Datasets for lot sizing and scheduling problems in the fruit-based beverage production process

The datasets presented here were partially used in “Formulation and MIP-heuristics for the lot sizing and scheduling problem with temporal cleanings” (Toscano, A., Ferreira, D. , Morabito, R. , Computers & Chemical Engineering) [1], in “A decomposition heuristic to solve the two-stage lot sizing and scheduling problem with temporal cleaning” (Toscano, A., Ferreira, D. , Morabito, R. , Flexible Services and Manufacturing Journal) [2], and in “A heuristic approach to optimize the production scheduling of fruit-based beverages” (Toscano et al., Gestão & Produção, 2020) [3]. In fruit-based production processes, there are two production stages: preparation tanks and production lines. This production process has some process-specific characteristics, such as temporal cleanings and synchrony between the two production stages, which make optimized production planning and scheduling even more difficult. In this sense, some papers in the literature have proposed different methods to solve this problem. To the best of our knowledge, there are no standard datasets used by researchers in the literature in order to verify the accuracy and performance of proposed methods or to be a benchmark for other researchers considering this problem. The authors have been using small data sets that do not satisfactorily represent different scenarios of production. Since the demand in the beverage sector is seasonal, a wide range of scenarios enables us to evaluate the effectiveness of the proposed methods in the scientific literature in solving real scenarios of the problem. The datasets presented here include data based on real data collected from five beverage companies. We presented four datasets that are specifically constructed assuming a scenario of restricted capacity and balanced costs. These dataset is supplementary data for the submitted paper to Data in Brief [4].
[1] Toscano, A., Ferreira, D., Morabito, R., Formulation and MIP-heuristics for the lot sizing and scheduling problem with temporal cleanings, Computers & Chemical Engineering. 142 (2020) 107038. Doi: 10.1016/j.compchemeng.2020.107038.
[2] Toscano, A., Ferreira, D., Morabito, R., A decomposition heuristic to solve the two-stage lot sizing and scheduling problem with temporal cleaning, Flexible Services and Manufacturing Journal. 31 (2019) 142-173. Doi: 10.1007/s10696-017-9303-9.
[3] Toscano, A., Ferreira, D., Morabito, R., Trassi, M. V. C., A heuristic approach to optimize the production scheduling of fruit-based beverages. Gestão & Produção, 27(4), e4869, 2020. https://doi.org/10.1590/0104-530X4869-20.
[4] Piñeros, J., Toscano, A., Ferreira, D., Morabito, R., Datasets for lot sizing and scheduling problems in the fruit-based beverage production process. Data in Brief (2021).

本数据集的部分数据已应用于以下三篇学术文献：《考虑时段性清洗的批量规划与调度问题的建模及混合整数规划（MIP）启发式算法》（Toscano A, Ferreira D, Morabito R, 发表于《Computers & Chemical Engineering》）[1]、《针对带时段性清洗的两阶段批量规划与调度问题的分解启发式算法》（Toscano A, Ferreira D, Morabito R, 发表于《Flexible Services and Manufacturing Journal》）[2]，以及《一种优化果味饮料生产调度的启发式方法》（Toscano等，发表于《Gestão & Produção》2020年）[3]。

果味饮料的生产流程包含两个核心生产阶段：调配罐工段与生产线工段。该生产流程具备若干工艺特有特性，例如时段性清洗工序，以及两个生产阶段间的同步要求，这使得生产计划与调度的优化难度进一步提升。现有文献中已有诸多学者提出各类方法以解决该类问题，但据我们所知，目前学界尚未形成适用于该问题的标准数据集，既无法用于验证所提方法的准确性与性能，也无法为其他研究者提供基准测试参照。过往研究仅采用小型数据集，无法充分覆盖多样化的生产场景。由于饮料行业的需求具有季节性特征，构建丰富多样的生产场景能够有效评估现有文献中所提方法在解决该问题真实场景时的实际效能。

本数据集所收录的数据均基于从五家饮料企业采集的真实生产数据。本次共提供四个数据集，均针对受限产能与成本均衡的生产场景构建。本数据集为提交至《Data in Brief》的论文[4]的补充数据。

[1] Toscano A, Ferreira D, Morabito R. 考虑时段性清洗的批量规划与调度问题的建模及混合整数规划启发式算法[J]. Computers & Chemical Engineering, 2020, 142: 107038. DOI: 10.1016/j.compchemeng.2020.107038.
[2] Toscano A, Ferreira D, Morabito R. 针对带时段性清洗的两阶段批量规划与调度问题的分解启发式算法[J]. Flexible Services and Manufacturing Journal, 2019, 31: 142-173. DOI: 10.1007/s10696-017-9303-9.
[3] Toscano A, Ferreira D, Morabito R, Trassi M V C. 一种优化果味饮料生产调度的启发式方法[J]. Gestão & Produção, 2020, 27(4): e4869. DOI: 10.1590/0104-530X4869-20.
[4] Piñeros J, Toscano A, Ferreira D, Morabito R. 果味饮料生产流程中批量规划与调度问题的数据集[J]. Data in Brief, 2021.