inventory-routing-problem
Installation
SKILL.md
Inventory Routing Problem (IRP)
You are an expert in Inventory Routing Problems (IRP) and integrated inventory-distribution optimization. Your goal is to help jointly optimize inventory management and vehicle routing decisions to minimize total system costs including inventory holding, routing, and potential stockouts.
Initial Assessment
Before solving inventory routing problems, understand:
-
System Structure
- Vendor-managed inventory (VMI) or retailer-managed?
- Number of customers/retailers?
- Single depot or multiple?
- Planning horizon (days, weeks)?
- Frequency of deliveries?
-
Inventory Characteristics
- Storage capacity at each location?
- Current inventory levels?
- Consumption/demand rates (deterministic or stochastic)?
- Minimum inventory levels (safety stock)?
- Maximum inventory levels (tank capacity, shelf space)?
- Product shelf life or perishability?
-
Routing Constraints
- Vehicle capacity (weight, volume)?
- Number of vehicles available?
- Maximum route duration or distance?
- Time windows for deliveries?
- Driver shift constraints?
- Accessibility restrictions?
-
Cost Structure
- Inventory holding costs at depot and customers?
- Transportation costs (per mile, per vehicle, per route)?
- Fixed cost per vehicle used?
- Penalty costs for stockouts?
- Setup/delivery fee per customer visit?
-
Service Requirements
- Must prevent stockouts?
- Minimum service frequency per customer?
- Priority customers?
- Contractual delivery requirements?
IRP Fundamentals
Problem Definition
The Inventory Routing Problem (IRP) integrates two classical problems:
- Inventory Management: When and how much to replenish each customer
- Vehicle Routing: How to efficiently route vehicles to serve customers
Key Trade-off:
- More frequent small deliveries → Higher routing costs, lower inventory
- Less frequent large deliveries → Lower routing costs, higher inventory
Problem Variants
1. Single-Period IRP
- One-time routing and delivery decision
- Given current inventory levels
- Minimize routing cost subject to inventory constraints
2. Multi-Period IRP
- Plan deliveries over time horizon (T periods)
- Account for inventory dynamics
- Most realistic and most complex
3. Deterministic vs. Stochastic IRP
- Deterministic: Known consumption rates
- Stochastic: Uncertain demand, requires safety stock
4. Maritime IRP (MIRP)
- Ships instead of trucks
- Larger capacities, longer travel times
- Often used for petrol/chemical distribution
Python Implementation: IRP Models
Single-Period IRP with MIP
import numpy as np
import pandas as pd
from pulp import *
from typing import List, Dict, Tuple
import matplotlib.pyplot as plt
from scipy.spatial.distance import cdist
class SinglePeriodIRP:
"""
Single-Period Inventory Routing Problem
Given:
- Current inventory at each customer
- Consumption rates
- Vehicle capacity
- Distance matrix
Decide:
- Which customers to visit
- How much to deliver to each
- Vehicle routes
"""
def __init__(self, num_customers: int, customer_locations: np.ndarray,
depot_location: np.ndarray, current_inventory: np.ndarray,
consumption_rates: np.ndarray, max_inventory: np.ndarray,
vehicle_capacity: float, num_vehicles: int,
holding_cost: float = 1.0, routing_cost_per_km: float = 1.0):
"""
Parameters:
-----------
num_customers : int
Number of customer locations
customer_locations : ndarray
(n x 2) array of customer coordinates
depot_location : ndarray
(2,) depot coordinates
current_inventory : ndarray
Current inventory level at each customer
consumption_rates : ndarray
Daily consumption at each customer
max_inventory : ndarray
Maximum storage capacity at each customer
vehicle_capacity : float
Vehicle capacity (units)
num_vehicles : int
Number of vehicles available
holding_cost : float
Inventory holding cost per unit per day
routing_cost_per_km : float
Cost per kilometer traveled
"""
self.n = num_customers
self.customer_locations = customer_locations
self.depot = depot_location
self.I = current_inventory
self.d = consumption_rates
self.C = max_inventory
self.Q = vehicle_capacity
self.K = num_vehicles
self.h = holding_cost
self.c_routing = routing_cost_per_km
# Calculate distance matrix
all_locations = np.vstack([depot_location, customer_locations])
self.dist_matrix = cdist(all_locations, all_locations, metric='euclidean')
def solve_mip(self, time_until_next_delivery: int = 1) -> Dict:
"""
Solve single-period IRP using Mixed-Integer Programming
Decision variables:
- x[i,j,k]: binary, 1 if vehicle k travels from i to j
- y[i,k]: binary, 1 if customer i is visited by vehicle k
- q[i]: quantity delivered to customer i
"""
# Create problem
prob = LpProblem("Single_Period_IRP", LpMinimize)
# Nodes: 0 = depot, 1..n = customers
nodes = range(self.n + 1)
customers = range(1, self.n + 1)
vehicles = range(self.K)
# Decision variables
# Routing variables
x = {}
for i in nodes:
for j in nodes:
for k in vehicles:
if i != j:
x[i, j, k] = LpVariable(f"x_{i}_{j}_{k}", cat='Binary')
# Visit variables
y = {}
for i in customers:
for k in vehicles:
y[i, k] = LpVariable(f"y_{i}_{k}", cat='Binary')
# Delivery quantities
q = {i: LpVariable(f"q_{i}", lowBound=0) for i in customers}
# Objective: Minimize routing cost + inventory holding cost
routing_cost = lpSum([
self.dist_matrix[i, j] * self.c_routing * x[i, j, k]
for i in nodes for j in nodes for k in vehicles if i != j
])
# Inventory after delivery
inventory_after = {i: self.I[i - 1] + q[i] for i in customers}
holding_cost = self.h * lpSum([inventory_after[i] for i in customers])
prob += routing_cost + holding_cost
# Constraints
# 1. Each customer visited at most once
for i in customers:
prob += lpSum([y[i, k] for k in vehicles]) <= 1
# 2. Visit variable linking
for i in customers:
for k in vehicles:
prob += lpSum([x[j, i, k] for j in nodes if j != i]) == y[i, k]
prob += lpSum([x[i, j, k] for j in nodes if j != i]) == y[i, k]
# 3. Vehicle starts and ends at depot
for k in vehicles:
prob += lpSum([x[0, j, k] for j in customers]) <= 1
prob += lpSum([x[i, 0, k] for i in customers]) <= 1
prob += (lpSum([x[0, j, k] for j in customers]) ==
lpSum([x[i, 0, k] for i in customers]))
# 4. Flow conservation
for k in vehicles:
for j in customers:
prob += (lpSum([x[i, j, k] for i in nodes if i != j]) ==
lpSum([x[j, i, k] for i in nodes if i != j]))
# 5. Vehicle capacity
for k in vehicles:
prob += lpSum([q[i] * y[i, k] for i in customers]) <= self.Q
# 6. Delivery quantity constraints
for i in customers:
# Don't deliver more than capacity minus current inventory
prob += q[i] <= (self.C[i - 1] - self.I[i - 1]) * lpSum([y[i, k]
for k in vehicles])
# If visited, deliver enough to avoid stockout until next delivery
min_delivery = max(0, time_until_next_delivery * self.d[i - 1] - self.I[i - 1])
prob += q[i] >= min_delivery * lpSum([y[i, k] for k in vehicles])
# 7. Subtour elimination (MTZ formulation)
u = {i: LpVariable(f"u_{i}", lowBound=0, upBound=self.n) for i in customers}
for i in customers:
for j in customers:
for k in vehicles:
if i != j:
prob += u[i] - u[j] + self.n * x[i, j, k] <= self.n - 1
# Solve
prob.solve(PULP_CBC_CMD(msg=0))
# Extract solution
routes = self._extract_routes(x, vehicles, nodes)
deliveries = {i: q[i].varValue if q[i].varValue else 0 for i in customers}
total_distance = sum(
self.dist_matrix[i, j] * x[i, j, k].varValue
for i in nodes for j in nodes for k in vehicles
if i != j and x[i, j, k].varValue > 0.5
)
total_delivery = sum(deliveries.values())
return {
'status': LpStatus[prob.status],
'routes': routes,
'deliveries': deliveries,
'total_cost': value(prob.objective),
'routing_cost': self.c_routing * total_distance,
'holding_cost': self.h * sum(self.I[i - 1] + deliveries[i]
for i in customers),
'total_distance': total_distance,
'total_delivery': total_delivery,
'vehicles_used': len([r for r in routes if len(r) > 2])
}
def _extract_routes(self, x, vehicles, nodes):
"""Extract route sequences from solution"""
routes = []
for k in vehicles:
route = [0] # Start at depot
current = 0
while True:
next_node = None
for j in nodes:
if j != current and (current, j, k) in x:
if x[current, j, k].varValue > 0.5:
next_node = j
break
if next_node is None or next_node == 0:
if len(route) > 1:
route.append(0) # Return to depot
routes.append(route)
break
route.append(next_node)
current = next_node
return routes
def plot_solution(self, solution: Dict):
"""Visualize IRP solution"""
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))
# Plot 1: Routes
colors = plt.cm.tab10(np.linspace(0, 1, len(solution['routes'])))
# Plot depot
ax1.plot(self.depot[0], self.depot[1], 'rs', markersize=15,
label='Depot', zorder=5)
# Plot customers
for i in range(self.n):
ax1.plot(self.customer_locations[i, 0],
self.customer_locations[i, 1],
'bo', markersize=10, zorder=3)
ax1.text(self.customer_locations[i, 0],
self.customer_locations[i, 1],
f' {i+1}', fontsize=9)
# Plot routes
for route_idx, route in enumerate(solution['routes']):
if len(route) > 2:
route_coords = np.vstack([
self.depot if node == 0
else self.customer_locations[node - 1]
for node in route
])
ax1.plot(route_coords[:, 0], route_coords[:, 1],
'o-', color=colors[route_idx], linewidth=2,
markersize=8, label=f'Route {route_idx + 1}',
alpha=0.7)
ax1.set_xlabel('X Coordinate')
ax1.set_ylabel('Y Coordinate')
ax1.set_title('Vehicle Routes', fontweight='bold')
ax1.legend()
ax1.grid(True, alpha=0.3)
# Plot 2: Inventory levels
customer_ids = np.arange(1, self.n + 1)
current_inv = self.I
deliveries = [solution['deliveries'][i] for i in customer_ids]
final_inv = current_inv + deliveries
capacity = self.C
x_pos = np.arange(self.n)
width = 0.35
ax2.bar(x_pos - width/2, current_inv, width, label='Current Inventory',
alpha=0.7, color='orange')
ax2.bar(x_pos + width/2, final_inv, width, label='After Delivery',
alpha=0.7, color='green')
ax2.plot(x_pos, capacity, 'r--', linewidth=2, label='Max Capacity')
# Mark delivered customers
delivered_customers = [i for i in customer_ids if deliveries[i - 1] > 0]
if delivered_customers:
ax2.scatter([c - 1 for c in delivered_customers],
[final_inv[c - 1] for c in delivered_customers],
s=200, marker='*', color='red', zorder=5,
label='Delivered')
ax2.set_xlabel('Customer ID')
ax2.set_ylabel('Inventory Level (units)')
ax2.set_title('Inventory Levels Before and After', fontweight='bold')
ax2.set_xticks(x_pos)
ax2.set_xticklabels(customer_ids)
ax2.legend()
ax2.grid(True, alpha=0.3, axis='y')
plt.tight_layout()
return plt
# Example Usage
def example_single_period_irp():
"""Example: Single-period IRP with 8 customers"""
print("\n" + "=" * 70)
print("INVENTORY ROUTING PROBLEM (IRP): SINGLE-PERIOD")
print("=" * 70)
np.random.seed(42)
# Problem setup
num_customers = 8
depot = np.array([50, 50])
# Customer locations (random)
customer_locations = np.random.rand(num_customers, 2) * 100
# Current inventory (random, 20-80% of capacity)
max_inventory = np.random.randint(80, 150, num_customers)
current_inventory = max_inventory * np.random.uniform(0.2, 0.8, num_customers)
# Consumption rates (units per day)
consumption_rates = np.random.uniform(5, 20, num_customers)
# Days until will stock out if not replenished
days_until_stockout = current_inventory / consumption_rates
print("\nProblem Data:")
print(f" Number of Customers: {num_customers}")
print(f" Vehicle Capacity: 200 units")
print(f" Number of Vehicles: 3")
print(f" Planning Period: 1 day")
print("\n Customer Inventory Status:")
print(f"\n {'Customer':<12} {'Current':<12} {'Capacity':<12} {'Usage/Day':<12} "
f"{'Days to Stockout'}")
print(" " + "-" * 65)
for i in range(num_customers):
print(f" {i+1:<12} {current_inventory[i]:<12.0f} "
f"{max_inventory[i]:<12.0f} {consumption_rates[i]:<12.1f} "
f"{days_until_stockout[i]:<.1f}")
# Create and solve IRP
irp = SinglePeriodIRP(
num_customers=num_customers,
customer_locations=customer_locations,
depot_location=depot,
current_inventory=current_inventory,
consumption_rates=consumption_rates,
max_inventory=max_inventory,
vehicle_capacity=200,
num_vehicles=3,
holding_cost=1.0,
routing_cost_per_km=2.0
)
print("\nSolving IRP with MIP...")
solution = irp.solve_mip(time_until_next_delivery=3) # Plan for 3 days
print(f"\n{'=' * 70}")
print("OPTIMAL SOLUTION")
print("=" * 70)
print(f"\n{'Status:':<30} {solution['status']}")
print(f"{'Total Cost:':<30} ${solution['total_cost']:,.2f}")
print(f"{'Routing Cost:':<30} ${solution['routing_cost']:,.2f}")
print(f"{'Holding Cost:':<30} ${solution['holding_cost']:,.2f}")
print(f"{'Total Distance:':<30} {solution['total_distance']:.1f} km")
print(f"{'Vehicles Used:':<30} {solution['vehicles_used']}")
print(f"{'Total Delivered:':<30} {solution['total_delivery']:.0f} units")
print("\n Vehicle Routes and Deliveries:")
for route_idx, route in enumerate(solution['routes']):
if len(route) > 2:
print(f"\n Route {route_idx + 1}: ", end='')
print(" → ".join([f"Depot" if node == 0 else f"Cust {node}"
for node in route]))
route_delivery = sum(solution['deliveries'][node]
for node in route if node > 0)
print(f" Total delivery on route: {route_delivery:.0f} units")
for node in route:
if node > 0:
delivery = solution['deliveries'][node]
if delivery > 0:
print(f" Customer {node}: Deliver {delivery:.0f} units")
# Plot solution
irp.plot_solution(solution)
plt.savefig('/tmp/irp_single_period.png', dpi=300, bbox_inches='tight')
print(f"\nSolution plot saved to /tmp/irp_single_period.png")
return irp, solution
if __name__ == "__main__":
example_single_period_irp()
Multi-Period IRP
Rolling Horizon Approach
class MultiPeriodIRP:
"""
Multi-Period IRP using rolling horizon approach
Solve single-period IRP repeatedly, updating inventory levels
"""
def __init__(self, single_period_irp: SinglePeriodIRP,
num_periods: int, delivery_frequency: int = 2):
"""
Parameters:
-----------
single_period_irp : SinglePeriodIRP
Single-period IRP model
num_periods : int
Number of periods to plan
delivery_frequency : int
Minimum periods between deliveries to same customer
"""
self.irp = single_period_irp
self.T = num_periods
self.freq = delivery_frequency
# Track inventory over time
self.inventory_history = np.zeros((num_periods + 1, self.irp.n))
self.inventory_history[0] = self.irp.I
# Track deliveries
self.delivery_history = []
# Track routes
self.route_history = []
def solve_rolling_horizon(self) -> Dict:
"""Solve multi-period IRP using rolling horizon"""
total_cost = 0
total_distance = 0
total_delivered = 0
for t in range(self.T):
print(f" Period {t+1}/{self.T}...", end='')
# Update current inventory in IRP model
self.irp.I = self.inventory_history[t]
# Solve single-period IRP
solution = self.irp.solve_mip(time_until_next_delivery=self.freq)
# Record solution
self.route_history.append(solution['routes'])
self.delivery_history.append(solution['deliveries'])
# Update costs
total_cost += solution['total_cost']
total_distance += solution['total_distance']
total_delivered += solution['total_delivery']
# Update inventory for next period
for i in range(1, self.irp.n + 1):
delivered = solution['deliveries'][i]
consumed = self.irp.d[i - 1]
self.inventory_history[t + 1, i - 1] = (
self.inventory_history[t, i - 1] + delivered - consumed
)
print(f" Cost: ${solution['total_cost']:.2f}")
return {
'total_cost': total_cost,
'total_distance': total_distance,
'total_delivered': total_delivered,
'avg_cost_per_period': total_cost / self.T,
'inventory_history': self.inventory_history,
'delivery_history': self.delivery_history,
'route_history': self.route_history
}
# Example: Multi-period
def example_multi_period_irp():
"""Example: 7-day multi-period IRP"""
print("\n" + "=" * 70)
print("MULTI-PERIOD INVENTORY ROUTING PROBLEM")
print("=" * 70)
np.random.seed(42)
# Setup (smaller problem for multi-period)
num_customers = 5
depot = np.array([50, 50])
customer_locations = np.random.rand(num_customers, 2) * 100
max_inventory = np.array([100, 120, 80, 150, 100])
current_inventory = np.array([80, 90, 60, 100, 70])
consumption_rates = np.array([10, 15, 8, 20, 12])
# Create single-period IRP
irp = SinglePeriodIRP(
num_customers=num_customers,
customer_locations=customer_locations,
depot_location=depot,
current_inventory=current_inventory,
consumption_rates=consumption_rates,
max_inventory=max_inventory,
vehicle_capacity=150,
num_vehicles=2,
holding_cost=0.5,
routing_cost_per_km=2.0
)
# Create multi-period wrapper
multi_irp = MultiPeriodIRP(irp, num_periods=7, delivery_frequency=2)
print("\nSolving 7-day multi-period IRP...")
print(" Using rolling horizon approach")
solution = multi_irp.solve_rolling_horizon()
print(f"\n{'=' * 70}")
print("MULTI-PERIOD RESULTS (7 days)")
print("=" * 70)
print(f"\n{'Total Cost (7 days):':<30} ${solution['total_cost']:,.2f}")
print(f"{'Average Cost per Day:':<30} ${solution['avg_cost_per_period']:,.2f}")
print(f"{'Total Distance:':<30} {solution['total_distance']:.1f} km")
print(f"{'Total Delivered:':<30} {solution['total_delivered']:.0f} units")
# Plot inventory evolution
fig, axes = plt.subplots(num_customers, 1, figsize=(12, 10))
for i in range(num_customers):
axes[i].plot(range(8), solution['inventory_history'][:, i],
marker='o', linewidth=2, color='blue')
axes[i].axhline(y=max_inventory[i], color='red', linestyle='--',
label='Capacity')
axes[i].axhline(y=0, color='black', linestyle='-', linewidth=0.5)
# Mark delivery days
for t in range(7):
if multi_irp.delivery_history[t][i + 1] > 0:
axes[i].plot(t, solution['inventory_history'][t, i],
'g^', markersize=12, label='Delivery' if t == 0 else '')
axes[i].set_ylabel(f'Cust {i+1}\nInventory')
axes[i].grid(True, alpha=0.3)
if i == 0:
axes[i].legend()
axes[-1].set_xlabel('Day')
plt.suptitle('Inventory Evolution Over 7 Days', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.savefig('/tmp/irp_multi_period.png', dpi=300, bbox_inches='tight')
print(f"\nInventory evolution plot saved to /tmp/irp_multi_period.png")
return multi_irp, solution
if __name__ == "__main__":
example_multi_period_irp()
Tools & Libraries
Python Libraries
pulp,pyomo: MIP modelingortools: Google OR-Tools for routingnumpy,scipy: Numerical computations
Commercial Software
- Blue Yonder TMS: Transportation with VMI
- Manhattan Associates: WMS/TMS integration with inventory
- SAP TM + EWM: Integrated transportation and warehouse management
- Oracle Transportation Management: Route optimization with inventory
- Descartes: Routing with inventory considerations
Common Challenges & Solutions
Challenge: Problem Size and Complexity
Problem: Combinatorial explosion with many customers and periods Solutions:
- Rolling horizon approach
- Cluster-first, route-second heuristics
- Decomposition methods
- Limit optimization time, use good heuristics
Challenge: Demand Uncertainty
Problem: Stochastic consumption rates Solutions:
- Safety stock at customers
- Robust optimization with demand scenarios
- Frequent replanning
- Risk pooling at depot
Challenge: Time Windows and Service Requirements
Problem: Customers have delivery windows, minimum frequencies Solutions:
- Add time window constraints to MIP
- Multi-objective optimization (cost vs. service)
- Penalty costs for violations
- Contract-based service level agreements
Challenge: Heterogeneous Fleet
Problem: Different vehicle types (capacity, cost) Solutions:
- Index vehicles by type in model
- Type-specific routing costs
- Preferential use of lower-cost vehicles
Related Skills
- vehicle-routing-problem: Pure routing optimization
- route-optimization: Transportation planning
- inventory-optimization: Inventory management
- multi-echelon-inventory: Network inventory
- network-design: Strategic distribution network
- fleet-management: Vehicle fleet operations
- demand-forecasting: Consumption rate prediction
Related skills
More from kishorkukreja/awesome-supply-chain
procurement-optimization
When the user wants to optimize procurement decisions, allocate orders across suppliers, or determine optimal order quantities. Also use when the user mentions "order allocation," "supplier portfolio optimization," "lot sizing," "order splitting," "purchase optimization," "EOQ," "sourcing optimization," or "multi-sourcing strategy." For supplier selection, see supplier-selection. For spend analysis, see spend-analysis.
71replenishment-strategy
When the user wants to design or optimize replenishment strategies, determine replenishment policies, or improve inventory flow between locations. Also use when the user mentions "inventory replenishment," "stock replenishment," "min-max inventory," "DRP," "auto-replenishment," "vendor-managed inventory," "forward pick replenishment," or "retail store replenishment." For safety stock calculations, see inventory-optimization. For multi-echelon networks, see multi-echelon-inventory.
37inventory-optimization
When the user wants to optimize inventory levels, calculate safety stock, determine reorder points, or minimize inventory costs. Also use when the user mentions "inventory management," "safety stock," "EOQ," "reorder point," "service level," "stockout prevention," "ABC analysis," "inventory turns," or "working capital reduction." For warehouse slotting, see warehouse-slotting-optimization. For multi-echelon systems, see multi-echelon-inventory.
33pharmaceutical-supply-chain
When the user wants to optimize pharmaceutical supply chains, manage cold chain logistics, ensure regulatory compliance, or implement serialization. Also use when the user mentions "pharma supply chain," "GMP compliance," "cold chain," "drug serialization," "clinical trials logistics," "pharmaceutical distribution," "good distribution practices," "GDP," "drug safety," or "pharmaceutical quality." For general healthcare, see hospital-logistics. For clinical trials specifically, see clinical-trial-logistics.
30supplier-selection
When the user wants to evaluate suppliers, select vendors, or perform supplier scoring and qualification. Also use when the user mentions "vendor selection," "supplier evaluation," "RFP scoring," "supplier qualification," "vendor comparison," "make vs buy," "supplier scorecard," or "bid analysis." For ongoing supplier risk monitoring, see supplier-risk-management. For contract negotiation, see contract-management.
30freight-optimization
When the user wants to optimize freight transportation, reduce shipping costs, or improve carrier selection. Also use when the user mentions "freight management," "carrier optimization," "mode selection," "LTL/TL optimization," "freight consolidation," "load planning," or "transportation procurement." For local delivery routes, see route-optimization. For last-mile, see last-mile-delivery.
28