mcp-optimizer

# Transportation & Logistics - Usage Examples ## Description Transportation and logistics problems optimize the movement of goods, route planning, and supply chain management to minimize costs and delivery times. ## Example Prompts for LLM ### Example 1: Classic Transportation Problem ``` Help me solve a transportation problem using MCP Optimizer. A company has 3 factories and 4 warehouses: Factories (production in tons): - Factory A: 300 tons - Factory B: 400 tons - Factory C: 500 tons Warehouses (demand in tons): - Warehouse 1: 250 tons - Warehouse 2: 350 tons - Warehouse 3: 300 tons - Warehouse 4: 300 tons Transportation costs ($/ton): From A: [8, 6, 10, 9] From B: [9, 12, 13, 7] From C: [14, 9, 16, 5] Find the transportation plan with minimum costs. ``` ### Example 2: Multi-Product Logistics ``` Use MCP Optimizer to optimize multi-product supply operations. A company supplies 3 types of goods from 2 distribution centers to 4 stores: Products: Electronics, Clothing, Food Distribution Centers (inventory): - DC New York: [500, 800, 1200] units - DC Chicago: [400, 600, 1000] units Stores (demand): - Store 1: [200, 300, 400] units - Store 2: [150, 250, 350] units - Store 3: [300, 400, 500] units - Store 4: [250, 450, 950] units Transportation costs by product ($/unit): Electronics - from NY: [15, 18, 12, 20], from Chicago: [25, 10, 22, 16] Clothing - from NY: [8, 10, 6, 12], from Chicago: [14, 5, 11, 8] Food - from NY: [3, 4, 2, 5], from Chicago: [6, 2, 4, 3] Minimize total logistics costs. ``` ### Example 3: Supply Chain Planning ``` Solve a supply chain planning problem with MCP Optimizer. The supply network includes: - 2 raw material suppliers - 3 manufacturing plants - 4 distribution centers - 5 retail locations Suppliers (capacity tons/month): - Supplier 1: 1000 tons - Supplier 2: 1500 tons Plants (capacity tons/month): - Plant A: 800 tons - Plant B: 900 tons - Plant C: 700 tons Distribution Centers (capacity): - DC 1: 600 tons - DC 2: 500 tons - DC 3: 550 tons - DC 4: 650 tons Retail Locations (demand): - Store 1: 300 tons - Store 2: 250 tons - Store 3: 400 tons - Store 4: 350 tons - Store 5: 200 tons Transportation costs: Suppliers → Plants: [[20, 25, 30], [22, 20, 28]] Plants → DCs: [[15, 18, 20, 16], [17, 15, 19, 18], [19, 16, 15, 20]] DCs → Stores: [[10, 12, 8, 11, 14], [11, 9, 10, 13, 12], [13, 11, 9, 10, 15], [12, 14, 11, 9, 13]] Optimize the entire supply chain to minimize total costs. ``` ### Example 4: Fleet Planning ``` Help optimize fleet utilization with MCP Optimizer. A transportation company has: - 5 types of trucks with different capacities - 8 delivery routes - Time and fuel constraints Truck Types: - Small (3 tons): 10 units, 12 L/100km consumption, $80/day cost - Medium (5 tons): 8 units, 18 L/100km consumption, $120/day cost - Large (8 tons): 6 units, 25 L/100km consumption, $180/day cost - Semi (15 tons): 4 units, 35 L/100km consumption, $250/day cost - Heavy (20 tons): 2 units, 40 L/100km consumption, $300/day cost Routes (cargo in tons, distance in km): - Route 1: 12 tons, 150 km - Route 2: 8 tons, 200 km - Route 3: 25 tons, 300 km - Route 4: 6 tons, 100 km - Route 5: 18 tons, 250 km - Route 6: 4 tons, 80 km - Route 7: 30 tons, 400 km - Route 8: 15 tons, 180 km Fuel price: $1.50/liter Find optimal truck assignment to routes to minimize total costs. ``` ### Example 5: Warehouse Logistics ``` Optimize warehouse operations using MCP Optimizer. A distribution center processes orders for an e-commerce company: Warehouse Zones: - Zone A (electronics): 500 m², storage cost $10/m²/day - Zone B (clothing): 800 m², storage cost $6/m²/day - Zone C (food): 600 m², storage cost $8/m²/day - Zone D (furniture): 400 m², storage cost $12/m²/day Product Groups (volume m³, required area m²): - Smartphones: 200 m³, 150 m² - Laptops: 300 m³, 200 m² - Jackets: 400 m³, 300 m² - Shoes: 350 m³, 250 m² - Canned goods: 500 m³, 200 m² - Beverages: 600 m³, 300 m² - Tables: 250 m³, 180 m² - Chairs: 300 m³, 220 m² Compatibility constraints: - Food cannot be stored with electronics - Furniture requires separate zone - Clothing is compatible with any products Minimize storage costs while meeting all constraints. ``` ### Example 6: International Logistics ``` Solve an international shipping problem with MCP Optimizer. A global company ships products from 3 producing countries to 6 consuming countries: Producing Countries (capacity thousand units/month): - China: 5000 units - Germany: 3000 units - USA: 4000 units Consuming Countries (demand thousand units/month): - Russia: 1500 units - Brazil: 2000 units - India: 2500 units - France: 1800 units - Japan: 2200 units - Australia: 1000 units Shipping costs ($/unit): From China: [8, 15, 6, 12, 4, 10] From Germany: [12, 18, 14, 3, 11, 16] From USA: [14, 8, 16, 9, 13, 12] Customs duties (%): To Russia: [5, 8, 12] To Brazil: [10, 6, 15] To India: [8, 12, 10] To France: [3, 0, 7] To Japan: [6, 4, 8] To Australia: [7, 5, 9] Delivery time (days): From China: [25, 35, 20, 30, 10, 15] From Germany: [20, 40, 25, 5, 15, 30] From USA: [30, 15, 35, 10, 20, 25] Optimize shipments considering costs, duties, and delivery times. ``` ### Example 7: Last-Mile Urban Logistics ``` Optimize last-mile delivery using MCP Optimizer. A delivery service operates in a city with 50 delivery points: Depot: Central warehouse (coordinates 0, 0) Couriers: - 10 walking couriers (3 km radius, 8 orders/day, $50/day) - 8 bike couriers (8 km radius, 15 orders/day, $80/day) - 6 motorcycle couriers (15 km radius, 25 orders/day, $120/day) - 4 van drivers (25 km radius, 40 orders/day, $200/day) Orders by district: - Downtown (0-5 km): 120 orders, high priority - Midtown (5-12 km): 180 orders, medium priority - Suburbs (12-20 km): 100 orders, low priority - Outskirts (20+ km): 50 orders, low priority Delivery time windows: - Morning (9-12): 30% of orders - Afternoon (12-15): 40% of orders - Evening (15-18): 30% of orders Late delivery penalties: - High priority: $20/order - Medium priority: $10/order - Low priority: $5/order Find optimal courier distribution and routes. ``` ### Example 8: Railway Transportation Optimization ``` Help optimize railway freight transportation with MCP Optimizer. A railway company transports cargo between 8 cities: Cities: New York, Chicago, Los Angeles, Houston, Phoenix, Philadelphia, San Antonio, San Diego Car Types: - Boxcars (40 tons): 200 cars, $150/day - Flatcars (60 tons): 150 cars, $200/day - Tank cars (50 tons): 100 cars, $180/day - Hoppers (70 tons): 80 cars, $220/day Cargo flows (tons/week): - New York → Chicago: 2000 (containers) - Los Angeles → New York: 3000 (steel) - Houston → Chicago: 1500 (grain) - Phoenix → San Antonio: 1000 (chemicals) - Philadelphia → New York: 2500 (food) - San Diego → New York: 1800 (containers) Distances between cities (miles): 8x8 distance matrix with actual railway distances Transit time (days): - Up to 500 miles: 2 days - 500-1000 miles: 4 days - 1000-2000 miles: 7 days - Over 2000 miles: 10 days Constraints: - Containers only in boxcars - Steel only on flatcars - Grain only in hoppers - Chemicals only in tank cars Minimize total transportation costs considering delivery time. ``` ## Request Structure for MCP Optimizer ```python # Example for transportation problem result = solve_transportation_problem( supply=[300, 400, 500], # supply demand=[250, 350, 300, 300], # demand costs=[ [8, 6, 10, 9], [9, 12, 13, 7], [14, 9, 16, 5] ] ) ``` ## Typical Activation Phrases - "Solve a transportation problem" - "Optimize delivery logistics" - "Find optimal shipping routes" - "Minimize transportation costs" - "Plan supply chain" - "Optimize fleet operations" - "Help with warehouse planning" ## Applications Transportation and logistics problems are used in: - Freight transportation and logistics - Supply chain planning - Warehouse operations management - Fleet optimization - International trade - Urban logistics - Railway transportation