A multistage stochastic mixed integer optimization model for the design of a renewable fuel supply chain network under demand uncertainty - Supplementary Materials

Zenodo (CERN European Organization for Nuclear Research)

Abstract

This repository contains supporting data for: "A data-driven multistage stochastic mixed integer optimization model for the design of a renewable fuel supply chain network under demand uncertainty" Model Input Data: Contains all input parameters used in the model. Demand Data and Scenarios: Provides demand data and scenario generation and reduction assumptions. Demand Scenario Fan (AR1_LHS): Includes the full set of generated demand scenarios based on the Auto-Regression (1) process with Latin Hypercube Sampling. Reduced Demand Scenarios (Cluster_Tree_Representative): Contains scenario trees after reduction using the forward selection algorithm. MSSP Base: Multi-stage stochastic programming (MSSP) results for the base case with standard resource availability and demand uncertainty. MSSP_Low Resource Availability: MSSP results under lower resource availability and demand uncertainty. MSSP_High Resource Availability: MSSP results under higher resource availability and demand uncertainty. EV_Base: Expected Value (deterministic) model results. EEV_Base: Expected result of expected value solution (take the EV solution, then evaluate it across all scenarios). WS_Base: MSSP (wait-and-see) model results.

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