Experimental data for "Biomass Procurement and Supplier Diversification for Energy Generation: Optimization Models and Insights"

Dataset associated with the article:“Biomass Procurement and Supplier Diversification for Energy Generation: Optimization Models and Insights”,submitted to Applied Energy (July 2025).

This dataset contains all the input data and solution reports generated during the computational experiments reported in the associated research article.

Dataset Description, "Data.xlsx"

The dataset is organized in a single Excel file composed of six sheets, each containing a specific data category aligned with the mathematical formulation proposed in the paper. The nomenclature used in the sheets matches the notation described in the article, omitting subscript indices for clarity.

basicContains general parameters of the problem, including the planning horizon (in months), the monthly variation factor applied to demand, and the list of biomass profiles considered in the case study.

suppliersProvides detailed information about all suppliers, including biomass type, moisture category, source location, monthly supply capacity, total annual offer, purchase costs, and contract bounds such as minimum, maximum, and pre-committed volumes.

stocksIncludes initial inventory levels for each biomass profile, as well as storage parameters, including minimum and maximum allowable stocks and the maximum storage capacity for each month. It also contains the monthly holding cost per cubic meter.

supplyDefines the monthly thermal energy requirements (in MWh), as well as the lower and upper bounds for total biomass procurement in each period. Additionally, it includes the energy conversion factors for each biomass type according to moisture level.

mixSpecifies fuel mixture constraints for each biomass profile over the planning horizon. This includes the minimum required proportion of certain biomass types to satisfy technical or operational constraints related to fuel composition.

contractsLists the minimum contract commitments for specific biomass profiles over the entire horizon, representing supplier diversification constraints or contractual obligations.

This dataset is structured to directly match the variables and parameters defined in the mathematical model, ensuring full transparency and reproducibility of the computational experiments presented in this study. 

Model Implementation File, "model.ipynb"

The second file contains the implementation of the mathematical model developed for the research presented in the associated article. The model was coded in the Julia programming language, executed within the JupyterLab environment, and formulated using the JuMP modeling language. The solution process relies on two solvers: HiGHS, an open-source solver, and CPLEX, a commercial optimization solver.

The implementation includes data import routines using the XLSX library to read input data directly from Excel spreadsheets. The code is structured to execute the entire workflow, including data loading, model construction, optimization, and extraction of results.

All computational experiments were carried out on a desktop computer equipped with an Intel(R) Core(TM) i3-12100 (12th Gen) processor (3.30 GHz), 8 GB of RAM, and running Windows 11 (64-bit).

The provided code ensures full reproducibility of the results reported in the associated research article.

Solution Reports, "Output.zip"

Contains the solution reports generated from the computational experiments. It includes 21 Excel files, each named according to the format output_d[X]_Data.xlsx, where [X] represents the value of the contractual variation factor, expressed as a percentage. This factor defines the allowable proportional increase or reduction from the contracted purchase volume. The values of [X] range from 0% to 20%, in discrete increments of 1%.

Each Excel file contains the following sheets:

InstanceProvides a brief description of the instance parameters, including the contractual variation factor applied.

Sol-1Contains the solution report obtained without applying the diversification constraint, representing the baseline solution.

Sol0Reports the solution obtained with the diversification constraint enforced, without requiring additional biomass profiles beyond the contractually committed ones.

Sol1 to Sol10Each sheet reports the solution considering the diversification constraint plus an increasing number of additional biomass profiles—ranging from 1 to 10 additional profiles—to explore the impact of progressive diversification on procurement cost and resource allocation.

ParetoPresents the Pareto frontier for the corresponding contractual variation factor [X], summarizing the trade-offs between procurement cost and supplier diversification for that scenario.

This structured set of solution reports supports a comprehensive analysis of the trade-offs between contractual flexibility, supplier diversification, and procurement cost, as explored in the associated research article.