Green Port Operations and Shipping Route Optimization

Module: Sustainable Tech | Difficulty: Premium

Shore Power Formula

Speed Optimization

Halving speed reduces fuel consumption by 87.5%.

Comparison

Python Implementation

import numpy as np
from scipy.optimize import minimize

class PortOptimizer:
    def __init__(self, n_berths, n_cranes):
        self.berths = n_berths
        self.cranes = n_cranes

    def berth_allocation(self, vessels, distances):
        n_v = len(vessels)
        n_b = self.berths
        assignment = np.zeros((n_v, n_b), dtype=int)
        for i, v in enumerate(vessels):
            best_berth = np.argmin(distances[i])
            assignment[i, best_berth] = 1
        return assignment

    def speed_optimization(self, distance, fuel_price, time_value, max_speed=20):
        def total_cost(speed):
            fuel = 0.5 * speed**3 * fuel_price * distance / (speed * 3600)
            time_cost = time_value * distance / (speed * 3600)
            return fuel + time_cost
        result = minimize(total_cost, 15, bounds=[(5, max_speed)])
        return result.x[0]

    def shore_power_scheduling(self, vessels, grid_carbon_intensity, shore_ci):
        schedule = []
        for v in vessels:
            if shore_ci < grid_carbon_intensity * 0.7:
                schedule.append({'vessel': v['id'], 'use_shore': True, 'savings': v['fuel'] * 0.4})
            else:
                schedule.append({'vessel': v['id'], 'use_shore': False})
        return schedule

Research Insight: Shore power and cold ironing reduce port-side emissions by 40-60% while improving air quality in nearby communities.