pytorch_agentic_supply_chain_optimization.py

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import numpy as np

# Define the SupplyChainAgent class to represent individual supply chain agents
class SupplyChainAgent:
    def __init__(self, agent_id, role, decision_model):
        self.agent_id = agent_id
        self.role = role
        self.decision_model = decision_model
        self.observations = []
        self.actions = []

    def observe(self, environment):
        # Collect observations from the agent's perspective (simulated)
        observation = self.simulate_observation(environment)
        self.observations.append(observation)

    def simulate_observation(self, environment):
        # Simulate the observation process (simulated)
        if self.role == 'supplier':
            observation = {
                'inventory_level': environment.get_inventory_level(),
                'demand_forecast': environment.get_demand_forecast(),
                'lead_time': environment.get_lead_time()
            }
        elif self.role == 'manufacturer':
            observation = {
                'inventory_level': environment.get_inventory_level(),
                'production_capacity': environment.get_production_capacity(),
                'order_quantity': environment.get_order_quantity()
            }
        elif self.role == 'distributor':
            observation = {
                'inventory_level': environment.get_inventory_level(),
                'order_quantity': environment.get_order_quantity(),
                'transportation_cost': environment.get_transportation_cost()
            }
        return observation

    def decide(self):
        # Make a decision based on the observations and the decision model
        observation = self.observations[-1]
        action = self.decision_model.predict(observation)
        self.actions.append(action)

    def act(self, environment):
        # Execute the decided action (simulated)
        action = self.actions[-1]
        if self.role == 'supplier':
            if action == 'increase_inventory':
                environment.increase_inventory()
            elif action == 'decrease_inventory':
                environment.decrease_inventory()
        elif self.role == 'manufacturer':
            if action == 'increase_production':
                environment.increase_production()
            elif action == 'decrease_production':
                environment.decrease_production()
        elif self.role == 'distributor':
            if action == 'increase_order':
                environment.increase_order()
            elif action == 'decrease_order':
                environment.decrease_order()

# Define the DecisionModel class to represent the decision-making model
class DecisionModel(nn.Module):
    def __init__(self, input_size, output_size):
        super(DecisionModel, self).__init__()
        self.fc1 = nn.Linear(input_size, 64)
        self.fc2 = nn.Linear(64, 32)
        self.fc3 = nn.Linear(32, output_size)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

    def predict(self, observation):
        # Preprocess the observation data
        input_data = self.preprocess_observation(observation)
        # Convert the input data to a PyTorch tensor
        input_tensor = torch.tensor(input_data, dtype=torch.float32)
        # Make a prediction using the trained model
        with torch.no_grad():
            output_tensor = self.forward(input_tensor)
            action_index = torch.argmax(output_tensor).item()
        # Map the action index to a specific action
        action = self.index_to_action(action_index)
        return action

    def preprocess_observation(self, observation):
        # Preprocess the observation data
        input_data = list(observation.values())
        return input_data

    def index_to_action(self, index):
        # Map the action index to a specific action based on the agent's role
        if self.role == 'supplier':
            actions = ['increase_inventory', 'decrease_inventory']
        elif self.role == 'manufacturer':
            actions = ['increase_production', 'decrease_production']
        elif self.role == 'distributor':
            actions = ['increase_order', 'decrease_order']
        return actions[index]

# Define the SupplyChainEnvironment class to represent the supply chain environment
class SupplyChainEnvironment:
    def __init__(self, num_agents, num_timesteps):
        self.num_agents = num_agents
        self.num_timesteps = num_timesteps
        self.agents = self.deploy_agents()
        self.inventory_level = 100
        self.production_capacity = 50
        self.order_quantity = 30
        self.transportation_cost = 0.1

    def deploy_agents(self):
        # Deploy supply chain agents with different roles and decision models
        agents = []
        for i in range(self.num_agents):
            if i % 3 == 0:
                role = 'supplier'
                input_size = 3
            elif i % 3 == 1:
                role = 'manufacturer'
                input_size = 3
            else:
                role = 'distributor'
                input_size = 3
            decision_model = DecisionModel(input_size=input_size, output_size=2)
            agent = SupplyChainAgent(i, role, decision_model)
            agents.append(agent)
        return agents

    def get_inventory_level(self):
        # Get the current inventory level
        return self.inventory_level

    def get_demand_forecast(self):
        # Simulate demand forecast (replace with actual data if available)
        return np.random.randint(80, 121)

    def get_lead_time(self):
        # Simulate lead time (replace with actual data if available)
        return np.random.randint(1, 8)

    def get_production_capacity(self):
        # Get the current production capacity
        return self.production_capacity

    def get_order_quantity(self):
        # Get the current order quantity
        return self.order_quantity

    def get_transportation_cost(self):
        # Get the current transportation cost
        return self.transportation_cost

    def increase_inventory(self):
        # Simulate increasing inventory
        self.inventory_level += 10

    def decrease_inventory(self):
        # Simulate decreasing inventory
        self.inventory_level = max(0, self.inventory_level - 10)

    def increase_production(self):
        # Simulate increasing production
        self.production_capacity += 5

    def decrease_production(self):
        # Simulate decreasing production
        self.production_capacity = max(0, self.production_capacity - 5)

    def increase_order(self):
        # Simulate increasing order quantity
        self.order_quantity += 5

    def decrease_order(self):
        # Simulate decreasing order quantity
        self.order_quantity = max(0, self.order_quantity - 5)

    def step(self):
        # Perform one step of the supply chain optimization process
        for agent in self.agents:
            agent.observe(self)
            agent.decide()
            agent.act(self)

    def train_decision_models(self, num_epochs):
        # Train the decision models of the agents using simulated data
        for epoch in range(num_epochs):
            for agent in self.agents:
                # Simulate training data (replace with actual data if available)
                training_data = self.simulate_training_data(agent.role)
                # Train the decision model
                self.train_model(agent.decision_model, training_data)

    def simulate_training_data(self, role):
        # Simulate training data for decision models (replace with actual data if available)
        # This is just a placeholder example
        if role == 'supplier':
            training_data = {
                'inputs': [
                    [100, 110, 3],
                    [80, 90, 5],
                    [120, 130, 2]
                ],
                'outputs': [
                    [1, 0],
                    [0, 1],
                    [1, 0]
                ]
            }
        elif role == 'manufacturer':
            training_data = {
                'inputs': [
                    [100, 50, 30],
                    [90, 60, 25],
                    [110, 40, 35]
                ],
                'outputs': [
                    [1, 0],
                    [0, 1],
                    [1, 0]
                ]
            }
        elif role == 'distributor':
            training_data = {
                'inputs': [
                    [100, 30, 0.1],
                    [90, 35, 0.15],
                    [110, 25, 0.08]
                ],
                'outputs': [
                    [1, 0],
                    [0, 1],
                    [1, 0]
                ]
            }
        return training_data

    def train_model(self, model, training_data):
        # Train the decision model using the training data
        criterion = nn.CrossEntropyLoss()
        optimizer = optim.Adam(model.parameters(), lr=0.001)

        inputs = torch.tensor(training_data['inputs'], dtype=torch.float32)
        outputs = torch.tensor(training_data['outputs'], dtype=torch.long)

        for _ in range(100):  # Adjust the number of training iterations as needed
            optimizer.zero_grad()
            predicted_outputs = model(inputs)
            loss = criterion(predicted_outputs, torch.argmax(outputs, dim=1))
            loss.backward()
            optimizer.step()

    def run_simulation(self):
        # Run the supply chain optimization simulation for the specified number of timesteps
        for t in range(self.num_timesteps):
            print(f"Timestep {t + 1}:")
            self.step()
            print()

# Set the random seed for reproducibility
torch.manual_seed(42)
np.random.seed(42)

# Define the simulation parameters
num_agents = 9
num_timesteps = 10
num_training_epochs = 10

# Create an instance of the SupplyChainEnvironment
env = SupplyChainEnvironment(num_agents, num_timesteps)

# Train the decision models of the agents
env.train_decision_models(num_training_epochs)

# Run the supply chain optimization simulation
env.run_simulation()

# Possible Errors and Solutions:
# 1. Error: No module named 'torch'
#    Solution: Make sure you have PyTorch installed. You can install it using pip: `pip install torch`.
#
# 2. Error: ValueError: too many dimensions
#    Solution: This error can occur if the input data passed to the decision model is not in the expected format.
#              Make sure that the input data is properly preprocessed and has the correct dimensions.
#              Check the `preprocess_observation` method and ensure that it returns a list of numerical values.
#
# 3. Error: RuntimeError: expected scalar type Float but found Double
#    Solution: This error can happen if the data types of tensors are inconsistent.
#              Make sure that all tensors have the same data type (e.g., float32).
#              Use `dtype=torch.float32` when creating tensors to ensure consistency.
#
# 4. Error: Inefficient or suboptimal supply chain decisions
#    Solution: If the supply chain agents are making inefficient or suboptimal decisions, consider the following:
#              - Review the decision model architecture and hyperparameters to ensure they are suitable for the task.
#              - Increase the complexity of the decision model or use a different neural network architecture.
#              - Incorporate more advanced techniques such as reinforcement learning or optimization algorithms.
#              - Gather more diverse and representative training data to improve the decision models' performance.
#              - Implement mechanisms for agents to share information and coordinate their actions effectively.

# Note: The code provided here is a simplified simulation and may not cover all the complexities of real-world supply chain optimization.
#       In practice, you would need to integrate with the actual supply chain systems, data sources, and business processes
#       to deploy the multi-agent system effectively. The simulation can be extended to incorporate more realistic supply chain
#       dynamics, constraints, and performance metrics based on the specific industry and scenario.
#       The training data for the decision models would typically be obtained from historical supply chain data, demand forecasts,
#       and domain expertise, depending on the availability and relevance of the data.