This repository contains the codebase for the optimization of the Biomass Supply Chain (BSC) system, as presented in the thesis titled “Optimizing the Long-term Biomass Supply Chain: A Deep Learning and Mixed Integer Linear Programming Approach.”

Optimizing the Long-term Biomass Supply Chain: A Deep Learning and Mixed Integer Linear Programming Approach

This thesis introduces a comprehensive case study focused on optimizing the long-term Biomass Supply Chain (BSC) system. The proposed methodology consists of two key phases:

1. Predictive Modeling with Deep Learning:
   • Development of a predictive model for forecasting biomass availability.
   • Utilization of Deep Learning (DL) techniques to handle the Spatio-Temporal (ST) nature of the data.
2. Facility Location Problem with MILP:
   • Formulation of a Facility Location Problem model.
   • Deployment of Mixed Integer Linear Programming (MILP) to optimize facility location-allocation and minimize costs.

The primary objective is to establish a framework for realizing the BSC system in a given region, leveraging advanced DL and MILP methods. This approach aims to reduce dependence on Geographical Information Systems (GIS), addressing the associated challenges of expertise and high costs.

You can see more details about the repo in this link