MDP - Delimiting Hinterlands Under Climate-Induced Impacts

Description

This research data is part of the manuscript A Markov Decision Process Approach to Delimiting Hinterlands Under Climate-Induced Impacts: The Case of the Ferrogrão Intermodal Service.

Steps to reproduce

User Guide: Logistics MDP SimulationThis script implements a Markov Decision Process (MDP) to optimize crop outflow through various logistics corridors. To correctly run the model and reproduce different academic scenarios, please follow these instructions: 1. External Dataset Requirements: The model is designed to be lean, with the distances dataset being the only required external input. Filename: distances.csvLocation: Ensure this file is saved in the same directory as the script. Structure: The CSV must contain exactly three columns:origin: Name of the production city.destination: Name of the terminal or port.distance_km: Numeric value representing the distance between them. 2. Parameter Configuration and Scenario Reproduction: All logic parameters are exhibited directly within the code. To reproduce specific scenarios (such as the impact of the Ferrogrão railway or climate-induced barge limitations), you should modify the variables in the "2. CONFIGURATION AND GLOBAL CONSTANTS" section: Diesel Prices (DEFAULT_SIMULATED_DIESEL_PRICE): Alter this to simulate fluctuations in road transport costs. Carbon Market (CARBON_MARKET_PRICE): Adjust the price per ton of CO2 to analyze how Pigouvian taxes or environmental costs influence the choice of the optimal route. Infrastructure Availability: Inside the load_corridor_data() function, you can model the presence or absence of the Ferrogrão project by adjusting its capacity or the associated deforestation_direct_area parameters. Climate Uncertainty: To simulate different levels of drought or yield volatility, modify the probabilities in the load_scenario_data() function. 3. Execution and Output: Once the parameters are set and distances.csv is ready, run the script. The model will: Generate all possible state transitions based on yield and barge availability scenarios. Perform Bellman Reverse Induction to solve for the finite horizon optimal policy. Export the results to politica_otima_geral.csv, providing the optimal allocation decisions for each origin point under the simulated conditions.

Institutions

• Universidade de São Paulo

  São Paulo, São Paulo

Categories

Licence