import pandas as pd
import numpy as np
import pulp as pl
import matplotlib.pyplot as plt
import gradio as gr
from itertools import product
import io
import base64
import tempfile
import os
from datetime import datetime
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go
import seaborn as sns
from ortools.sat.python import cp_model
import random
from deap import base, creator, tools, algorithms
import time
def am_pm(hour):
"""Converts 24-hour time to AM/PM format."""
period = "AM"
if hour >= 12:
period = "PM"
if hour > 12:
hour -= 12
elif hour == 0:
hour = 12 # Midnight
return f"{int(hour):02d}:00 {period}"
def show_dataframe(csv_path):
"""Reads a CSV file and returns a Pandas DataFrame."""
try:
df = pd.read_csv(csv_path)
return df
except Exception as e:
return f"Error loading CSV: {e}"
def optimize_staffing(
csv_file,
beds_per_staff,
max_hours_per_staff, # This will now be interpreted as hours per 28-day period
hours_per_cycle,
rest_days_per_week,
clinic_start,
clinic_end,
overlap_time,
max_start_time_change,
exact_staff_count=None,
overtime_percent=100
):
# Load data
try:
if isinstance(csv_file, str):
# Handle the case when a filepath is passed directly
data = pd.read_csv(csv_file)
elif hasattr(csv_file, 'name'):
# Handle the case when file object is uploaded through Gradio
data = pd.read_csv(csv_file.name)
elif csv_file is None:
# Create a default DataFrame for testing
days = range(1, 21) # 20 days
data = pd.DataFrame({'day': days})
# Add 4 cycles per day (5-hour cycles)
for cycle in range(1, 5):
data[f'cycle{cycle}'] = 3 # Default 3 beds per cycle
else:
# Try direct CSV reading
data = pd.read_csv(io.StringIO(csv_file.decode('utf-8')))
except Exception as e:
print(f"Error loading CSV file: {e}")
# Create a default DataFrame
days = range(1, 21) # 20 days
data = pd.DataFrame({'day': days})
# Add 4 cycles per day (5-hour cycles)
for cycle in range(1, 5):
data[f'cycle{cycle}'] = 3 # Default 3 beds per cycle
print("Created default schedule with 20 days and 4 cycles per day")
# Rename the index column if necessary
if data.columns[0] not in ['day', 'Day', 'DAY']:
data = data.rename(columns={data.columns[0]: 'day'})
# Fill missing values
for col in data.columns:
if col.startswith('cycle'):
data[col] = data[col].fillna(0)
# Calculate clinic hours
if clinic_end < clinic_start:
clinic_hours = 24 - clinic_start + clinic_end
else:
clinic_hours = clinic_end - clinic_start
# Get number of days in the dataset
num_days = len(data)
# Parameters
BEDS_PER_STAFF = float(beds_per_staff)
STANDARD_PERIOD_DAYS = 30 # Standard 4-week period
# Scale MAX_HOURS_PER_STAFF based on the ratio of actual days to standard period
BASE_MAX_HOURS = float(max_hours_per_staff) # This is for a 28-day period
MAX_HOURS_PER_STAFF = BASE_MAX_HOURS * (num_days / STANDARD_PERIOD_DAYS)
# Log the adjustment for transparency
original_results = f"Input max hours per staff (28-day period): {BASE_MAX_HOURS}\n"
original_results += f"Adjusted max hours for {num_days}-day period: {MAX_HOURS_PER_STAFF:.1f}\n\n"
HOURS_PER_CYCLE = float(hours_per_cycle)
REST_DAYS_PER_WEEK = int(rest_days_per_week)
SHIFT_TYPES = [5, 10] # Modified to match 5-hour cycles
OVERLAP_TIME = float(overlap_time)
CLINIC_START = int(clinic_start)
CLINIC_END = int(clinic_end)
CLINIC_HOURS = clinic_hours
MAX_START_TIME_CHANGE = int(max_start_time_change)
OVERTIME_ALLOWED = 1 + (overtime_percent / 100) # Convert percentage to multiplier
# Calculate staff needed per cycle (beds/BEDS_PER_STAFF, rounded up)
for col in data.columns:
if col.startswith('cycle') and not col.endswith('_staff'):
data[f'{col}_staff'] = np.ceil(data[col] / BEDS_PER_STAFF)
# Get cycle names and number of cycles
cycle_cols = [col for col in data.columns if col.startswith('cycle') and not col.endswith('_staff')]
num_cycles = len(cycle_cols)
# Define cycle times
cycle_times = {}
for i, cycle in enumerate(cycle_cols):
cycle_start = (CLINIC_START + i * HOURS_PER_CYCLE) % 24
cycle_end = (CLINIC_START + (i + 1) * HOURS_PER_CYCLE) % 24
cycle_times[cycle] = (cycle_start, cycle_end)
# Get staff requirements
max_staff_needed = max([data[f'{cycle}_staff'].max() for cycle in cycle_cols])
# Define possible shift start times to align with cycles
shift_start_times = []
for i in range(num_cycles):
cycle_start = (CLINIC_START + i * HOURS_PER_CYCLE) % 24
shift_start_times.append(cycle_start)
# Generate all possible shifts
possible_shifts = []
for duration in SHIFT_TYPES:
for start_time in shift_start_times:
end_time = (start_time + duration) % 24
# Create a shift with its coverage of cycles
shift = {
'id': f"{int(duration)}hr_{int(start_time):02d}",
'start': start_time,
'end': end_time,
'duration': duration,
'cycles_covered': set()
}
# Determine which cycles this shift covers
for cycle, (cycle_start, cycle_end) in cycle_times.items():
# Handle overnight cycles
if cycle_end < cycle_start: # overnight cycle
if start_time >= cycle_start or end_time <= cycle_end or (start_time < end_time and end_time > cycle_start):
shift['cycles_covered'].add(cycle)
else: # normal cycle
shift_end = end_time if end_time > start_time else end_time + 24
cycle_end_adj = cycle_end if cycle_end > cycle_start else cycle_end + 24
# Check for overlap
if not (shift_end <= cycle_start or start_time >= cycle_end_adj):
shift['cycles_covered'].add(cycle)
if shift['cycles_covered']: # Only add shifts that cover at least one cycle
possible_shifts.append(shift)
# Estimate minimum number of staff needed - more precise calculation
total_staff_hours = 0
for _, row in data.iterrows():
for cycle in cycle_cols:
total_staff_hours += row[f'{cycle}_staff'] * HOURS_PER_CYCLE
# Calculate theoretical minimum staff with perfect utilization
theoretical_min_staff = np.ceil(total_staff_hours / MAX_HOURS_PER_STAFF)
# Add a small buffer for rest day constraints
min_staff_estimate = np.ceil(theoretical_min_staff * (7 / (7 - REST_DAYS_PER_WEEK)))
# Use exact_staff_count if provided, otherwise estimate
if exact_staff_count is not None and exact_staff_count > 0:
# When exact staff count is provided, only create that many staff in the model
estimated_staff = exact_staff_count
num_staff_to_create = exact_staff_count # Only create exactly this many staff
else:
# Add some buffer for constraints like rest days and shift changes
estimated_staff = max(min_staff_estimate, max_staff_needed + 1)
num_staff_to_create = int(estimated_staff) # Create the estimated number of staff
def optimize_schedule(num_staff, time_limit=600):
try:
# Create a binary linear programming model
model = pl.LpProblem("Staff_Scheduling", pl.LpMinimize)
# Decision variables
x = pl.LpVariable.dicts("shift",
[(s, d, shift['id']) for s in range(1, num_staff+1)
for d in range(1, num_days+1)
for shift in possible_shifts],
cat='Binary')
# Variables for timing violations - this is our highest priority
timing_violations = pl.LpVariable.dicts("timing_violation",
[(s, d) for s in range(1, num_staff+1)
for d in range(2, num_days+1)],
lowBound=0)
# Objective function - ONLY care about timing violations
model += pl.lpSum(1000000 * timing_violations[s,d] for s in range(1, num_staff+1)
for d in range(2, num_days+1))
# HARD CONSTRAINT: Timing Consistency
for s in range(1, num_staff+1):
for d in range(2, num_days+1):
# Get previous day's shift
prev_day_shifts = pl.lpSum(x[(s, d-1, shift['id'])] for shift in possible_shifts)
current_day_shifts = pl.lpSum(x[(s, d, shift['id'])] for shift in possible_shifts)
# If both days have shifts (consecutive working days)
model += prev_day_shifts + current_day_shifts <= 2 + timing_violations[s,d]
# If working consecutive days, times must match
for shift1 in possible_shifts:
for shift2 in possible_shifts:
if shift1['start'] != shift2['start']:
model += x[(s, d-1, shift1['id'])] + x[(s, d, shift2['id'])] <= 1 + timing_violations[s,d]
# Coverage Requirements (secondary priority)
for d in range(1, num_days+1):
day_index = d - 1
for cycle in cycle_cols:
staff_needed = data.iloc[day_index][f'{cycle}_staff']
covering_shifts = [shift for shift in possible_shifts if cycle in shift['cycles_covered']]
model += pl.lpSum(x[(s, d, shift['id'])]
for s in range(1, num_staff+1)
for shift in covering_shifts) >= staff_needed
# Basic feasibility constraints
for s in range(1, num_staff+1):
for d in range(1, num_days+1):
# At most one shift per day per staff
model += pl.lpSum(x[(s, d, shift['id'])] for shift in possible_shifts) <= 1
# Solve with extended time limit
solver = pl.PULP_CBC_CMD(timeLimit=time_limit, msg=1, gapRel=0.01)
model.solve(solver)
# Check if a feasible solution was found
if model.status == pl.LpStatusOptimal or model.status == pl.LpStatusNotSolved:
# Extract the solution
schedule = []
for s in range(1, num_staff+1):
for d in range(1, num_days+1):
for shift in possible_shifts:
if pl.value(x[(s, d, shift['id'])]) == 1:
shift_details = next((sh for sh in possible_shifts if sh['id'] == shift['id']), None)
schedule.append({
'staff_id': s,
'day': d,
'shift_id': shift['id'],
'start': shift_details['start'],
'end': shift_details['end'],
'duration': shift_details['duration'],
'cycles_covered': list(shift_details['cycles_covered'])
})
# Verify timing consistency in the solution
timing_violations = sum(pl.value(timing_violations[s,d])
for s in range(1, num_staff+1)
for d in range(2, num_days+1))
if timing_violations > 0:
print(f"Warning: Found {timing_violations} timing violations")
return None, None
return schedule, model.objective.value()
else:
return None, None
except Exception as e:
print(f"Error in optimization: {e}")
return None, None
# Try to solve with estimated number of staff
if exact_staff_count is not None and exact_staff_count > 0:
# If exact staff count is specified, only try with that count
staff_count = int(exact_staff_count)
results = f"Using exactly {staff_count} staff as specified...\n"
# Try to solve with exactly this many staff
schedule, objective = optimize_schedule(staff_count)
if schedule is None:
results += f"Failed to find a feasible solution with exactly {staff_count} staff.\n"
results += "Try increasing the staff count.\n"
return results, None, None, None, None
else:
# Start from theoretical minimum and work up
min_staff = max(1, int(theoretical_min_staff)) # Start from theoretical minimum
max_staff = int(min_staff_estimate) + 5 # Allow some buffer
results = f"Theoretical minimum staff needed: {theoretical_min_staff:.1f}\n"
results += f"Searching for minimum staff count starting from {min_staff}...\n"
# Try each staff count from min to max
for staff_count in range(min_staff, max_staff + 1):
results += f"Trying with {staff_count} staff...\n"
# Increase time limit for each attempt to give the solver more time
time_limit = 300 + (staff_count - min_staff) * 100 # More time for larger staff counts
schedule, objective = optimize_schedule(staff_count, time_limit)
if schedule is not None:
results += f"Found feasible solution with {staff_count} staff.\n"
break
if schedule is None:
results += "Failed to find a feasible solution with the attempted staff counts.\n"
results += "Try increasing the staff count manually or relaxing constraints.\n"
return results, None, None, None, None
results += f"Optimal solution found with {staff_count} staff\n"
results += f"Total staff hours: {objective}\n"
# Convert to DataFrame for analysis
schedule_df = pd.DataFrame(schedule)
# Analyze staff workload
staff_hours = {}
for s in range(1, staff_count+1):
staff_shifts = schedule_df[schedule_df['staff_id'] == s]
total_hours = staff_shifts['duration'].sum()
staff_hours[s] = total_hours
# After calculating staff hours, filter out staff with 0 hours before displaying
active_staff_hours = {s: hours for s, hours in staff_hours.items() if hours > 0}
results += "\nStaff Hours:\n"
for staff_id, hours in active_staff_hours.items():
utilization = (hours / MAX_HOURS_PER_STAFF) * 100
results += f"Staff {staff_id}: {hours} hours ({utilization:.1f}% utilization)\n"
# Add overtime information
if hours > MAX_HOURS_PER_STAFF:
overtime = hours - MAX_HOURS_PER_STAFF
overtime_percent = (overtime / MAX_HOURS_PER_STAFF) * 100
results += f" Overtime: {overtime:.1f} hours ({overtime_percent:.1f}%)\n"
# Use active_staff_hours for average utilization calculation
active_staff_count = len(active_staff_hours)
avg_utilization = sum(active_staff_hours.values()) / (active_staff_count * MAX_HOURS_PER_STAFF) * 100
results += f"\nAverage staff utilization: {avg_utilization:.1f}%\n"
# Check coverage for each day and cycle
coverage_check = []
for d in range(1, num_days+1):
day_index = d - 1 # 0-indexed for DataFrame
day_schedule = schedule_df[schedule_df['day'] == d]
for cycle in cycle_cols:
required = data.iloc[day_index][f'{cycle}_staff']
# Count staff covering this cycle
assigned = sum(1 for _, shift in day_schedule.iterrows()
if cycle in shift['cycles_covered'])
coverage_check.append({
'day': d,
'cycle': cycle,
'required': required,
'assigned': assigned,
'satisfied': assigned >= required
})
coverage_df = pd.DataFrame(coverage_check)
satisfaction = coverage_df['satisfied'].mean() * 100
results += f"Coverage satisfaction: {satisfaction:.1f}%\n"
if satisfaction < 100:
results += "Warning: Not all staffing requirements are met!\n"
unsatisfied = coverage_df[~coverage_df['satisfied']]
results += unsatisfied.to_string() + "\n"
# Generate detailed schedule report
detailed_schedule = "Detailed Schedule:\n"
for d in range(1, num_days+1):
day_schedule = schedule_df[schedule_df['day'] == d]
day_schedule = day_schedule.sort_values(['start'])
detailed_schedule += f"\nDay {d}:\n"
for _, shift in day_schedule.iterrows():
start_hour = shift['start']
end_hour = shift['end']
start_str = am_pm(start_hour)
end_str = am_pm(end_hour)
cycles = ", ".join(shift['cycles_covered'])
detailed_schedule += f" Staff {shift['staff_id']}: {start_str}-{end_str} ({shift['duration']} hrs), Cycles: {cycles}\n"
# Generate schedule visualization
fig, ax = plt.subplots(figsize=(15, 8))
# Prepare schedule for plotting
staff_days = {}
for s in range(1, staff_count+1):
staff_days[s] = [0] * num_days # 0 means off duty
for _, shift in schedule_df.iterrows():
staff_id = shift['staff_id']
day = shift['day'] - 1 # 0-indexed
staff_days[staff_id][day] = shift['duration']
# Plot the schedule
for s, hours in staff_days.items():
ax.bar(range(1, num_days+1), hours, label=f'Staff {s}')
ax.set_xlabel('Day')
ax.set_ylabel('Shift Hours')
ax.set_title('Staff Schedule')
ax.set_xticks(range(1, num_days+1))
ax.legend()
# Save the figure to a temporary file
plot_path = None
with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as f:
plt.savefig(f.name)
plt.close(fig)
plot_path = f.name
# Create a Gantt chart with advanced visuals and alternating labels - only showing active staff
gantt_path = create_gantt_chart(schedule_df, num_days, staff_count)
# Convert schedule to CSV data
schedule_df['start_ampm'] = schedule_df['start'].apply(am_pm)
schedule_df['end_ampm'] = schedule_df['end'].apply(am_pm)
schedule_csv = schedule_df[['staff_id', 'day', 'start_ampm', 'end_ampm', 'duration', 'cycles_covered']].to_csv(index=False)
# Create a temporary file and write the CSV data into it
with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix=".csv") as temp_file:
temp_file.write(schedule_csv)
schedule_csv_path = temp_file.name
# Create staff assignment table
staff_assignment_data = []
for d in range(1, num_days + 1):
cycle_staff = {}
for cycle in cycle_cols:
# Get staff IDs assigned to this cycle on this day
staff_ids = schedule_df[(schedule_df['day'] == d) & (schedule_df['cycles_covered'].apply(lambda x: cycle in x))]['staff_id'].tolist()
cycle_staff[cycle] = len(staff_ids)
staff_assignment_data.append([d] + [cycle_staff[cycle] for cycle in cycle_cols])
staff_assignment_df = pd.DataFrame(staff_assignment_data, columns=['Day'] + cycle_cols)
# Create CSV files for download
staff_assignment_csv_path = None
with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix=".csv") as temp_file:
staff_assignment_df.to_csv(temp_file.name, index=False)
staff_assignment_csv_path = temp_file.name
# Return all required values in the correct order
return results, staff_assignment_df, gantt_path, schedule_df, plot_path, schedule_csv_path, staff_assignment_csv_path
def convert_to_24h(time_str):
"""Converts AM/PM time string to 24-hour format."""
try:
time_obj = datetime.strptime(time_str, "%I:00 %p")
return time_obj.hour
except ValueError:
return None
def gradio_wrapper(
csv_file, beds_per_staff, max_hours_per_staff, hours_per_cycle,
rest_days_per_week, clinic_start_ampm, clinic_end_ampm, overlap_time, max_start_time_change,
exact_staff_count=None, overtime_percent=100
):
try:
# Convert AM/PM times to 24-hour format
clinic_start = convert_to_24h(clinic_start_ampm)
clinic_end = convert_to_24h(clinic_end_ampm)
# Call the optimization function
results, staff_assignment_df, gantt_path, schedule_df, plot_path, schedule_csv_path, staff_assignment_csv_path = optimize_staffing(
csv_file, beds_per_staff, max_hours_per_staff, hours_per_cycle,
rest_days_per_week, clinic_start, clinic_end, overlap_time, max_start_time_change,
exact_staff_count, overtime_percent
)
# Return the results
return staff_assignment_df, gantt_path, schedule_df, plot_path, staff_assignment_csv_path, schedule_csv_path
except Exception as e:
# If there's an error in the optimization process, return a meaningful error message
empty_staff_df = pd.DataFrame(columns=["Day"])
error_message = f"Error during optimization: {str(e)}\n\nPlease try with different parameters or a simpler dataset."
# Return error in the first output
return empty_staff_df, None, None, None, None, None
# Create a Gantt chart with advanced visuals and alternating labels - only showing active staff
def create_gantt_chart(schedule_df, num_days, staff_count):
# Get the list of active staff IDs (staff who have at least one shift)
active_staff_ids = sorted(schedule_df['staff_id'].unique())
active_staff_count = len(active_staff_ids)
# Create a mapping from original staff ID to position in the chart
staff_position = {staff_id: i+1 for i, staff_id in enumerate(active_staff_ids)}
# Create a larger figure with higher DPI
plt.figure(figsize=(max(30, num_days * 1.5), max(12, active_staff_count * 0.8)), dpi=200)
# Use a more sophisticated color palette - only for active staff
colors = plt.cm.viridis(np.linspace(0.1, 0.9, active_staff_count))
# Set a modern style
plt.style.use('seaborn-v0_8-whitegrid')
# Create a new axis with a slight background color
ax = plt.gca()
ax.set_facecolor('#f8f9fa')
# Sort by staff then day
schedule_df = schedule_df.sort_values(['staff_id', 'day'])
# Plot Gantt chart - only for active staff
for i, staff_id in enumerate(active_staff_ids):
staff_shifts = schedule_df[schedule_df['staff_id'] == staff_id]
y_pos = active_staff_count - i # Position based on index in active staff list
# Add staff label with a background box
ax.text(-0.7, y_pos, f"Staff {staff_id}", fontsize=12, fontweight='bold',
ha='right', va='center', bbox=dict(facecolor='white', edgecolor='gray',
boxstyle='round,pad=0.5', alpha=0.9))
# Add a subtle background for each staff row
ax.axhspan(y_pos-0.4, y_pos+0.4, color='white', alpha=0.4, zorder=-5)
# Track shift positions to avoid label overlap
shift_positions = []
for idx, shift in enumerate(staff_shifts.iterrows()):
_, shift = shift
day = shift['day']
start_hour = shift['start']
end_hour = shift['end']
duration = shift['duration']
# Format times for display
start_ampm = am_pm(start_hour)
end_ampm = am_pm(end_hour)
# Calculate shift position
shift_start_pos = day-1+start_hour/24
# Handle overnight shifts
if end_hour < start_hour: # Overnight shift
# First part of shift (until midnight)
rect1 = ax.barh(y_pos, (24-start_hour)/24, left=shift_start_pos,
height=0.6, color=colors[i], alpha=0.9,
edgecolor='black', linewidth=1, zorder=10)
# Add gradient effect
for r in rect1:
r.set_edgecolor('black')
r.set_linewidth(1)
# Second part of shift (after midnight)
rect2 = ax.barh(y_pos, end_hour/24, left=day,
height=0.6, color=colors[i], alpha=0.9,
edgecolor='black', linewidth=1, zorder=10)
# Add gradient effect
for r in rect2:
r.set_edgecolor('black')
r.set_linewidth(1)
# For overnight shifts, we'll place the label in the first part if it's long enough
shift_width = (24-start_hour)/24
if shift_width >= 0.1: # Only add label if there's enough space
label_pos = shift_start_pos + shift_width/2
# Alternate labels above and below
y_offset = 0.35 if idx % 2 == 0 else -0.35
# Add label with background for better readability
label = f"{start_ampm}-{end_ampm}"
text = ax.text(label_pos, y_pos + y_offset, label,
ha='center', va='center', fontsize=9, fontweight='bold',
color='black', bbox=dict(facecolor='white', alpha=0.9, pad=3,
boxstyle='round,pad=0.3', edgecolor='gray'),
zorder=20)
shift_positions.append(label_pos)
else:
# Regular shift
shift_width = duration/24
rect = ax.barh(y_pos, shift_width, left=shift_start_pos,
height=0.6, color=colors[i], alpha=0.9,
edgecolor='black', linewidth=1, zorder=10)
# Add gradient effect
for r in rect:
r.set_edgecolor('black')
r.set_linewidth(1)
# Only add label if there's enough space
if shift_width >= 0.1:
label_pos = shift_start_pos + shift_width/2
# Alternate labels above and below
y_offset = 0.35 if idx % 2 == 0 else -0.35
# Add label with background for better readability
label = f"{start_ampm}-{end_ampm}"
text = ax.text(label_pos, y_pos + y_offset, label,
ha='center', va='center', fontsize=9, fontweight='bold',
color='black', bbox=dict(facecolor='white', alpha=0.9, pad=3,
boxstyle='round,pad=0.3', edgecolor='gray'),
zorder=20)
shift_positions.append(label_pos)
# Add weekend highlighting with a more sophisticated look
for day in range(1, num_days + 1):
# Determine if this is a weekend (assuming day 1 is Monday)
is_weekend = (day % 7 == 0) or (day % 7 == 6) # Saturday or Sunday
if is_weekend:
ax.axvspan(day-1, day, alpha=0.15, color='#ff9999', zorder=-10)
day_label = "Saturday" if day % 7 == 6 else "Sunday"
ax.text(day-0.5, 0.2, day_label, ha='center', fontsize=10, color='#cc0000',
fontweight='bold', bbox=dict(facecolor='white', alpha=0.7, pad=2, boxstyle='round'))
# Set x-axis ticks for each day with better formatting
ax.set_xticks(np.arange(0.5, num_days, 1))
day_labels = [f"Day {d}" for d in range(1, num_days+1)]
ax.set_xticklabels(day_labels, rotation=0, ha='center', fontsize=10)
# Add vertical lines between days with better styling
for day in range(1, num_days):
ax.axvline(x=day, color='#aaaaaa', linestyle='-', alpha=0.5, zorder=-5)
# Set y-axis ticks for each staff
ax.set_yticks(np.arange(1, active_staff_count+1))
ax.set_yticklabels([]) # Remove default labels as we've added custom ones
# Set axis limits with some padding
ax.set_xlim(-0.8, num_days)
ax.set_ylim(0.5, active_staff_count + 0.5)
# Add grid for hours (every 6 hours) with better styling
for day in range(num_days):
for hour in [6, 12, 18]:
ax.axvline(x=day + hour/24, color='#cccccc', linestyle=':', alpha=0.5, zorder=-5)
# Add small hour markers at the bottom
hour_label = "6AM" if hour == 6 else "Noon" if hour == 12 else "6PM"
ax.text(day + hour/24, 0, hour_label, ha='center', va='bottom', fontsize=7,
color='#666666', rotation=90, alpha=0.7)
# Add title and labels with more sophisticated styling
plt.title(f'Staff Schedule ({active_staff_count} Active Staff)', fontsize=24, fontweight='bold', pad=20, color='#333333')
plt.xlabel('Day', fontsize=16, labelpad=10, color='#333333')
# Add a legend for time reference with better styling
time_box = plt.figtext(0.01, 0.01, "Time Reference:", ha='left', fontsize=10,
fontweight='bold', color='#333333')
time_markers = ['6 AM', 'Noon', '6 PM', 'Midnight']
for i, time in enumerate(time_markers):
plt.figtext(0.08 + i*0.06, 0.01, time, ha='left', fontsize=9, color='#555555')
# Remove spines
for spine in ['top', 'right', 'left']:
ax.spines[spine].set_visible(False)
# Add a note about weekends with better styling
weekend_note = plt.figtext(0.01, 0.97, "Red areas = Weekends", fontsize=12,
color='#cc0000', fontweight='bold',
bbox=dict(facecolor='white', alpha=0.7, pad=5, boxstyle='round'))
# Add a subtle border around the entire chart
plt.box(False)
# Save the Gantt chart with high quality
with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as f:
plt.tight_layout()
plt.savefig(f.name, dpi=200, bbox_inches='tight', facecolor='white')
plt.close()
return f.name
# Define Gradio UI
am_pm_times = [f"{i:02d}:00 AM" for i in range(1, 13)] + [f"{i:02d}:00 PM" for i in range(1, 13)]
# Add CSS for chart containers
css = """
.chart-container {
height: 800px !important;
width: 100% !important;
margin: 20px 0;
padding: 20px;
border: 1px solid #ddd;
border-radius: 8px;
background: white;
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}
.weekly-chart-container {
height: 1000px !important;
width: 100% !important;
margin: 20px 0;
padding: 20px;
border: 1px solid #ddd;
border-radius: 8px;
background: white;
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}
/* Ensure plotly charts are visible */
.js-plotly-plot {
width: 100% !important;
height: 100% !important;
}
/* Improve visibility of chart titles */
.gtitle {
font-weight: bold !important;
font-size: 20px !important;
}
"""
with gr.Blocks(title="Staff Scheduling Optimizer", css=css) as iface:
gr.Markdown("# Staff Scheduling Optimizer")
gr.Markdown("Upload a CSV file with cycle data and configure parameters to generate an optimal staff schedule.")
with gr.Row():
# LEFT PANEL - Inputs
with gr.Column(scale=1):
gr.Markdown("### Input Parameters")
# Input parameters
csv_input = gr.File(label="Upload CSV")
beds_per_staff = gr.Number(label="Beds per Staff", value=3)
max_hours_per_staff = gr.Number(label="Maximum monthly hours", value=160)
hours_per_cycle = gr.Number(label="Hours per Cycle", value=4)
rest_days_per_week = gr.Number(label="Rest Days per Week", value=2)
clinic_start_ampm = gr.Dropdown(label="Clinic Start Hour (AM/PM)", choices=am_pm_times, value="08:00 AM")
clinic_end_ampm = gr.Dropdown(label="Clinic End Hour (AM/PM)", choices=am_pm_times, value="08:00 PM")
overlap_time = gr.Number(label="Overlap Time", value=0)
max_start_time_change = gr.Number(label="Max Start Time Change", value=2)
exact_staff_count = gr.Number(label="Exact Staff Count (optional)", value=None)
overtime_percent = gr.Slider(label="Overtime Allowed (%)", minimum=0, maximum=100, value=100, step=10)
optimize_btn = gr.Button("Optimize Schedule", variant="primary", size="lg")
# RIGHT PANEL - Outputs
with gr.Column(scale=2):
gr.Markdown("### Results")
# Tabs for different outputs - reordered
with gr.Tabs():
with gr.TabItem("Detailed Schedule"):
with gr.Row():
csv_schedule = gr.Dataframe(label="Detailed Schedule", elem_id="csv_schedule")
with gr.Row():
schedule_download_file = gr.File(label="Download Detailed Schedule", visible=True)
with gr.TabItem("Gantt Chart"):
gantt_chart = gr.Image(label="Staff Schedule Visualization", elem_id="gantt_chart")
with gr.TabItem("Staff Coverage by Cycle"):
with gr.Row():
staff_assignment_table = gr.Dataframe(label="Staff Count in Each Cycle (Staff May Overlap)", elem_id="staff_assignment_table")
with gr.Row():
staff_download_file = gr.File(label="Download Coverage Table", visible=True)
with gr.TabItem("Constraints and Analytics"):
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### Applied Constraints")
constraints_text = gr.TextArea(
label="",
interactive=False,
show_label=False
)
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### Monthly Distribution")
monthly_chart = gr.HTML(
label="Monthly Hours Distribution",
show_label=False,
elem_classes="chart-container"
)
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### Weekly Distribution")
weekly_charts = gr.HTML(
label="Weekly Hours Distribution",
show_label=False,
elem_classes="weekly-chart-container"
)
with gr.TabItem("Staff Overlap"):
with gr.Row():
overlap_chart = gr.HTML(
label="Staff Overlap Visualization",
show_label=False
)
with gr.Row():
gr.Markdown("""
This heatmap shows the number of staff members working simultaneously throughout each day.
- Darker colors indicate more staff overlap
- The x-axis shows time of day in 30-minute intervals
- The y-axis shows each day of the schedule
""")
with gr.TabItem("Staff Absence Handler"):
with gr.Row():
with gr.Column():
gr.Markdown("### Handle Staff Absence")
absent_staff = gr.Number(label="Staff ID to be absent", precision=0)
absence_start = gr.Number(label="Start Day", precision=0)
absence_end = gr.Number(label="End Day", precision=0)
handle_absence_btn = gr.Button("Redistribute Shifts", variant="primary")
with gr.Column():
absence_result = gr.TextArea(label="Redistribution Results", interactive=False)
updated_schedule = gr.DataFrame(label="Updated Schedule")
absence_gantt_chart = gr.Image(label="Absence Schedule Visualization", elem_id="absence_gantt_chart")
# Define download functions
def create_download_link(df, filename="data.csv"):
"""Create a CSV download link for a dataframe"""
if df is None or df.empty:
return None
csv_data = df.to_csv(index=False)
with tempfile.NamedTemporaryFile(mode='w', delete=False, suffix='.csv') as f:
f.write(csv_data)
return f.name
# Update the optimize_and_display function
def optimize_and_display(csv_file, beds_per_staff, max_hours_per_staff, hours_per_cycle,
rest_days_per_week, clinic_start_ampm, clinic_end_ampm,
overlap_time, max_start_time_change, exact_staff_count, overtime_percent):
try:
# Convert AM/PM times to 24-hour format
clinic_start = convert_to_24h(clinic_start_ampm)
clinic_end = convert_to_24h(clinic_end_ampm)
# Call the optimization function
results, staff_assignment_df, gantt_path, schedule_df, plot_path, schedule_csv_path, staff_assignment_csv_path = optimize_staffing(
csv_file, beds_per_staff, max_hours_per_staff, hours_per_cycle,
rest_days_per_week, clinic_start, clinic_end, overlap_time, max_start_time_change,
exact_staff_count, overtime_percent
)
if schedule_df is not None:
try:
# Generate analytics data
constraints_info = get_constraints_summary(
max_hours_per_staff,
rest_days_per_week,
overtime_percent
)
# Create visualizations directly as HTML
monthly_html = create_monthly_distribution_chart(schedule_df)
weekly_html = create_weekly_distribution_charts(schedule_df)
overlap_html = create_overlap_visualization(schedule_df)
return (
staff_assignment_df,
gantt_path,
schedule_df,
schedule_csv_path,
constraints_info,
monthly_html,
weekly_html,
overlap_html
)
except Exception as e:
print(f"Error in visualization: {str(e)}")
return (
staff_assignment_df,
gantt_path,
schedule_df,
schedule_csv_path,
"Error in constraints",
"Error creating monthly chart
",
"Error creating weekly charts
",
"Error creating overlap visualization
"
)
else:
return (None,) * 8
except Exception as e:
print(f"Error in optimization: {str(e)}")
return (None,) * 8
def get_constraints_summary(max_hours, rest_days, overtime_percent):
"""Generate a summary of all applied constraints from actual parameters"""
constraints = [
"Applied Scheduling Constraints:",
"----------------------------",
f"1. Maximum Hours per Month: {max_hours} hours",
f"2. Required Rest Days per Week: {rest_days} days",
f"3. Maximum Weekly Hours: 60 hours per staff member",
"4. Minimum Rest Period: 11 hours between shifts",
"5. Maximum Consecutive Days: 6 working days",
f"6. Overtime Allowance: {overtime_percent}% of standard hours",
"7. Coverage Requirements:",
" - All cycles must be fully staffed",
" - No understaffing allowed",
" - Staff assigned based on required beds/staff ratio",
"8. Shift Constraints:",
" - Available shift durations: 5, 10 hours",
" - Shifts must align with cycle times",
"9. Staff Scheduling Rules:",
" - Equal distribution of workload when possible",
" - Consistent shift patterns preferred",
" - Weekend rotations distributed fairly"
]
return "\n".join(constraints)
def create_monthly_distribution_chart(schedule_df):
"""Create Seaborn pie chart for monthly hours distribution"""
if schedule_df is None or schedule_df.empty:
return "No data available for visualization
"
try:
# Calculate total hours per staff member
staff_hours = schedule_df.groupby('staff_id')['duration'].sum()
# Create pie chart
fig, ax = plt.subplots(figsize=(8, 8))
sns.set_palette("pastel")
ax.pie(staff_hours, labels=staff_hours.index, autopct='%1.1f%%', startangle=90)
ax.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.title("Monthly Hours Distribution")
# Convert plot to PNG image
img = io.BytesIO()
plt.savefig(img, format='png', bbox_inches='tight') # Added bbox_inches='tight'
plt.close(fig)
img.seek(0)
# Encode to base64
img_base64 = base64.b64encode(img.read()).decode('utf-8')
img_html = f'
'
return img_html
except Exception as e:
print(f"Error in monthly chart: {e}")
return f"Error creating monthly chart: {str(e)}
"
def create_weekly_distribution_charts(schedule_df):
"""Create Plotly pie charts for weekly hours distribution"""
if schedule_df is None or schedule_df.empty:
return "No data available for visualization
"
try:
# Calculate total hours per staff member for each week
schedule_df['week'] = schedule_df['day'] // 7 # Assuming each week starts on day 0, 7, 14, etc.
weekly_hours = schedule_df.groupby(['week', 'staff_id'])['duration'].sum().reset_index()
# Create staff labels
weekly_hours['staff_label'] = weekly_hours.apply(
lambda x: f"Staff {x['staff_id']} ({x['duration']:.1f}hrs)",
axis=1
)
# Get unique weeks
weeks = sorted(weekly_hours['week'].unique())
# Define color palette
colors = px.colors.qualitative.Set3
# Create subplots
fig = make_subplots(
rows=len(weeks),
cols=1,
subplot_titles=[f'Week {week}' for week in weeks],
specs=[[{'type': 'domain'}] for week in weeks]
)
# Add pie charts for each week
for i, week in enumerate(weeks, start=1):
week_data = weekly_hours[weekly_hours['week'] == week]
fig.add_trace(
go.Pie(
values=week_data['duration'],
labels=week_data['staff_label'],
name=f'Week {week}',
showlegend=(i == 1),
marker_colors=colors,
textposition='inside',
textinfo='percent+label',
hovertemplate=(
"Staff: %{label}
"
"Hours: %{value:.1f}
"
"Percentage: %{percent:.1f}%"
""
)
),
row=i,
col=1
)
fig.update_layout(
height=300 * len(weeks),
width=800,
title_text="Weekly Hours Distribution",
title_x=0.5,
title_font_size=20,
margin=dict(t=50, l=50, r=50, b=50),
showlegend=True
)
return fig.to_html(include_plotlyjs='cdn', full_html=False)
except Exception as e:
print(f"Error in weekly charts: {e}")
return f"Error creating weekly charts: {str(e)}
"
# Add this new function for creating the overlap visualization
def create_overlap_visualization(schedule_df):
"""Create Seaborn heatmap for staff overlap"""
if schedule_df is None or schedule_df.empty:
return "No data available for visualization
"
try:
# Create 24-hour timeline with 30-minute intervals
intervals = 48 # 24 hours * 2 (30-minute intervals)
days = sorted(schedule_df['day'].unique())
# Initialize overlap matrix
overlap_data = np.zeros((len(days), intervals))
# Calculate overlaps
for day_idx, day in enumerate(days):
day_shifts = schedule_df[schedule_df['day'] == day]
for i in range(intervals):
time = i * 0.5
staff_working = 0
for _, shift in day_shifts.iterrows():
start = shift['start']
end = shift['end']
if end < start: # Overnight shift
if time >= start or time < end:
staff_working += 1
else:
if start <= time < end:
staff_working += 1
overlap_data[day_idx, i] = staff_working
# Create time labels
time_labels = [f"{int(i//2):02d}:{int((i%2)*30):02d}" for i in range(intervals)]
# Create heatmap
fig, ax = plt.subplots(figsize=(12, 8))
sns.heatmap(overlap_data, cmap="viridis", ax=ax, cbar_kws={'label': 'Staff Count'})
# Set labels
ax.set_xticks(np.arange(len(time_labels[::4])))
ax.set_xticklabels(time_labels[::4], rotation=45, ha="right")
ax.set_yticks(np.arange(len(days)))
ax.set_yticklabels(days)
# Add title
ax.set_title("Staff Overlap Throughout the Day")
# Ensure layout is tight
plt.tight_layout()
# Convert plot to PNG image
img = io.BytesIO()
plt.savefig(img, format='png', bbox_inches='tight') # Added bbox_inches='tight'
plt.close(fig)
img.seek(0)
# Encode to base64
img_base64 = base64.b64encode(img.read()).decode('utf-8')
img_html = f''
return img_html
except Exception as e:
print(f"Error in overlap visualization: {e}")
return f"Error creating overlap visualization: {str(e)}
"
# Connect the button to the optimization function
optimize_btn.click(
fn=optimize_and_display,
inputs=[
csv_input, beds_per_staff, max_hours_per_staff, hours_per_cycle,
rest_days_per_week, clinic_start_ampm, clinic_end_ampm,
overlap_time, max_start_time_change, exact_staff_count, overtime_percent
],
outputs=[
staff_assignment_table, # Staff coverage table
gantt_chart, # Gantt chart
csv_schedule, # Detailed schedule
schedule_download_file, # Download file
constraints_text, # Constraints text
monthly_chart, # Monthly distribution
weekly_charts, # Weekly distribution
overlap_chart # Staff overlap visualization
]
)
# Add the handler function
def handle_absence_click(staff_id, start_day, end_day, current_schedule, max_hours_per_staff, overtime_percent):
if current_schedule is None or current_schedule.empty:
return "No current schedule loaded.", None, None
absence_dates = list(range(int(start_day), int(end_day) + 1))
summary, absence_schedule, absence_gantt_path = handle_staff_absence(
current_schedule,
int(staff_id),
absence_dates,
max_hours_per_staff,
overtime_percent
)
return summary, absence_schedule, absence_gantt_path
# Connect the absence handler button
handle_absence_btn.click(
fn=handle_absence_click,
inputs=[
absent_staff,
absence_start,
absence_end,
csv_schedule, # Current schedule
max_hours_per_staff, # Add this parameter
overtime_percent # Add this parameter
],
outputs=[
absence_result,
updated_schedule,
absence_gantt_chart
]
)
# Launch the Gradio app
iface.launch(share=True)
def create_interface():
with gr.Blocks() as demo:
gr.Markdown("# NEF Scheduling System")
with gr.Tabs() as tabs:
with gr.Tab("Schedule Input"):
# Schedule input components
with gr.Row():
csv_input = gr.File(label="Upload Schedule Data (CSV)")
schedule_preview = gr.DataFrame(label="Schedule Preview")
with gr.Tab("Schedule Output"):
# Schedule output components
with gr.Row():
schedule_output = gr.DataFrame(label="Generated Schedule")
download_btn = gr.Button("Download Schedule")
with gr.Tab("Constraints and Analytics"):
with gr.Row():
with gr.Column():
gr.Markdown("### Applied Constraints")
constraints_text = gr.TextArea(label="", interactive=False)
with gr.Row():
with gr.Column():
gr.Markdown("### Monthly Distribution")
monthly_chart = gr.HTML(label="Monthly Hours Distribution")
with gr.Row():
with gr.Column():
gr.Markdown("### Weekly Distribution")
weekly_charts = gr.HTML(label="Weekly Hours Distribution")
with gr.TabItem("Staff Absence Handler"):
with gr.Row():
with gr.Column():
gr.Markdown("### Handle Staff Absence")
absent_staff = gr.Number(label="Staff ID to be absent", precision=0)
absence_start = gr.Number(label="Start Day", precision=0)
absence_end = gr.Number(label="End Day", precision=0)
handle_absence_btn = gr.Button("Redistribute Shifts", variant="primary")
with gr.Column():
absence_result = gr.TextArea(label="Redistribution Results", interactive=False)
updated_schedule = gr.DataFrame(label="Updated Schedule")
absence_gantt_chart = gr.Image(label="Absence Schedule Visualization", elem_id="absence_gantt_chart")
return demo
def handle_staff_absence(schedule_df, absent_staff_id, absence_dates, max_hours_per_staff, overtime_percent):
"""
Redistribute shifts of absent staff member to others, prioritizing staff with lowest monthly hours
"""
try:
# Create a copy of the original schedule
new_schedule = schedule_df.copy()
# Get shifts that need to be redistributed
absent_shifts = new_schedule[
(new_schedule['staff_id'] == absent_staff_id) &
(new_schedule['day'].isin(absence_dates))
]
if absent_shifts.empty:
return "No shifts found for the specified staff member on given dates.", None, None
# Get available staff (excluding absent staff)
available_staff = sorted(list(set(new_schedule['staff_id']) - {absent_staff_id}))
# Calculate current hours for each staff member
current_hours = new_schedule.groupby('staff_id')['duration'].sum()
# Sort staff by current hours (ascending) to prioritize those with fewer hours
staff_hours_sorted = current_hours.reindex(available_staff).sort_values()
available_staff = staff_hours_sorted.index.tolist()
# Calculate remaining available hours for each staff
max_allowed_hours = max_hours_per_staff * (1 + overtime_percent/100)
available_hours = {
staff_id: max_allowed_hours - current_hours.get(staff_id, 0)
for staff_id in available_staff
}
results = []
unassigned_shifts = []
# Process each shift that needs to be redistributed
for _, shift in absent_shifts.iterrows():
# Find eligible staff for this shift, prioritizing those with fewer hours
eligible_staff = []
eligible_staff_hours = {}
for staff_id in available_staff:
# Check if staff has enough remaining hours
if available_hours[staff_id] >= shift['duration']:
# Check if staff is not already working that day
staff_shifts_that_day = new_schedule[
(new_schedule['staff_id'] == staff_id) &
(new_schedule['day'] == shift['day'])
]
if staff_shifts_that_day.empty:
# Check minimum rest period (11 hours)
day_before = new_schedule[
(new_schedule['staff_id'] == staff_id) &
(new_schedule['day'] == shift['day'] - 1)
]
day_after = new_schedule[
(new_schedule['staff_id'] == staff_id) &
(new_schedule['day'] == shift['day'] + 1)
]
can_work = True
if not day_before.empty:
end_time_before = day_before.iloc[0]['end']
if (shift['start'] + 24 - end_time_before) < 11:
can_work = False
if not day_after.empty and can_work:
start_time_after = day_after.iloc[0]['start']
if (starttime_after + 24 - shift['end']) < 11:
can_work = False
if can_work:
eligible_staff.append(staff_id)
eligible_staff_hours[staff_id] = current_hours.get(staff_id, 0)
if eligible_staff:
# Sort eligible staff by current hours to prioritize those with fewer hours
sorted_eligible = sorted(eligible_staff, key=lambda x: eligible_staff_hours[x])
best_staff = sorted_eligible[0] # Select staff with lowest hours
# Update the schedule
new_schedule.loc[shift.name, 'staff_id'] = best_staff
# Update available hours and current hours
available_hours[best_staff] -= shift['duration']
current_hours[best_staff] = current_hours.get(best_staff, 0) + shift['duration']
results.append(
f"Shift on Day {shift['day']} ({shift['duration']} hours) "
f"reassigned to Staff {best_staff} (current hours: {current_hours[best_staff]:.1f})"
)
else:
unassigned_shifts.append(
f"Could not reassign shift on Day {shift['day']} ({shift['duration']} hours)"
)
# Generate detailed summary with hours distribution
summary = "\n".join([
"Shift Redistribution Summary:",
"----------------------------",
f"Staff {absent_staff_id} absent for {len(absence_dates)} days",
f"Successfully reassigned: {len(results)} shifts",
f"Failed to reassign: {len(unassigned_shifts)} shifts",
"\nCurrent Hours Distribution:",
"-------------------------"
] + [
f"Staff {s}: {current_hours.get(s, 0):.1f} hours (of max {max_allowed_hours:.1f})"
for s in sorted(available_staff)
] + [
"\nReassignment Details:",
*results,
"\nUnassigned Shifts:",
*unassigned_shifts
])
# Filter the schedule for the absence period
absence_schedule = new_schedule[new_schedule['day'].isin(absence_dates)].copy()
# Create a Gantt chart for the absence period
absence_gantt_path = create_gantt_chart(absence_schedule, len(absence_dates), len(set(absence_schedule['staff_id'])))
if unassigned_shifts:
return summary, None, None
else:
return summary, absence_schedule, absence_gantt_path
except Exception as e:
return f"Error redistributing shifts: {str(e)}", None, None
class FastScheduler:
def __init__(self, num_staff, num_days, possible_shifts, staff_requirements, constraints):
self.num_staff = num_staff
self.num_days = num_days
self.possible_shifts = possible_shifts
self.staff_requirements = staff_requirements
self.constraints = constraints
self.best_schedule = None
self.best_score = float('inf')
# Track each staff member's last working day and shift time
self.staff_sequences = {} # {staff_id: {'last_day': day, 'last_time': time}}
def _find_optimal_shift(self, staff_id, day, cycle, staff_hours):
"""Find a valid shift that strictly enforces timing consistency"""
valid_shifts = self._get_valid_shifts(cycle)
if not valid_shifts:
return None
# Get staff's last working day and time
staff_info = self.staff_sequences.get(staff_id)
if staff_info:
days_gap = day - staff_info['last_day']
if days_gap == 1: # Consecutive working day
# MUST use exactly the same start time
required_start_time = staff_info['last_time']
valid_shifts = [s for s in valid_shifts if s['start'] == required_start_time]
if not valid_shifts:
return None # No shifts match required start time
# If we have valid shifts (either after rest/off days or matching previous time)
if valid_shifts:
# Take the first valid shift - we don't need to optimize further
shift = valid_shifts[0]
assigned_shift = {
'staff_id': staff_id,
'day': day,
'shift_id': shift['id'],
'start': shift['start'],
'end': shift['end'],
'duration': shift['duration'],
'cycles_covered': list(shift['cycles_covered'])
}
# Update staff sequence tracking
self.staff_sequences[staff_id] = {
'last_day': day,
'last_time': shift['start']
}
return assigned_shift
return None
def _evaluate_schedule(self, schedule):
"""Evaluate schedule based ONLY on timing consistency"""
if not schedule:
return float('inf')
# Group shifts by staff
staff_shifts = {}
for shift in schedule:
staff_id = shift['staff_id']
if staff_id not in staff_shifts:
staff_shifts[staff_id] = []
staff_shifts[staff_id].append(shift)
# Check each staff's shifts for timing consistency
for shifts in staff_shifts.values():
shifts.sort(key=lambda x: x['day'])
for i in range(1, len(shifts)):
current_shift = shifts[i]
prev_shift = shifts[i-1]
days_gap = current_shift['day'] - prev_shift['day']
if days_gap == 1: # Consecutive working days
if current_shift['start'] != prev_shift['start']:
return float('inf') # Immediate rejection for timing violation
# If we get here, all timing constraints are satisfied
return 0 # Perfect score - we don't care about other optimizations
def _get_reference_time(self, staff_id, day):
"""Get the reference time that MUST be used for consecutive days"""
staff_info = self.staff_sequences.get(staff_id)
if staff_info and (day - staff_info['last_day'] == 1):
return staff_info['last_time']
return None # No reference time needed (after rest/off days)
def _is_new_sequence(self, staff_id, day):
"""Check if this is a new sequence (after rest/off days)"""
staff_info = self.staff_sequences.get(staff_id)
if not staff_info:
return True
return (day - staff_info['last_day']) >= 2