})")
        if deps_in_list:
            print(f"      Dependencies: {deps_in_list}")
    
    if len(sorted_products) > 10:
        print(f"  ... and {len(sorted_products) - 10} more products")
    
    return sorted_products

# Removed get_dependency_timing_weight function - no longer needed
# Dependency ordering is now handled by topological sorting in sort_products_by_hierarchy()

def solve_fixed_team_weekly():
    # *** CRITICAL: Load fresh data to reflect current Streamlit configs ***
    print("\n" + "="*60)
    print("🔄 LOADING FRESH DATA FOR OPTIMIZATION")
    print("="*60)
    
    # Get fresh product list and demand data
    PRODUCT_LIST = get_product_list()
    DEMAND_DICTIONARY = get_demand_dictionary()
    TEAM_REQ_PER_PRODUCT = get_team_requirements(PRODUCT_LIST)
    
    print(f"📦 LOADED PRODUCTS: {len(PRODUCT_LIST)} products")
    print(f"📈 LOADED DEMAND: {sum(DEMAND_DICTIONARY.values())} total units")
    print(f"👥 LOADED TEAM REQUIREMENTS: {len(TEAM_REQ_PER_PRODUCT)} employee types")
    
    # Build ACTIVE schedule for fresh product list
    ACTIVE = {t: {p: 1 for p in PRODUCT_LIST} for t in DATE_SPAN}
    
    # --- Sets ---
    D = list(DATE_SPAN)
    # print("D",D)
    S = sorted(list(get_active_shift_list()))  # Dynamic call
    E = list(get_employee_type_list())  # Dynamic call - e.g., ['UNICEF Fixed term','Humanizer']
    print("E",E)
    # *** HIERARCHY SORTING: Sort products by production priority ***
    print("\n" + "="*60)
    print("🔗 APPLYING HIERARCHY-BASED PRODUCTION ORDERING")
    print("="*60)
    P_sorted = sort_products_by_hierarchy(list(PRODUCT_LIST))
    P = P_sorted  # Use sorted product list
    
    L = build_lines()
    print("Lines",L)

    # --- Short aliases for parameters ---
    Hmax_s = dict(get_max_hour_per_shift_per_person())  # Dynamic call - per-shift hours
    Hmax_daily = MAX_HOUR_PER_PERSON_PER_DAY          # {6:cap, 7:cap}
    max_workers_line = dict(get_max_parallel_workers())  # Dynamic call - per line type
    N_day = get_max_employee_per_type_on_day()        # Dynamic call - {emp_type:{t:headcount}}
    cost = get_cost_list_per_emp_shift()              # Dynamic call - {emp_type:{shift:cost}}
    d_week = DEMAND_DICTIONARY                        # {product: demand over period}
    print("d_week",d_week)
    # --- Feasibility quick checks ---
    
    # 1) If team size is greater than max_workers_line, block the product-line type combination
    for p in P:
        req_total = sum(TEAM_REQ_PER_PRODUCT[e][p] for e in E)
        lt = KIT_LINE_MATCH_DICT.get(p, 6)  # Default to long line (6) if not found
        if p not in KIT_LINE_MATCH_DICT:
            print(f"[WARN] Product {p}: No line type mapping found, defaulting to long line (6)")
        if req_total > max_workers_line.get(lt, 1e9):
            print(f"[WARN] Product {p}: team size {req_total} > MAX_PARALLEL_WORKERS[{lt}] "
                  f"= {max_workers_line.get(lt)}. Blocked.")

    # 2) Check if demand can be met without evening shift (only if in normal mode)
    if EVENING_SHIFT_MODE == "normal":
        total_demand = sum(DEMAND_DICTIONARY.get(p, 0) for p in P)
        
        # Calculate maximum capacity with regular + overtime shifts only
        regular_overtime_shifts = [s for s in S if s in ShiftType.REGULAR_AND_OVERTIME]
        max_capacity = 0
        
        for p in P:
            if p in PER_PRODUCT_SPEED:
                product_speed = PER_PRODUCT_SPEED[p]  # units per hour
                # Calculate max hours available for this product across all lines and shifts
                max_hours_per_product = 0
                for ell in L:
                    for s in regular_overtime_shifts:
                        for t in D:
                            max_hours_per_product += Hmax_s[s]
                
                max_capacity += product_speed * max_hours_per_product
        
        capacity_ratio = max_capacity / total_demand if total_demand > 0 else float('inf')
        
        print(f"[CAPACITY CHECK] Total demand: {total_demand}")
        print(f"[CAPACITY CHECK] Max capacity (Regular + Overtime): {max_capacity:.1f}")
        print(f"[CAPACITY CHECK] Capacity ratio: {capacity_ratio:.2f}")
        
        if capacity_ratio < EVENING_SHIFT_DEMAND_THRESHOLD:
            print(f"\n🚨 [ALERT] DEMAND TOO HIGH!")
            print(f"   Current capacity can only meet {capacity_ratio*100:.1f}% of demand")
            print(f"   Threshold: {EVENING_SHIFT_DEMAND_THRESHOLD*100:.1f}%")
            print(f"   RECOMMENDATION: Change EVENING_SHIFT_MODE to 'activate_evening' to enable evening shift")
            print(f"   This will add shift 3 to increase capacity\n")


    # --- Solver ---
    solver = pywraplp.Solver.CreateSolver('CBC')
    if not solver:
        raise RuntimeError("CBC solver not found.")
    INF = solver.infinity()

    # --- Variables ---
    # Z[p,ell,s,t] ∈ {0,1}: 1 if product p runs on (line,shift,day)
    Z, T, U = {}, {}, {}  # T: run hours, U: production units
    for p in P:
        for ell in L:     # ell = (line_type_id, idx)
            for s in S:
                for t in D:
                    Z[p, ell, s, t] = solver.BoolVar(f"Z_{p}_{ell[0]}_{ell[1]}_s{s}_d{t}")
                    T[p, ell, s, t] = solver.NumVar(0, Hmax_s[s], f"T_{p}_{ell[0]}_{ell[1]}_s{s}_d{t}")
                    U[p, ell, s, t] = solver.NumVar(0, INF,       f"U_{p}_{ell[0]}_{ell[1]}_s{s}_d{t}")
    
    # Idle employee variables: IDLE[e,s,t] = number of idle employees of type e in shift s on day t
    IDLE = {}
    for e in E:
        for s in S:
            for t in D:
                max_idle = N_day[e][t]  # Can't have more idle employees than available
                IDLE[e, s, t] = solver.IntVar(0, max_idle, f"IDLE_{e}_s{s}_d{t}")

    # Note: Binary variables for bulk payment are now created inline in the cost calculation

    # --- Objective: total labor cost with payment modes + hierarchy timing penalty ---
    PAYMENT_MODE_CONFIG = get_payment_mode_config()  # Dynamic call
    print(f"Payment mode configuration: {PAYMENT_MODE_CONFIG}")
    
    # Build cost terms based on payment mode
    cost_terms = []
    
    for e in E:
        for s in S:
            payment_mode = PAYMENT_MODE_CONFIG.get(s, "partial")  # Default to partial if not specified
            
            if payment_mode == "partial":
                # Partial payment: pay for actual hours worked
                for p in P:
                    for ell in L:
                        for t in D:
                            cost_terms.append(cost[e][s] * TEAM_REQ_PER_PRODUCT[e][p] * T[p, ell, s, t])
            
            elif payment_mode == "bulk":
                # Bulk payment: if employees work ANY hours in a shift, pay them for FULL shift hours
                # BUT only pay the employees who actually work, not all employees of that type
                for p in P:
                    for ell in L:
                        for t in D:
                            # Calculate actual employees working: TEAM_REQ_PER_PRODUCT[e][p] employees work T[p,ell,s,t] hours
                            # For bulk payment: if T[p,ell,s,t] > 0, pay TEAM_REQ_PER_PRODUCT[e][p] employees for full shift
                            # We need a binary variable for each (e,s,p,ell,t) combination
                            # But we can use the existing logic: if T > 0, then those specific employees get bulk pay
                            
                            # Create binary variable for this specific work assignment
                            work_binary = solver.BoolVar(f"work_{e}_s{s}_{p}_{ell[0]}{ell[1]}_d{t}")
                            
                            # Link work_binary to T[p,ell,s,t]: work_binary = 1 if T > 0
                            solver.Add(T[p, ell, s, t] <= Hmax_s[s] * work_binary)
                            solver.Add(work_binary * 0.001 <= T[p, ell, s, t])
                            
                            # Cost: pay the specific working employees for full shift hours
                            cost_terms.append(cost[e][s] * Hmax_s[s] * TEAM_REQ_PER_PRODUCT[e][p] * work_binary)
    
    # Add idle employee costs (idle employees are paid for full shift hours)
    for e in E:
        for s in S:
            for t in D:
                cost_terms.append(cost[e][s] * Hmax_s[s] * IDLE[e, s, t])
    
    total_cost = solver.Sum(cost_terms)
    
    # Objective: minimize total cost only
    # Dependency ordering is handled by topological sorting and hard constraints
    solver.Minimize(total_cost)

    # --- Constraints ---

    # 1) Weekly demand - must meet exactly (no over/under production)
    for p in P:
        total_production = solver.Sum(U[p, ell, s, t] for ell in L for s in S for t in D)
        demand = d_week.get(p, 0)
        
        # Must produce at least the demand
        solver.Add(total_production >= demand)
        
        # Must not produce more than the demand (prevent overproduction)
        solver.Add(total_production <= demand)

    # 2) One product per (line,shift,day) + time gating
    for ell in L:
        for s in S:
            for t in D:
                solver.Add(solver.Sum(Z[p, ell, s, t] for p in P) <= 1)
                for p in P:
                    solver.Add(T[p, ell, s, t] <= Hmax_s[s] * Z[p, ell, s, t])

    # 3) Product-line type compatibility + (optional) activity by day
    for p in P:
        req_lt = KIT_LINE_MATCH_DICT.get(p, LineType.LONG_LINE)  # Default to long line if not found
        req_total = sum(TEAM_REQ_PER_PRODUCT[e][p] for e in E)
        for ell in L:
            allowed = (ell[0] == req_lt) and (req_total <= max_workers_line.get(ell[0], 1e9))
            for s in S:
                for t in D:
                    if ACTIVE[t][p] == 0 or not allowed:
                        solver.Add(Z[p, ell, s, t] == 0)
                        solver.Add(T[p, ell, s, t] == 0)
                        solver.Add(U[p, ell, s, t] == 0)

    # 4) Line throughput: U ≤ product_speed * T
    for p in P:
        for ell in L:
            for s in S:
                for t in D:
                    # Get product speed (same speed regardless of line type)
                    if p in PER_PRODUCT_SPEED:
                        # Convert kit per day to kit per hour (assuming 7.5 hour workday)
                        speed = PER_PRODUCT_SPEED[p] 
                        # Upper bound: units cannot exceed capacity
                        solver.Add(
                            U[p, ell, s, t] <= speed * T[p, ell, s, t]
                        )
                        # Lower bound: if working, must produce (prevent phantom work)
                        solver.Add(
                            U[p, ell, s, t] >= speed * T[p, ell, s, t]
                        )
                    else:
                        # Default speed if not found
                        default_speed = 800 / 7.5  # units per hour
                        print(f"Warning: No speed data for product {p}, using default {default_speed:.1f} per hour")
                        # Upper bound: units cannot exceed capacity
                        solver.Add(
                            U[p, ell, s, t] <= default_speed * T[p, ell, s, t]
                        )
                        # Lower bound: if working, must produce (prevent phantom work)
                        solver.Add(
                            U[p, ell, s, t] >= default_speed * T[p, ell, s, t]
                        )

    # 5) Per-shift staffing capacity by type: idle employees ≤ available headcount
    for e in E:
        for s in S:
            for t in D:
                # Idle employees cannot exceed available headcount
                # (Active employees are constrained by the working hours constraint below)
                solver.Add(IDLE[e, s, t] <= N_day[e][t])
                
                # Working hours constraint: active employees cannot exceed shift hour capacity
                solver.Add(
                    solver.Sum(TEAM_REQ_PER_PRODUCT[e][p] * T[p, ell, s, t] for p in P for ell in L)
                    <= Hmax_s[s] * N_day[e][t]
                )

    # 6) Per-day staffing capacity by type: sum(req*hours across shifts) ≤ 14h * headcount
    for e in E:
        for t in D:
            solver.Add(
                solver.Sum(TEAM_REQ_PER_PRODUCT[e][p] * T[p, ell, s, t] for s in S for p in P for ell in L)
                <= MAX_HOUR_PER_PERSON_PER_DAY * N_day[e][t]
            )

    # 7) Shift ordering constraints (only apply if shifts are available)
    # Evening shift after regular shift
    if ShiftType.EVENING in S and ShiftType.REGULAR in S:  # Only if both shifts are available
        for e in E:
            for t in D:
                solver.Add(
                    solver.Sum(TEAM_REQ_PER_PRODUCT[e][p] * T[p, ell, ShiftType.EVENING, t] for p in P for ell in L)
                    <=
                    solver.Sum(TEAM_REQ_PER_PRODUCT[e][p] * T[p, ell, ShiftType.REGULAR, t] for p in P for ell in L)
                )
    
    # Overtime should only be used when regular shift is at capacity
    if ShiftType.OVERTIME in S and ShiftType.REGULAR in S:  # Only if both shifts are available
        print("\n[OVERTIME] Adding constraints to ensure overtime only when regular shift is insufficient...")
        
        for e in E:
            for t in D:
                # Get available regular capacity for this employee type and day
                regular_capacity = N_day[e][t]
                
                # Total regular shift usage for this employee type and day
                regular_usage = solver.Sum(
                    TEAM_REQ_PER_PRODUCT[e][p] * T[p, ell, ShiftType.REGULAR, t] 
                    for p in P for ell in L
                )
                
                # Total overtime usage for this employee type and day
                overtime_usage = solver.Sum(
                    TEAM_REQ_PER_PRODUCT[e][p] * T[p, ell, ShiftType.OVERTIME, t] 
                    for p in P for ell in L
                )
                
                # Create binary variable: 1 if using overtime, 0 otherwise
                using_overtime = solver.IntVar(0, 1, f'using_overtime_{e}_{t}')
                
                # If using overtime, regular capacity must be utilized significantly
                # Regular usage must be at least 90% of capacity to allow overtime
                min_regular_for_overtime = int(0.9 * regular_capacity)
                
                # Constraint 1: Can only use overtime if regular usage is high
                solver.Add(regular_usage >= min_regular_for_overtime * using_overtime)
                
                # Constraint 2: If any overtime is used, set the binary variable
                solver.Add(overtime_usage <= regular_capacity * using_overtime)
                
        overtime_constraints_added = len(E) * len(D) * 2  # 2 constraints per employee type per day
        print(f"[OVERTIME] Added {overtime_constraints_added} constraints ensuring overtime only when regular shifts are at 90%+ capacity")
    
    # 7.5) Bulk payment linking constraints are now handled inline in the cost calculation
    
    # 7.6) *** FIXED MINIMUM UNICEF EMPLOYEES CONSTRAINT ***
    # Ensure minimum UNICEF fixed-term staff are present every working day
    FIXED_MIN_UNICEF_PER_DAY = get_fixed_min_unicef_per_day()  # Dynamic call
    if 'UNICEF Fixed term' in E and FIXED_MIN_UNICEF_PER_DAY > 0:
        print(f"\n[FIXED STAFFING] Adding constraint for minimum {FIXED_MIN_UNICEF_PER_DAY} UNICEF employees per day...")
        
        unicef_constraints_added = 0
        for t in D:
            # Method 1: Simple approach - ensure minimum UNICEF employees are scheduled
            # regardless of whether they're working or idle
            # Sum up all possible UNICEF work assignments + idle UNICEF employees
            
            # Count all UNICEF work hours across all products, lines, and shifts
            all_unicef_hours = solver.Sum(
                TEAM_REQ_PER_PRODUCT.get('UNICEF Fixed term', {}).get(p, 0) * T[p, ell, s, t]
                for p in P
                for ell in L 
                for s in S
            )
            
            # Count idle UNICEF employees across all shifts
            idle_unicef_employees = solver.Sum(
                IDLE['UNICEF Fixed term', s, t] for s in S
            )
            
            # Constraint: total hours (work + idle*14) must meet minimum staffing
            # This ensures at least FIXED_MIN_UNICEF_PER_DAY employees are present
            solver.Add(all_unicef_hours + idle_unicef_employees * MAX_HOUR_PER_PERSON_PER_DAY >= FIXED_MIN_UNICEF_PER_DAY * MAX_HOUR_PER_PERSON_PER_DAY)
            
            # Additional constraint: ensure idle employees are properly linked to total headcount
            # This prevents the solver from avoiding the minimum by setting everyone to zero
            total_unicef_hours_needed_for_production = solver.Sum(
                TEAM_REQ_PER_PRODUCT.get('UNICEF Fixed term', {}).get(p, 0) * T[p, ell, s, t]
                for p in P for ell in L for s in S
            )
            
            # Simpler approach: just ensure the basic constraint is strong enough
            # The main constraint above should be sufficient: all_unicef_hours + idle*14 >= min*14
            # This already forces idle employees when production is insufficient
            unicef_constraints_added += 1
            
        print(f"[FIXED STAFFING] Added {unicef_constraints_added} constraints ensuring >= {FIXED_MIN_UNICEF_PER_DAY} UNICEF employees per day")
    
    # 8) *** HIERARCHY DEPENDENCY CONSTRAINTS ***
    # For subkits with prepack dependencies: dependencies should be produced before or same time
    print("\n[HIERARCHY] Adding dependency constraints...")
    dependency_constraints_added = 0
    
    for p in P:
        dependencies = KIT_DEPENDENCIES.get(p, [])
        if dependencies:
            # Get the level of the current product
            p_level = KIT_LEVELS.get(p, 2)
            
            for dep in dependencies:
                if dep in P:  # Only if dependency is also in production list
                    # Calculate "completion time" for each product (sum of all production times)
                    p_completion = solver.Sum(
                        t * T[p, ell, s, t] for ell in L for s in S for t in D
                    )
                    dep_completion = solver.Sum(
                        t * T[dep, ell, s, t] for ell in L for s in S for t in D
                    )
                    
                    # Dependency should complete before or at the same time
                    solver.Add(dep_completion <= p_completion)
                    dependency_constraints_added += 1
                    
                    print(f"  Added constraint: {dep} (dependency) <= {p} (level {p_level})")
    
    print(f"[HIERARCHY] Added {dependency_constraints_added} dependency constraints")

    # --- Solve ---
    status = solver.Solve()
    if status != pywraplp.Solver.OPTIMAL:
        status_names = {pywraplp.Solver.INFEASIBLE: "INFEASIBLE", pywraplp.Solver.UNBOUNDED: "UNBOUNDED"}
        print(f"No optimal solution. Status: {status} ({status_names.get(status, 'UNKNOWN')})")
        # Debug hint:
        # solver.EnableOutput()
        # solver.ExportModelAsLpFile("model.lp")
        return None

    # --- Report ---
    result = {}
    result['objective'] = solver.Objective().Value()

    # Weekly production
    prod_week = {p: sum(U[p, ell, s, t].solution_value() for ell in L for s in S for t in D) for p in P}
    result['weekly_production'] = prod_week

    # Which product ran on which line/shift/day
    schedule = []
    for t in D:
        for ell in L:
            for s in S:
                chosen = [p for p in P if Z[p, ell, s, t].solution_value() > 0.5]
                if chosen:
                    p = chosen[0]
                    schedule.append({
                        'day': t,
                        'line_type_id': ell[0],
                        'line_idx': ell[1],
                        'shift': s,
                        'product': p,
                        'run_hours': T[p, ell, s, t].solution_value(),
                        'units': U[p, ell, s, t].solution_value(),
                    })
    result['run_schedule'] = schedule

    # Implied headcount by type/shift/day (ceil)
    headcount = []
    for e in E:
        for s in S:
            for t in D:
                used_ph = sum(TEAM_REQ_PER_PRODUCT[e][p] * T[p, ell, s, t].solution_value() for p in P for ell in L)
                need = ceil(used_ph / (Hmax_s[s] + 1e-9))
                headcount.append({'emp_type': e, 'shift': s, 'day': t,
                                  'needed': need, 'available': N_day[e][t]})
    result['headcount_per_shift'] = headcount

    # Total person-hours by type/day (≤ 14h * headcount)
    ph_by_day = []
    for e in E:
        for t in D:
            used = sum(TEAM_REQ_PER_PRODUCT[e][p] * T[p, ell, s, t].solution_value() for s in S for p in P for ell in L)
            ph_by_day.append({'emp_type': e, 'day': t,
                              'used_person_hours': used,
                              'cap_person_hours': Hmax_daily * N_day[e][t]})
    result['person_hours_by_day'] = ph_by_day

    # Idle employee data for visualization
    idle_employees = []
    for e in E:
        for s in S:
            for t in D:
                idle_count = IDLE[e, s, t].solution_value()
                if idle_count > 0:  # Only include non-zero idle counts
                    idle_employees.append({
                        'emp_type': e,
                        'shift': s,
                        'day': t,
                        'idle_count': idle_count
                    })
    result['idle_employees'] = idle_employees

    # Pretty print
    print("Objective (min cost):", result['objective'])
    print("\n--- Weekly production by product ---")
    for p, u in prod_week.items():
        print(f"{p}: {u:.1f} / demand {d_week.get(p,0)}")

    print("\n--- Schedule (line, shift, day) ---")
    for row in schedule:
        shift_name = ShiftType.get_name(row['shift'])
        line_name = LineType.get_name(row['line_type_id'])
        print(f"D{row['day']} {line_name}-{row['line_idx']} {shift_name}: "
              f"{row['product']}  T={row['run_hours']:.2f}h  U={row['units']:.1f}")

    print("\n--- Implied headcount need (per type/shift/day) ---")
    for row in headcount:
        shift_name = ShiftType.get_name(row['shift'])
        print(f"{row['emp_type']}, {shift_name}, D{row['day']}: "
              f"need={row['needed']} (avail {row['available']})")

    print("\n--- Total person-hours by type/day ---")
    for row in ph_by_day:
        print(f"{row['emp_type']}, D{row['day']}: used={row['used_person_hours']:.1f} "
              f"(cap {row['cap_person_hours']})")

    # Report idle employees
    print("\n--- Idle employees (per type/shift/day) ---")
    idle_found = False
    for e in E:
        for s in S:
            for t in D:
                idle_count = IDLE[e, s, t].solution_value()
                if idle_count > 0:
                    shift_name = ShiftType.get_name(s)
                    print(f"{e}, {shift_name}, D{t}: idle={idle_count}")
                    idle_found = True
    if not idle_found:
        print("No idle employees scheduled")

    return result


if __name__ == "__main__":
    solve_fixed_team_weekly()