The Evolution of Hybrid Work in 2025: Reshaping the Future of Work Through Flexible, Technology-Enhanced Collaboration

import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Any
from dataclasses import dataclass, field
from enum import Enum
import json
import uuid

class WorkLocation(Enum):
    OFFICE = "office"
    HOME = "home"
    COWORKING = "coworking"
    CLIENT_SITE = "client_site"

class MeetingType(Enum):
    BRAINSTORMING = "brainstorming"
    STATUS_UPDATE = "status_update"
    CLIENT_PRESENTATION = "client_presentation"
    TRAINING = "training"
    ONE_ON_ONE = "one_on_one"
    DECISION_MAKING = "decision_making"

@dataclass
class Employee:
    """Employee profile with hybrid work preferences and metrics"""
    employee_id: str
    name: str
    role: str
    team: str
    home_location: str
    office_location: str
    preferred_work_days: List[str] = field(default_factory=list)
    productivity_metrics: Dict[WorkLocation, float] = field(default_factory=dict)
    collaboration_score: float = 0.8
    well_being_index: float = 0.7
    equipment_access: Dict[str, bool] = field(default_factory=dict)
    
@dataclass
class WorkSession:
    """Individual work session data"""
    session_id: str
    employee_id: str
    date: datetime
    location: WorkLocation
    duration_hours: float
    productivity_score: float
    tasks_completed: int
    meetings_attended: int
    collaboration_time: float
    focus_time: float
    
@dataclass
class Team:
    """Team configuration and performance metrics"""
    team_id: str
    name: str
    members: List[str]
    required_collaboration_days: List[str]
    performance_metrics: Dict[str, float] = field(default_factory=dict)
    communication_score: float = 0.75
    project_velocity: float = 1.0
    
class HybridWorkOptimizer:
    """Comprehensive framework for optimizing hybrid work arrangements"""
    
    def __init__(self, organization_name: str):
        self.organization_name = organization_name
        self.employees: Dict[str, Employee] = {}
        self.teams: Dict[str, Team] = {}
        self.work_sessions: List[WorkSession] = []
        self.policies = {
            "min_office_days_per_week": 3,
            "max_consecutive_remote_days": 3,
            "core_collaboration_hours": (9, 17),
            "flexible_hours_allowed": True,
            "outcome_based_evaluation": True
        }
        
        # AI-powered optimization parameters
        self.optimization_weights = {
            "productivity": 0.35,
            "collaboration": 0.25,
            "employee_satisfaction": 0.20,
            "cost_efficiency": 0.20
        }
        
        # Technology integration settings
        self.tech_stack = {
            "ai_scheduling": True,
            "vr_meetings": False,
            "smart_office_booking": True,
            "productivity_analytics": True,
            "wellness_monitoring": True
        }
        
    def register_employee(self, employee: Employee) -> bool:
        """Register employee with hybrid work profile"""
        self.employees[employee.employee_id] = employee
        
        # Initialize productivity metrics if not provided
        if not employee.productivity_metrics:
            employee.productivity_metrics = {
                WorkLocation.OFFICE: 0.85,
                WorkLocation.HOME: 0.80,
                WorkLocation.COWORKING: 0.75
            }
            
        print(f"Registered employee: {employee.name} ({employee.role})")
        return True
        
    def register_team(self, team: Team) -> bool:
        """Register team with collaboration requirements"""
        self.teams[team.team_id] = team
        
        # Validate team members exist
        missing_members = [member for member in team.members if member not in self.employees]
        if missing_members:
            print(f"Warning: Team {team.name} references non-existent employees: {missing_members}")
            
        print(f"Registered team: {team.name} with {len(team.members)} members")
        return True
        
    def optimize_weekly_schedule(self, week_start: datetime, employee_id: str) -> Dict[str, Any]:
        """Generate optimized weekly schedule for an employee"""
        if employee_id not in self.employees:
            return {"error": "Employee not found"}
            
        employee = self.employees[employee_id]
        team_id = next((tid for tid, team in self.teams.items() if employee_id in team.members), None)
        team = self.teams.get(team_id) if team_id else None
        
        # Generate 5-day work week schedule
        schedule = {}
        office_days_assigned = 0
        min_office_days = self.policies["min_office_days_per_week"]
        
        for i in range(5):  # Monday to Friday
            current_date = week_start + timedelta(days=i)
            day_name = current_date.strftime("%A")
            
            # Determine optimal location for this day
            location_scores = self._calculate_location_scores(
                employee, team, current_date, day_name
            )
            
            # Select best location considering constraints
            optimal_location = self._select_optimal_location(
                location_scores, office_days_assigned, min_office_days, i
            )
            
            if optimal_location == WorkLocation.OFFICE:
                office_days_assigned += 1
                
            schedule[day_name] = {
                "date": current_date.isoformat(),
                "location": optimal_location.value,
                "location_scores": {loc.value: score for loc, score in location_scores.items()},
                "recommended_hours": self._get_recommended_hours(employee, optimal_location),
                "focus_blocks": self._schedule_focus_time(optimal_location),
                "collaboration_blocks": self._schedule_collaboration_time(employee, team, optimal_location)
            }
            
        return {
            "employee_id": employee_id,
            "week_start": week_start.isoformat(),
            "schedule": schedule,
            "office_days": office_days_assigned,
            "remote_days": 5 - office_days_assigned,
            "optimization_score": self._calculate_schedule_score(schedule, employee, team)
        }
        
    def track_work_session(self, session: WorkSession):
        """Track individual work session for analytics"""
        self.work_sessions.append(session)
        
        # Update employee productivity metrics
        if session.employee_id in self.employees:
            employee = self.employees[session.employee_id]
            current_metric = employee.productivity_metrics.get(session.location, 0.8)
            
            # Exponentially weighted moving average for productivity updates
            alpha = 0.1
            updated_metric = (alpha * session.productivity_score) + ((1 - alpha) * current_metric)
            employee.productivity_metrics[session.location] = updated_metric
            
    def analyze_hybrid_effectiveness(self, period_days: int = 30) -> Dict[str, Any]:
        """Analyze effectiveness of hybrid work arrangements"""
        end_date = datetime.now()
        start_date = end_date - timedelta(days=period_days)
        
        # Filter sessions for analysis period
        period_sessions = [
            session for session in self.work_sessions
            if start_date <= session.date <= end_date
        ]
        
        if not period_sessions:
            return {"error": "No data available for analysis period"}
            
        # Calculate key metrics
        location_productivity = {}
        location_counts = {}
        
        for session in period_sessions:
            location = session.location
            if location not in location_productivity:
                location_productivity[location] = []
                location_counts[location] = 0
                
            location_productivity[location].append(session.productivity_score)
            location_counts[location] += 1
            
        # Average productivity by location
        avg_productivity = {
            location.value: np.mean(scores) 
            for location, scores in location_productivity.items()
        }
        
        # Team collaboration analysis
        team_performance = {}
        for team_id, team in self.teams.items():
            team_sessions = [s for s in period_sessions if s.employee_id in team.members]
            if team_sessions:
                avg_collab_time = np.mean([s.collaboration_time for s in team_sessions])
                avg_productivity = np.mean([s.productivity_score for s in team_sessions])
                
                team_performance[team.name] = {
                    "average_collaboration_time": avg_collab_time,
                    "average_productivity": avg_productivity,
                    "total_sessions": len(team_sessions)
                }
                
        # Employee satisfaction analysis
        employee_metrics = {}
        for emp_id, employee in self.employees.items():
            emp_sessions = [s for s in period_sessions if s.employee_id == emp_id]
            if emp_sessions:
                office_sessions = [s for s in emp_sessions if s.location == WorkLocation.OFFICE]
                remote_sessions = [s for s in emp_sessions if s.location == WorkLocation.HOME]
                
                employee_metrics[employee.name] = {
                    "total_sessions": len(emp_sessions),
                    "office_percentage": len(office_sessions) / len(emp_sessions) * 100,
                    "average_productivity": np.mean([s.productivity_score for s in emp_sessions]),
                    "well_being_index": employee.well_being_index,
                    "collaboration_score": employee.collaboration_score
                }
                
        return {
            "analysis_period": {
                "start_date": start_date.isoformat(),
                "end_date": end_date.isoformat(),
                "total_sessions": len(period_sessions)
            },
            "productivity_by_location": avg_productivity,
            "location_usage": {loc.value: count for loc, count in location_counts.items()},
            "team_performance": team_performance,
            "employee_metrics": employee_metrics,
            "recommendations": self._generate_optimization_recommendations(period_sessions)
        }
        
    def simulate_schedule_scenarios(self, employee_id: str, scenarios: List[Dict]) -> Dict[str, Any]:
        """Simulate different hybrid work scenarios for comparison"""
        if employee_id not in self.employees:
            return {"error": "Employee not found"}
            
        employee = self.employees[employee_id]
        scenario_results = {}
        
        for i, scenario in enumerate(scenarios):
            scenario_name = scenario.get("name", f"Scenario_{i+1}")
            office_days = scenario.get("office_days_per_week", 3)
            flexible_hours = scenario.get("flexible_hours", True)
            focus_time_blocks = scenario.get("focus_time_blocks", 2)
            
            # Simulate productivity and satisfaction scores
            productivity_score = self._simulate_productivity(
                employee, office_days, flexible_hours, focus_time_blocks
            )
            
            satisfaction_score = self._simulate_satisfaction(
                employee, office_days, flexible_hours
            )
            
            collaboration_effectiveness = self._simulate_collaboration(
                employee, office_days
            )
            
            scenario_results[scenario_name] = {
                "office_days_per_week": office_days,
                "flexible_hours": flexible_hours,
                "focus_time_blocks": focus_time_blocks,
                "predicted_productivity": productivity_score,
                "predicted_satisfaction": satisfaction_score,
                "collaboration_effectiveness": collaboration_effectiveness,
                "overall_score": self._calculate_overall_scenario_score(
                    productivity_score, satisfaction_score, collaboration_effectiveness
                )
            }
            
        # Rank scenarios by overall score
        ranked_scenarios = sorted(
            scenario_results.items(),
            key=lambda x: x["overall_score"],
            reverse=True
        )
        
        return {
            "employee_id": employee_id,
            "scenario_results": scenario_results,
            "recommended_scenario": ranked_scenarios[0][0] if ranked_scenarios else None,
            "scenario_ranking": [name for name, _ in ranked_scenarios]
        }
        
    def _calculate_location_scores(self, employee: Employee, team: Optional[Team],
                                 date: datetime, day_name: str) -> Dict[WorkLocation, float]:
        """Calculate location preference scores for a specific day"""
        scores = {}
        
        # Base productivity scores
        for location in WorkLocation:
            base_score = employee.productivity_metrics.get(location, 0.7)
            
            # Adjust for team collaboration needs
            if team and day_name in team.required_collaboration_days:
                if location == WorkLocation.OFFICE:
                    base_score += 0.2  # Boost office score for collaboration days
                else:
                    base_score -= 0.1  # Reduce remote scores
                    
            # Adjust for employee preferences
            if day_name in employee.preferred_work_days:
                if location == WorkLocation.HOME:
                    base_score += 0.1
                    
            scores[location] = max(0, min(1, base_score))
            
        return scores
        
    def _select_optimal_location(self, location_scores: Dict[WorkLocation, float],
                               office_days_assigned: int, min_office_days: int,
                               day_index: int) -> WorkLocation:
        """Select optimal location considering constraints"""
        # Force office days if minimum not met and running out of days
        remaining_days = 5 - day_index
        office_days_needed = min_office_days - office_days_assigned
        
        if office_days_needed > 0 and remaining_days <= office_days_needed:
            return WorkLocation.OFFICE
            
        # Otherwise, select highest scoring location
        return max(location_scores.items(), key=lambda x: x)[0]
        
    def _get_recommended_hours(self, employee: Employee, location: WorkLocation) -> Dict[str, str]:
        """Get recommended working hours for location"""
        if location == WorkLocation.HOME and self.policies["flexible_hours_allowed"]:
            return {"start": "flexible", "end": "flexible", "core_hours": "10:00-15:00"}
        else:
            return {"start": "09:00", "end": "17:00", "core_hours": "09:00-17:00"}
            
    def _schedule_focus_time(self, location: WorkLocation) -> List[Dict[str, str]]:
        """Schedule focused work blocks"""
        if location == WorkLocation.HOME:
            return [
                {"start": "09:00", "end": "11:00", "type": "deep_work"},
                {"start": "14:00", "end": "16:00", "type": "creative_work"}
            ]
        else:
            return [
                {"start": "09:00", "end": "10:30", "type": "planning"},
                {"start": "15:30", "end": "17:00", "type": "individual_tasks"}
            ]
            
    def _schedule_collaboration_time(self, employee: Employee, team: Optional[Team],
                                   location: WorkLocation) -> List[Dict[str, str]]:
        """Schedule collaboration blocks"""
        if location == WorkLocation.OFFICE:
            return [
                {"start": "10:30", "end": "12:00", "type": "team_sync"},
                {"start": "13:00", "end": "14:00", "type": "brainstorming"},
                {"start": "16:00", "end": "17:00", "type": "stakeholder_meetings"}
            ]
        else:
            return [
                {"start": "11:00", "end": "12:00", "type": "virtual_standup"},
                {"start": "16:00", "end": "17:00", "type": "client_calls"}
            ]
            
    def _calculate_schedule_score(self, schedule: Dict, employee: Employee, team: Optional[Team]) -> float:
        """Calculate optimization score for schedule"""
        scores = []
        
        for day_data in schedule.values():
            location = WorkLocation(day_data["location"])
            location_score = employee.productivity_metrics.get(location, 0.7)
            scores.append(location_score)
            
        return np.mean(scores)
        
    def _generate_optimization_recommendations(self, sessions: List[WorkSession]) -> List[str]:
        """Generate recommendations based on session analysis"""
        recommendations = []
        
        # Analyze productivity patterns
        home_productivity = np.mean([s.productivity_score for s in sessions if s.location == WorkLocation.HOME])
        office_productivity = np.mean([s.productivity_score for s in sessions if s.location == WorkLocation.OFFICE])
        
        if home_productivity > office_productivity * 1.1:
            recommendations.append("Consider increasing remote work allocation for improved productivity")
        elif office_productivity > home_productivity * 1.1:
            recommendations.append("Office work shows higher productivity - consider more in-person time")
            
        # Analyze collaboration patterns
        avg_collab_time = np.mean([s.collaboration_time for s in sessions])
        if avg_collab_time < 2.0:
            recommendations.append("Increase structured collaboration time to improve team cohesion")
            
        return recommendations
        
    # Simulation helper methods
    def _simulate_productivity(self, employee: Employee, office_days: int, 
                             flexible_hours: bool, focus_blocks: int) -> float:
        """Simulate productivity score for scenario"""
        base_score = 0.75
        
        # Office/remote balance impact
        optimal_office_ratio = 0.6  # 3 days out of 5
        actual_ratio = office_days / 5
        balance_penalty = abs(optimal_office_ratio - actual_ratio) * 0.2
        
        # Flexibility bonus
        flexibility_bonus = 0.1 if flexible_hours else 0
        
        # Focus time bonus
        focus_bonus = min(focus_blocks * 0.05, 0.15)
        
        return min(1.0, base_score - balance_penalty + flexibility_bonus + focus_bonus)
        
    def _simulate_satisfaction(self, employee: Employee, office_days: int, flexible_hours: bool) -> float:
        """Simulate employee satisfaction score"""
        base_satisfaction = employee.well_being_index
        
        # Flexibility strongly impacts satisfaction
        if flexible_hours:
            base_satisfaction += 0.15
            
        # Commute frequency impact
        commute_penalty = (office_days - 2) * 0.05  # Penalty increases with more office days
        
        return max(0, min(1, base_satisfaction - commute_penalty))
        
    def _simulate_collaboration(self, employee: Employee, office_days: int) -> float:
        """Simulate collaboration effectiveness"""
        base_collaboration = employee.collaboration_score
        
        # More office days generally improve collaboration
        office_bonus = min(office_days * 0.05, 0.2)
        
        return min(1.0, base_collaboration + office_bonus)
        
    def _calculate_overall_scenario_score(self, productivity: float, satisfaction: float, collaboration: float) -> float:
        """Calculate weighted overall score for scenario"""
        weights = self.optimization_weights
        
        overall_score = (
            productivity * weights["productivity"] +
            satisfaction * weights["employee_satisfaction"] +
            collaboration * weights["collaboration"] +
            0.8 * weights["cost_efficiency"]  # Baseline cost efficiency
        )
        
        return overall_score

# Example usage and demonstration
def run_hybrid_work_demo():
    print("=== Hybrid Work Optimization Framework Demo ===")
    
    # Initialize optimizer
    optimizer = HybridWorkOptimizer("TechCorp Inc.")
    
    # Register employees
    employees = [
        Employee(
            employee_id="emp001",
            name="Sarah Johnson",
            role="Software Engineer",
            team="Backend Development",
            home_location="San Francisco",
            office_location="SF Downtown",
            preferred_work_days=["Monday", "Wednesday", "Friday"],
            productivity_metrics={
                WorkLocation.OFFICE: 0.85,
                WorkLocation.HOME: 0.90,
                WorkLocation.COWORKING: 0.75
            },
            collaboration_score=0.85,
            well_being_index=0.8
        ),
        Employee(
            employee_id="emp002",
            name="Mike Chen",
            role="Product Manager",
            team="Product Strategy",
            home_location="Palo Alto",
            office_location="SF Downtown", 
            preferred_work_days=["Tuesday", "Thursday"],
            productivity_metrics={
                WorkLocation.OFFICE: 0.90,
                WorkLocation.HOME: 0.75,
                WorkLocation.COWORKING: 0.80
            },
            collaboration_score=0.95,
            well_being_index=0.7
        )
    ]
    
    for employee in employees:
        optimizer.register_employee(employee)
        
    # Register teams
    teams = [
        Team(
            team_id="team001",
            name="Backend Development",
            members=["emp001"],
            required_collaboration_days=["Tuesday", "Thursday"]
        ),
        Team(
            team_id="team002",
            name="Product Strategy",
            members=["emp002"],
            required_collaboration_days=["Monday", "Wednesday", "Friday"]
        )
    ]
    
    for team in teams:
        optimizer.register_team(team)
        
    print(f"\nRegistered {len(employees)} employees and {len(teams)} teams")
    
    # Generate optimized schedule
    print("\n=== Weekly Schedule Optimization ===")
    week_start = datetime(2025, 9, 1)  # Monday
    
    for employee in employees:
        schedule = optimizer.optimize_weekly_schedule(week_start, employee.employee_id)
        print(f"\n{employee.name} - Optimized Schedule:")
        print(f"Office Days: {schedule['office_days']}, Remote Days: {schedule['remote_days']}")
        print(f"Optimization Score: {schedule['optimization_score']:.2f}")
        
        for day, details in schedule['schedule'].items():
            print(f"  {day}: {details['location']} (Score: {details['location_scores'][details['location']]:.2f})")
    
    # Simulate work sessions
    print("\n=== Work Session Simulation ===")
    
    # Generate sample work sessions
    for i in range(20):
        session = WorkSession(
            session_id=f"session_{i+1}",
            employee_id=np.random.choice(["emp001", "emp002"]),
            date=datetime.now() - timedelta(days=np.random.randint(0, 30)),
            location=np.random.choice(list(WorkLocation)),
            duration_hours=np.random.uniform(6, 9),
            productivity_score=np.random.uniform(0.6, 0.95),
            tasks_completed=np.random.randint(3, 8),
            meetings_attended=np.random.randint(1, 5),
            collaboration_time=np.random.uniform(1, 4),
            focus_time=np.random.uniform(2, 6)
        )
        optimizer.track_work_session(session)
        
    print(f"Tracked {len(optimizer.work_sessions)} work sessions")
    
    # Analyze hybrid effectiveness
    print("\n=== Hybrid Work Effectiveness Analysis ===")
    analysis = optimizer.analyze_hybrid_effectiveness()
    
    print(f"Analysis Period: {analysis['analysis_period']['total_sessions']} sessions")
    print("\nProductivity by Location:")
    for location, productivity in analysis['productivity_by_location'].items():
        print(f"  {location}: {productivity:.2f}")
        
    print("\nLocation Usage:")
    for location, count in analysis['location_usage'].items():
        print(f"  {location}: {count} sessions")
        
    print("\nRecommendations:")
    for i, rec in enumerate(analysis['recommendations'], 1):
        print(f"  {i}. {rec}")
    
    # Scenario simulation
    print("\n=== Scenario Simulation ===")
    scenarios = [
        {"name": "3-2 Split", "office_days_per_week": 3, "flexible_hours": True, "focus_time_blocks": 2},
        {"name": "4-1 Split", "office_days_per_week": 4, "flexible_hours": False, "focus_time_blocks": 1},
        {"name": "2-3 Split", "office_days_per_week": 2, "flexible_hours": True, "focus_time_blocks": 3}
    ]
    
    scenario_results = optimizer.simulate_schedule_scenarios("emp001", scenarios)
    
    print(f"Scenario Analysis for {employees[0].name}:")
    print(f"Recommended Scenario: {scenario_results['recommended_scenario']}")
    
    for scenario_name in scenario_results['scenario_ranking']:
        scenario = scenario_results['scenario_results'][scenario_name]
        print(f"\n{scenario_name}:")
        print(f"  Office Days: {scenario['office_days_per_week']}")
        print(f"  Productivity: {scenario['predicted_productivity']:.2f}")
        print(f"  Satisfaction: {scenario['predicted_satisfaction']:.2f}")
        print(f"  Overall Score: {scenario['overall_score']:.2f}")
    
    return optimizer

# Run demonstration
if __name__ == "__main__":
    demo_optimizer = run_hybrid_work_demo()