import numpy as np
import pandas as pd
class CircularityCalculator:
    """Calculate circularity metrics for products and systems"""
    def calculate_mci(self, virgin_input, recycled_input, product_mass,
                     waste_generated, product_lifespan_actual,
                     product_lifespan_industry_avg):
        """
        Calculate Material Circularity Indicator (Ellen MacArthur Foundation)
        MCI ranges from 0 (linear) to 1 (fully circular)
        Parameters:
        - virgin_input: kg of virgin material input
        - recycled_input: kg of recycled material input
        - product_mass: kg of final product
        - waste_generated: kg of waste in production + end-of-life
        - product_lifespan_actual: years product is used
        - product_lifespan_industry_avg: years average for product category
        """
        total_input = virgin_input + recycled_input
        # Linear Flow Index (LFI) - measures material losses
        # LFI = (Virgin input + Waste) / (2 × Product mass)
        lfi = (virgin_input + waste_generated) / (2 * product_mass) if product_mass > 0 else 1
        # Utility factor - accounts for product lifespan
        # Longer life = better circularity
        utility_factor = product_lifespan_actual / product_lifespan_industry_avg
        utility_factor = min(utility_factor, 5)  # Cap at 5x industry average
        # MCI calculation
        mci = (1 - lfi) * utility_factor
        # Ensure MCI is between 0 and 1
        mci = max(0, min(1, mci))
        return {
            'mci': round(mci, 3),
            'lfi': round(lfi, 3),
            'utility_factor': round(utility_factor, 2),
            'virgin_content_pct': round(virgin_input / total_input * 100, 1) if total_input > 0 else 0,
            'recycled_content_pct': round(recycled_input / total_input * 100, 1) if total_input > 0 else 0,
            'circularity_level': self._classify_mci(mci)
        }
    def _classify_mci(self, mci):
        """Classify circularity level"""
        if mci >= 0.9:
            return 'Highly Circular'
        elif mci >= 0.7:
            return 'Circular'
        elif mci >= 0.5:
            return 'Moderately Circular'
        elif mci >= 0.3:
            return 'Somewhat Circular'
        else:
            return 'Linear'
    def calculate_circularity_rate(self, cycled_materials, total_material_flow):
        """
        Calculate Circularity Rate
        Percentage of materials that are cycled back into the economy
        """
        circularity_rate = (cycled_materials / total_material_flow * 100) if total_material_flow > 0 else 0
        return {
            'circularity_rate_pct': round(circularity_rate, 1),
            'cycled_materials': cycled_materials,
            'total_material_flow': total_material_flow,
            'linear_materials': total_material_flow - cycled_materials
        }
    def calculate_r_strategies_impact(self, product_value, r_strategy,
                                     value_retention_rate):
        """
        Calculate value retention for different R-strategies
        R-strategies (9R framework):
        R0: Refuse, R1: Rethink, R2: Reduce
        R3: Reuse, R4: Repair, R5: Refurbish
        R6: Remanufacture, R7: Repurpose, R8: Recycle, R9: Recover
        """
        # Value retention rates by strategy (typical)
        retention_rates = {
            'refuse': 1.0,       # Prevent need
            'rethink': 1.0,      # Product-as-service
            'reduce': 1.0,       # More efficient use
            'reuse': 0.95,       # Direct reuse
            'repair': 0.90,      # Fix for same use
            'refurbish': 0.85,   # Restore to good condition
            'remanufacture': 0.80,  # Disassemble and rebuild
            'repurpose': 0.60,   # Different application
            'recycle': 0.40,     # Material recovery
            'recover': 0.20      # Energy recovery
        }
        default_retention = retention_rates.get(r_strategy, 0.5)
        actual_retention = value_retention_rate if value_retention_rate else default_retention
        retained_value = product_value * actual_retention
        return {
            'r_strategy': r_strategy,
            'original_value': product_value,
            'retention_rate': actual_retention,
            'retained_value': round(retained_value, 2),
            'value_lost': round(product_value - retained_value, 2)
        }
# Example usage
calculator = CircularityCalculator()
# Example 1: Calculate MCI for a product
mci_result = calculator.calculate_mci(
    virgin_input=8.0,       # kg virgin materials
    recycled_input=2.0,     # kg recycled materials
    product_mass=10.0,      # kg final product
    waste_generated=1.5,    # kg waste (production + end-of-life)
    product_lifespan_actual=8,  # years
    product_lifespan_industry_avg=5  # years
)
print("Material Circularity Indicator (MCI):")
print(f"  MCI Score: {mci_result['mci']}")
print(f"  Circularity Level: {mci_result['circularity_level']}")
print(f"  Virgin Content: {mci_result['virgin_content_pct']}%")
print(f"  Recycled Content: {mci_result['recycled_content_pct']}%")
print(f"  Utility Factor: {mci_result['utility_factor']}")
# Example 2: Circularity rate
circ_rate = calculator.calculate_circularity_rate(
    cycled_materials=3000,      # tonnes recycled/reused
    total_material_flow=10000   # tonnes total materials used
)
print(f"\nCircularity Rate: {circ_rate['circularity_rate_pct']}%")
# Example 3: R-strategy value retention
repair_value = calculator.calculate_r_strategies_impact(
    product_value=500,
    r_strategy='repair',
    value_retention_rate=0.90
)
print(f"\nRepair Strategy:")
print(f"  Original Value: ${repair_value['original_value']}")
print(f"  Retained Value: ${repair_value['retained_value']}")
print(f"  Value Retention Rate: {repair_value['retention_rate']*100}%")

class CircularDesignAssessment:
    """Assess product design for circularity"""
    def __init__(self, product_name):
        self.product_name = product_name
        self.assessment_criteria = {}
    def assess_design_for_disassembly(self, design_data):
        """
        Assess how easy product is to disassemble
        design_data: dict with design characteristics
        """
        score = 0
        max_score = 100
        feedback = []
        # Fastener type (0-20 points)
        fasteners = design_data.get('fasteners', 'permanent')
        if fasteners == 'snap_fit_reversible':
            score += 20
            feedback.append("✓ Reversible snap-fit fasteners")
        elif fasteners == 'screws_standard':
            score += 15
            feedback.append("✓ Standard screws used")
        elif fasteners == 'screws_proprietary':
            score += 10
            feedback.append("⚠ Proprietary fasteners (use standard)")
        else:
            score += 0
            feedback.append("✗ Permanent fasteners (welding, glue)")
        # Material variety (0-20 points)
        num_materials = design_data.get('num_material_types', 5)
        if num_materials <= 2:
            score += 20
            feedback.append("✓ Limited material types (≤2)")
        elif num_materials <= 4:
            score += 15
            feedback.append("⚠ Moderate material variety (3-4 types)")
        else:
            score += 5
            feedback.append("✗ High material variety (simplify)")
        # Material compatibility (0-20 points)
        incompatible_materials = design_data.get('incompatible_material_combos', 0)
        if incompatible_materials == 0:
            score += 20
            feedback.append("✓ No incompatible material combinations")
        elif incompatible_materials <= 2:
            score += 10
            feedback.append("⚠ Some incompatible combinations")
        else:
            score += 0
            feedback.append("✗ Many incompatible combinations")
        # Component labeling (0-15 points)
        if design_data.get('components_labeled', False):
            score += 15
            feedback.append("✓ Components labeled for recycling")
        else:
            score += 0
            feedback.append("✗ Components not labeled")
        # Modular design (0-25 points)
        modularity = design_data.get('modularity_level', 'none')
        if modularity == 'high':
            score += 25
            feedback.append("✓ Highly modular design")
        elif modularity == 'medium':
            score += 15
            feedback.append("⚠ Partially modular")
        else:
            score += 0
            feedback.append("✗ Not modular")
        return {
            'score': score,
            'max_score': max_score,
            'percentage': round(score / max_score * 100, 1),
            'rating': self._get_rating(score / max_score),
            'feedback': feedback
        }
    def assess_material_selection(self, material_data):
        """Assess material choices for circularity"""
        score = 0
        max_score = 100
        feedback = []
        # Recycled content (0-30 points)
        recycled_pct = material_data.get('recycled_content_pct', 0)
        if recycled_pct >= 50:
            score += 30
            feedback.append(f"✓ High recycled content ({recycled_pct}%)")
        elif recycled_pct >= 25:
            score += 20
            feedback.append(f"⚠ Moderate recycled content ({recycled_pct}%)")
        elif recycled_pct > 0:
            score += 10
            feedback.append(f"⚠ Low recycled content ({recycled_pct}%)")
        else:
            score += 0
            feedback.append("✗ No recycled content")
        # Recyclability (0-30 points)
        recyclability = material_data.get('recyclability', 'difficult')
        if recyclability == 'easily_recyclable':
            score += 30
            feedback.append("✓ Materials easily recyclable")
        elif recyclability == 'recyclable_with_effort':
            score += 20
            feedback.append("⚠ Materials recyclable with effort")
        else:
            score += 5
            feedback.append("✗ Materials difficult to recycle")
        # Renewable materials (0-20 points)
        renewable_pct = material_data.get('renewable_content_pct', 0)
        if renewable_pct >= 50:
            score += 20
            feedback.append(f"✓ High renewable content ({renewable_pct}%)")
        elif renewable_pct >= 25:
            score += 12
            feedback.append(f"⚠ Moderate renewable content ({renewable_pct}%)")
        elif renewable_pct > 0:
            score += 5
            feedback.append(f"⚠ Low renewable content ({renewable_pct}%)")
        # Hazardous substances (0-20 points)
        if material_data.get('hazardous_free', True):
            score += 20
            feedback.append("✓ No hazardous substances")
        else:
            score += 0
            feedback.append("✗ Contains hazardous substances")
            if material_data.get('hazardous_list'):
                feedback.append(f"  Substances: {', '.join(material_data['hazardous_list'])}")
        return {
            'score': score,
            'max_score': max_score,
            'percentage': round(score / max_score * 100, 1),
            'rating': self._get_rating(score / max_score),
            'feedback': feedback
        }
    def assess_durability_repairability(self, durability_data):
        """Assess product durability and ease of repair"""
        score = 0
        max_score = 100
        feedback = []
        # Expected lifespan (0-25 points)
        expected_years = durability_data.get('expected_lifespan_years', 0)
        industry_avg = durability_data.get('industry_avg_lifespan_years', 5)
        if expected_years >= industry_avg * 1.5:
            score += 25
            feedback.append(f"✓ Long lifespan ({expected_years} yrs, 1.5x industry avg)")
        elif expected_years >= industry_avg:
            score += 18
            feedback.append(f"✓ Good lifespan ({expected_years} yrs, meets industry avg)")
        else:
            score += 10
            feedback.append(f"⚠ Below average lifespan ({expected_years} yrs)")
        # Repairability (0-25 points)
        repair_score = durability_data.get('repair_score_out_of_10', 5)
        score += repair_score * 2.5
        if repair_score >= 8:
            feedback.append(f"✓ Highly repairable (score: {repair_score}/10)")
        elif repair_score >= 5:
            feedback.append(f"⚠ Moderately repairable (score: {repair_score}/10)")
        else:
            feedback.append(f"✗ Difficult to repair (score: {repair_score}/10)")
        # Spare parts availability (0-25 points)
        if durability_data.get('spare_parts_available', False):
            score += 25
            commitment_years = durability_data.get('spare_parts_commitment_years', 0)
            feedback.append(f"✓ Spare parts available ({commitment_years} years)")
        else:
            score += 0
            feedback.append("✗ Spare parts not available")
        # Repair documentation (0-25 points)
        if durability_data.get('repair_manual_available', False):
            score += 15
            feedback.append("✓ Repair manual available")
        else:
            feedback.append("✗ No repair manual")
        if durability_data.get('repair_videos_available', False):
            score += 10
            feedback.append("✓ Repair videos available")
        return {
            'score': score,
            'max_score': max_score,
            'percentage': round(score / max_score * 100, 1),
            'rating': self._get_rating(score / max_score),
            'feedback': feedback
        }
    def _get_rating(self, score_ratio):
        """Convert score to rating"""
        if score_ratio >= 0.9:
            return 'Excellent'
        elif score_ratio >= 0.75:
            return 'Good'
        elif score_ratio >= 0.60:
            return 'Fair'
        elif score_ratio >= 0.40:
            return 'Poor'
        else:
            return 'Very Poor'
    def generate_comprehensive_assessment(self, design_data, material_data,
                                          durability_data):
        """Generate full circularity assessment"""
        disassembly = self.assess_design_for_disassembly(design_data)
        materials = self.assess_material_selection(material_data)
        durability = self.assess_durability_repairability(durability_data)
        # Overall score (weighted average)
        overall_score = (
            disassembly['percentage'] * 0.35 +
            materials['percentage'] * 0.35 +
            durability['percentage'] * 0.30
        )
        return {
            'product': self.product_name,
            'overall_score': round(overall_score, 1),
            'overall_rating': self._get_rating(overall_score / 100),
            'design_for_disassembly': disassembly,
            'material_selection': materials,
            'durability_repairability': durability
        }
# Example assessment
product = CircularDesignAssessment('Acme Widget Pro')
design_data = {
    'fasteners': 'screws_standard',
    'num_material_types': 3,
    'incompatible_material_combos': 0,
    'components_labeled': True,
    'modularity_level': 'high'
}
material_data = {
    'recycled_content_pct': 60,
    'recyclability': 'easily_recyclable',
    'renewable_content_pct': 20,
    'hazardous_free': True
}
durability_data = {
    'expected_lifespan_years': 10,
    'industry_avg_lifespan_years': 6,
    'repair_score_out_of_10': 8,
    'spare_parts_available': True,
    'spare_parts_commitment_years': 10,
    'repair_manual_available': True,
    'repair_videos_available': True
}
assessment = product.generate_comprehensive_assessment(
    design_data, material_data, durability_data
)
print(f"Circular Design Assessment: {assessment['product']}")
print(f"Overall Score: {assessment['overall_score']}/100 ({assessment['overall_rating']})")
print(f"\nDesign for Disassembly: {assessment['design_for_disassembly']['percentage']}%")
print(f"Material Selection: {assessment['material_selection']['percentage']}%")
print(f"Durability & Repairability: {assessment['durability_repairability']['percentage']}%")

class ReverseLogisticsOptimizer:
    """Optimize reverse logistics and product take-back operations"""
    def __init__(self):
        self.collection_sites = []
        self.processing_facilities = []
    def calculate_collection_coverage(self, collection_sites, customer_locations,
                                     max_distance_km=50):
        """
        Calculate coverage of collection network
        collection_sites: list of dicts with lat/lon
        customer_locations: list of dicts with lat/lon
        """
        def haversine_distance(lat1, lon1, lat2, lon2):
            """Calculate distance between two points on Earth"""
            from math import radians, sin, cos, sqrt, atan2
            R = 6371  # Earth radius in km
            lat1, lon1, lat2, lon2 = map(radians, [lat1, lon1, lat2, lon2])
            dlat = lat2 - lat1
            dlon = lon2 - lon1
            a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
            c = 2 * atan2(sqrt(a), sqrt(1-a))
            return R * c
        covered_customers = 0
        for customer in customer_locations:
            cust_lat = customer['lat']
            cust_lon = customer['lon']
            # Check if any collection site is within max distance
            for site in collection_sites:
                distance = haversine_distance(
                    cust_lat, cust_lon,
                    site['lat'], site['lon']
                )
                if distance <= max_distance_km:
                    covered_customers += 1
                    break  # Customer is covered
        coverage_pct = (covered_customers / len(customer_locations) * 100) if customer_locations else 0
        return {
            'total_customers': len(customer_locations),
            'covered_customers': covered_customers,
            'coverage_percentage': round(coverage_pct, 1),
            'uncovered_customers': len(customer_locations) - covered_customers,
            'max_distance_km': max_distance_km
        }
    def calculate_return_rate_economics(self, product_data):
        """
        Calculate economics of product returns and refurbishment
        product_data: dict with product and return info
        """
        original_price = product_data['original_price']
        units_sold = product_data['units_sold']
        return_rate = product_data['return_rate']  # fraction (e.g., 0.15 for 15%)
        # Returns
        units_returned = units_sold * return_rate
        # Collection costs
        collection_cost_per_unit = product_data.get('collection_cost_per_unit', 5)
        collection_cost_total = units_returned * collection_cost_per_unit
        # Sorting and grading
        sorting_cost_per_unit = product_data.get('sorting_cost_per_unit', 2)
        sorting_cost_total = units_returned * sorting_cost_per_unit
        # Categorize returns
        grade_a_rate = product_data.get('grade_a_rate', 0.30)  # Resell as-is
        grade_b_rate = product_data.get('grade_b_rate', 0.40)  # Refurbish
        grade_c_rate = product_data.get('grade_c_rate', 0.20)  # Parts recovery
        grade_d_rate = product_data.get('grade_d_rate', 0.10)  # Recycle/dispose
        units_grade_a = units_returned * grade_a_rate
        units_grade_b = units_returned * grade_b_rate
        units_grade_c = units_returned * grade_c_rate
        units_grade_d = units_returned * grade_d_rate
        # Grade A: Resell as-is
        resale_price_a = original_price * 0.70  # 70% of original
        cleaning_cost_a = 3
        revenue_a = units_grade_a * (resale_price_a - cleaning_cost_a)
        # Grade B: Refurbish
        refurb_cost_b = original_price * 0.20  # 20% of original to refurbish
        resale_price_b = original_price * 0.60  # 60% of original
        revenue_b = units_grade_b * (resale_price_b - refurb_cost_b)
        # Grade C: Parts recovery
        parts_value_c = original_price * 0.30  # 30% parts value
        disassembly_cost_c = original_price * 0.10
        revenue_c = units_grade_c * (parts_value_c - disassembly_cost_c)
        # Grade D: Material recycling
        material_value_d = original_price * 0.05  # 5% material value
        processing_cost_d = original_price * 0.03
        revenue_d = units_grade_d * (material_value_d - processing_cost_d)
        # Total
        total_revenue = revenue_a + revenue_b + revenue_c + revenue_d
        total_cost = collection_cost_total + sorting_cost_total
        net_value = total_revenue - total_cost
        return {
            'units_returned': round(units_returned, 0),
            'return_rate_pct': round(return_rate * 100, 1),
            'distribution': {
                'grade_a_resell': round(units_grade_a, 0),
                'grade_b_refurbish': round(units_grade_b, 0),
                'grade_c_parts': round(units_grade_c, 0),
                'grade_d_recycle': round(units_grade_d, 0)
            },
            'revenue_by_grade': {
                'grade_a': round(revenue_a, 2),
                'grade_b': round(revenue_b, 2),
                'grade_c': round(revenue_c, 2),
                'grade_d': round(revenue_d, 2),
                'total': round(total_revenue, 2)
            },
            'costs': {
                'collection': round(collection_cost_total, 2),
                'sorting': round(sorting_cost_total, 2),
                'total': round(total_cost, 2)
            },
            'net_value': round(net_value, 2),
            'value_per_returned_unit': round(net_value / units_returned, 2) if units_returned > 0 else 0
        }
    def optimize_refurbishment_capacity(self, demand_data):
        """
        Optimize refurbishment facility capacity
        demand_data: dict with demand forecasts and cost info
        """
        # Demand parameters
        avg_monthly_returns = demand_data['avg_monthly_returns']
        returns_std_dev = demand_data['returns_std_dev']
        refurb_rate = demand_data.get('refurb_rate', 0.40)  # 40% need refurbishment
        refurb_demand_avg = avg_monthly_returns * refurb_rate
        refurb_demand_std = returns_std_dev * refurb_rate
        # Capacity options
        capacity_options = []
        for capacity_level in [0.8, 1.0, 1.2, 1.5, 2.0]:
            capacity = refurb_demand_avg * capacity_level
            # Fixed costs (facility, equipment, base staffing)
            fixed_cost_monthly = demand_data.get('fixed_cost_base', 50000) * capacity_level
            # Variable costs (per unit processed)
            variable_cost_per_unit = demand_data.get('variable_cost_per_unit', 20)
            # Calculate expected utilization
            # Assumes normal distribution of demand
            from scipy import stats
            prob_exceed = 1 - stats.norm.cdf(capacity, refurb_demand_avg, refurb_demand_std)
            utilization = min(1.0, refurb_demand_avg / capacity)
            # Units processed
            units_processed = min(capacity, refurb_demand_avg)
            # Backlog cost (if demand exceeds capacity)
            backlog_units = max(0, refurb_demand_avg - capacity)
            backlog_cost_per_unit = demand_data.get('backlog_cost_per_unit', 50)
            backlog_cost = backlog_units * backlog_cost_per_unit
            # Total cost
            total_cost = fixed_cost_monthly + (units_processed * variable_cost_per_unit) + backlog_cost
            cost_per_unit = total_cost / units_processed if units_processed > 0 else float('inf')
            capacity_options.append({
                'capacity_level': capacity_level,
                'capacity_units': round(capacity, 0),
                'expected_utilization': round(utilization * 100, 1),
                'units_processed': round(units_processed, 0),
                'backlog_units': round(backlog_units, 0),
                'fixed_cost': round(fixed_cost_monthly, 2),
                'variable_cost': round(units_processed * variable_cost_per_unit, 2),
                'backlog_cost': round(backlog_cost, 2),
                'total_cost': round(total_cost, 2),
                'cost_per_unit': round(cost_per_unit, 2)
            })
        # Find optimal (lowest cost per unit)
        optimal = min(capacity_options, key=lambda x: x['cost_per_unit'])
        return {
            'capacity_options': pd.DataFrame(capacity_options),
            'optimal_capacity': optimal,
            'recommendation': f"Build capacity for {optimal['capacity_level']}x average demand"
        }
# Example usage
rl_optimizer = ReverseLogisticsOptimizer()
# Example 1: Calculate return economics
product_data = {
    'original_price': 500,
    'units_sold': 10000,
    'return_rate': 0.12,  # 12% return rate
    'collection_cost_per_unit': 10,
    'sorting_cost_per_unit': 3,
    'grade_a_rate': 0.25,  # 25% can resell as-is
    'grade_b_rate': 0.45,  # 45% need refurbishment
    'grade_c_rate': 0.20,  # 20% for parts
    'grade_d_rate': 0.10   # 10% recycle
}
economics = rl_optimizer.calculate_return_rate_economics(product_data)
print("Return Economics:")
print(f"  Units Returned: {economics['units_returned']}")
print(f"  Total Revenue: ${economics['revenue_by_grade']['total']:,.2f}")
print(f"  Total Costs: ${economics['costs']['total']:,.2f}")
print(f"  Net Value: ${economics['net_value']:,.2f}")
print(f"  Value per Unit: ${economics['value_per_returned_unit']:.2f}")
# Example 2: Optimize refurbishment capacity
demand_data = {
    'avg_monthly_returns': 500,
    'returns_std_dev': 100,
    'refurb_rate': 0.45,
    'fixed_cost_base': 40000,
    'variable_cost_per_unit': 25,
    'backlog_cost_per_unit': 60
}
capacity_analysis = rl_optimizer.optimize_refurbishment_capacity(demand_data)
print("\n\nRefurbishment Capacity Options:")
print(capacity_analysis['capacity_options'][['capacity_level', 'capacity_units', 'expected_utilization', 'cost_per_unit']])
print(f"\n{capacity_analysis['recommendation']}")