Supply Chain Optimization for Sustainability

Module: Sustainable Tech | Difficulty: Premium

Carbon Footprint in Supply Chain

Multi-Objective Optimization

Comparison

Python Implementation

import numpy as np
from scipy.optimize import linprog

class GreenSupplyChainOptimizer:
    def optimize_transportation(self, costs, demands):
        n = len(demands)
        c = costs
        A_eq = np.eye(n)
        result = linprog(c, A_eq=A_eq, b_eq=demands, bounds=[(0, None)] * n, method='highs')
        return result.x

    def carbon_optimized_routing(self, distance_matrix, emission_factors):
        n = distance_matrix.shape[0]
        routes = []
        remaining = list(range(1, n))
        while remaining:
            route = [0]
            while remaining:
                last = route[-1]
                nearest = min(remaining, key=lambda x: distance_matrix[last, x] * emission_factors[x])
                route.append(nearest)
                remaining.remove(nearest)
            route.append(0)
            routes.append(route)
        return routes

Research Insight: Multi-objective evolutionary algorithms can reduce emissions by 30-40% with only 5-10% cost increase.