cruise-supply-chain

Cruise Supply Chain

You are an expert in cruise ship supply chain management and maritime logistics. Your goal is to help optimize the complex provisioning, inventory management, and logistics for cruise vessels, ensuring passenger satisfaction while managing costs, storage constraints, and port operations.

Initial Assessment

Before optimizing cruise supply chain, understand:

1. Vessel & Fleet Profile

   • Fleet size and vessel types?
   • Passenger capacity and crew size?
   • Storage capacity (dry, cold, frozen)?
   • Galley and food service capabilities?

2. Itinerary & Operations

   • Route structure? (Caribbean, Mediterranean, Alaska, world cruise)
   • Port rotation and frequency?
   • Days at sea vs. in port?
   • Seasonal variations?

3. Current Supply Chain

   • Provisioning frequency and locations?
   • Supplier network? (global, regional)
   • Inventory management system?
   • Cold chain capabilities?

4. Objectives & Challenges

   • Primary goals? (cost, quality, waste reduction)
   • Current pain points? (stockouts, waste, costs)
   • Sustainability targets?
   • Guest satisfaction metrics?

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Cruise Supply Chain Framework

Supply Chain Components

Food & Beverage:

• Fresh produce (fruits, vegetables)
• Proteins (beef, poultry, seafood)
• Dairy products
• Dry goods and pantry items
• Beverages (alcoholic and non-alcoholic)
• Specialty items and ingredients

Hotel Operations:

• Linens and towels
• Guest amenities (toiletries, etc.)
• Cleaning supplies
• Cabin supplies

Technical & Maintenance:

• Spare parts
• Fuel and lubricants
• Technical supplies
• Safety equipment

Entertainment & Recreation:

• Shore excursion supplies
• Entertainment equipment
• Retail merchandise

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Provisioning Planning & Optimization

Multi-Port Provisioning Strategy

import numpy as np
import pandas as pd
from pulp import *

class CruiseProvisioningOptimizer:
    """
    Optimize cruise ship provisioning across multiple ports

    Balance costs, storage capacity, and quality
    """

    def __init__(self, vessel_capacity, itinerary):
        self.vessel_capacity = vessel_capacity  # storage capacity by type
        self.itinerary = itinerary  # list of port calls

    def optimize_provisioning_schedule(self, item_requirements, port_costs,
                                      port_availability):
        """
        Determine what to purchase at each port to minimize total cost

        Parameters:
        - item_requirements: dict of {item: daily_consumption}
        - port_costs: dict of {(port, item): cost_per_unit}
        - port_availability: dict of {(port, item): available_quantity}
        """

        prob = LpProblem("Cruise_Provisioning", LpMinimize)

        items = list(item_requirements.keys())
        ports = [port['name'] for port in self.itinerary]

        # Variables: quantity of item i purchased at port p
        purchase = {}

        for port in ports:
            for item in items:
                if (port, item) in port_costs:
                    purchase[port, item] = LpVariable(
                        f"Purchase_{port}_{item}",
                        lowBound=0
                    )

        # Objective: minimize total procurement cost
        total_cost = lpSum([purchase[port, item] * port_costs.get((port, item), 999999)
                           for port in ports
                           for item in items
                           if (port, item) in purchase])

        prob += total_cost

        # Constraints

        # Meet demand for full voyage
        voyage_days = sum([port['days_until_next'] for port in self.itinerary])

        for item in items:
            total_required = item_requirements[item] * voyage_days

            total_purchased = lpSum([purchase.get((port, item), 0)
                                    for port in ports])

            prob += total_purchased >= total_required

        # Storage capacity constraints at each port
        for p, port in enumerate(self.itinerary):
            # Remaining voyage days from this port
            remaining_days = sum([self.itinerary[i]['days_until_next']
                                 for i in range(p, len(self.itinerary))])

            # Storage at this port = purchases at this port + previous inventory
            # (Simplified model - actual would track consumption)

            for storage_type in ['dry', 'cold', 'frozen']:
                items_this_type = [i for i in items
                                  if item_requirements[i].get('storage_type') == storage_type]

                # Total storage used
                storage_used = lpSum([purchase.get((port['name'], item), 0) *
                                    item_requirements[item].get('volume_per_unit', 1)
                                    for item in items_this_type])

                prob += storage_used <= self.vessel_capacity[storage_type]

        # Port availability limits
        for port in ports:
            for item in items:
                if (port, item) in port_availability:
                    if (port, item) in purchase:
                        prob += purchase[port, item] <= port_availability[port, item]

        # Solve
        prob.solve(PULP_CBC_CMD(msg=0))

        # Extract provisioning schedule
        schedule = []

        for port in ports:
            port_orders = []
            port_cost = 0

            for item in items:
                if (port, item) in purchase and purchase[port, item].varValue > 0.1:
                    quantity = purchase[port, item].varValue
                    cost = quantity * port_costs.get((port, item), 0)

                    port_orders.append({
                        'item': item,
                        'quantity': quantity,
                        'unit_cost': port_costs.get((port, item), 0),
                        'total_cost': cost
                    })

                    port_cost += cost

            if port_orders:
                schedule.append({
                    'port': port,
                    'orders': port_orders,
                    'total_port_cost': port_cost
                })

        return {
            'status': LpStatus[prob.status],
            'total_cost': value(prob.objective),
            'provisioning_schedule': schedule
        }

    def calculate_food_requirements(self, passenger_count, crew_count,
                                   voyage_days, menu_plan):
        """
        Calculate food and beverage requirements based on passenger load
        and menu planning
        """

        requirements = {}

        # Per-person-per-day consumption rates
        consumption_rates = {
            'beef': 0.25,  # kg
            'chicken': 0.20,
            'seafood': 0.15,
            'vegetables': 0.30,
            'fruits': 0.25,
            'dairy_milk': 0.15,  # liters
            'bread': 0.15,  # kg
            'wine': 0.10,  # liters
            'beer': 0.20,  # liters
            'soft_drinks': 0.30  # liters
        }

        total_pax = passenger_count + crew_count

        for item, rate_per_day in consumption_rates.items():
            daily_consumption = rate_per_day * total_pax

            # Add safety factor
            safety_factor = 1.15

            requirements[item] = {
                'daily_consumption': daily_consumption * safety_factor,
                'total_voyage': daily_consumption * safety_factor * voyage_days
            }

        return requirements

# Example usage
vessel_capacity = {
    'dry': 500,  # cubic meters
    'cold': 300,
    'frozen': 200
}

itinerary = [
    {'name': 'Miami', 'days_until_next': 3},
    {'name': 'Cozumel', 'days_until_next': 2},
    {'name': 'Grand Cayman', 'days_until_next': 2},
    {'name': 'Miami', 'days_until_next': 0}
]

optimizer = CruiseProvisioningOptimizer(vessel_capacity, itinerary)

item_requirements = {
    'beef': {'daily_consumption': 500, 'storage_type': 'frozen', 'volume_per_unit': 0.001},
    'chicken': {'daily_consumption': 400, 'storage_type': 'frozen', 'volume_per_unit': 0.001},
    'vegetables': {'daily_consumption': 600, 'storage_type': 'cold', 'volume_per_unit': 0.0015},
    'wine': {'daily_consumption': 200, 'storage_type': 'dry', 'volume_per_unit': 0.001},
}

port_costs = {
    ('Miami', 'beef'): 12.00,
    ('Miami', 'chicken'): 6.00,
    ('Miami', 'vegetables'): 3.00,
    ('Miami', 'wine'): 8.00,
    ('Cozumel', 'beef'): 14.00,
    ('Cozumel', 'vegetables'): 2.50,
    ('Grand Cayman', 'beef'): 15.00,
}

port_availability = {
    ('Miami', 'beef'): 10000,
    ('Miami', 'chicken'): 10000,
    ('Miami', 'vegetables'): 10000,
    ('Miami', 'wine'): 5000,
    ('Cozumel', 'beef'): 2000,
    ('Cozumel', 'vegetables'): 3000,
}

result = optimizer.optimize_provisioning_schedule(item_requirements,
                                                 port_costs,
                                                 port_availability)

print(f"Total provisioning cost: ${result['total_cost']:,.2f}")