DOI: 10.5281/zenodo.15120474
title: Experimental data for "A Make-to-Order Capacitated Lot-Sizing Model with Parallel Machines, Eligibility Constraints, Extra Shifts, and Backorders"

description: Dataset used in the experimentation reported in the article:

A Make-to-Order Capacitated Lot-Sizing Model with Parallel Machines, Eligibility Constraints, Extra Shifts, and Backorders.

Reference:Muñoz, F. T., & Ulloa-Navarro, J. (2025). A Make-to-Order Capacitated Lot-Sizing Model with Parallel Machines, Eligibility Constraints, Extra Shifts, and Backorders. Mathematics, 13(11), 1798. https://doi.org/10.3390/math13111798

 

Dataset Contents

1. Problem Instances

The problem instances arise from the production planning and manufacturing processes of a Chilean manufacturer of cold-formed steel profiles, which span a two-month operating period of 24 working days per month.File: All_Instances.7zThe problem instances were developed using base data from the production planning and manufacturing process of a Chilean manufacturer of cold-formed steel profiles over a six-month operating period of 24 working days per month. The details regarding the construction of these instances are presented in the article "A Make-to-Order Capacitated Lot-Sizing Model with Parallel Machines, Eligibility Constraints, Extra Shifts, and Backorders," [DOI: 10.3390/math13111798].The dataset includes 49 folders, each combining different demand levels and order quantities. For each combination of demand factors and the number of orders, 100 instances (4,900 total).Demand factors: 70%, 80%, 90%, 100%, 110%, 120%, and 130%.Number of orders: Random integers uniformly distributed with means of 35, 45, 55, 65, 75, 85, and 95 orders, each with a ±5 variation.

 

2. Solution Reports

A compressed file is provided for each of the following solution runs:i) Optimal runs for all instances with the HiGHS solver: File "optimal runs.7z" (3,533 feasible instances)ii) Time-limited runs for all instances (30, 60, and 120 seconds) with the HiGHS solver: Files "Reports 30s time limit.zip", "Reports 60s time limit.zip", and "Reports 120s time limit.zip" (1,032 instances)iii) Time-limited runs for hard instances (41 selected instances) with the HiGHS solver: File "Reports Hard instances 300s time limit.zip" (41 instances)iv) Optimal runs for hard instances (41 selected instances) with the CPLEX solver: File "Reports Hard instances optimal CPLEX.zip" (41 instances)v) Time-limited runs for hard instances (41 selected instances) with the CPLEX solver: File "Reports Hard instances 300s time limit CPLEX.zip" (41 instances)

3. Summary of Results

This file includes the results for each experiment and instance, detailing the Objective Value, Runtime, and Solver Status. A dedicated README file is provided with additional information.

 

4. Model Implementation

The implementation of the optimization model is provided in the LotSizing.ipynb file, developed using:



Programming Language: Julia.

Platform: JupyterLab.

Modeling Language: JuMP.

Solver: HiGHS and CPLEX.

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