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).

本数据集曾部分应用于《面向带临时清机工序的批量规划与排程（lot sizing and scheduling）问题的建模及混合整数规划启发式算法（MIP-heuristics）》（Toscano A、Ferreira D、Morabito R，发表于《计算机与化学工程（Computers & Chemical Engineering）》）[1]、《面向带临时清机工序的两阶段批量规划与排程问题的分解启发式求解方法》（Toscano A、Ferreira D、Morabito R，发表于《柔性服务与制造期刊（Flexible Services and Manufacturing Journal）》）[2]，以及《果汁饮料生产排程优化的启发式方法》（Toscano等，《管理与生产（Gestão & Produção）》，2020）[3]。

在果汁饮料生产流程中，包含调配罐与生产线两个生产阶段。该生产流程具备若干工艺特性，例如临时清机工序（temporal cleanings）与两生产阶段间的同步要求，这使得优化生产计划与排程的难度进一步提升。现有文献中已有诸多学者提出各类方法以求解该类问题，但据我们所知，目前尚无学界通用的标准数据集供研究者验证所提方法的准确性与性能，或作为同类问题研究的基准数据集。过往研究多采用小型数据集，无法充分表征多样化的生产场景。鉴于饮料行业需求具有季节性特征，构建覆盖广泛场景的数据集能够用于评估现有文献中各类方法对实际生产场景问题的求解有效性。

本数据集包含基于五家饮料企业真实采集数据构建的数据集。本次共提供四组数据集，均针对产能受限且成本均衡的生产场景构建。本数据集为投稿至《数据简报（Data in Brief）》的论文[4]的补充数据。

[1] Toscano A, Ferreira D, Morabito R. Formulation and MIP-heuristics for the lot sizing and scheduling problem with temporal cleanings[J]. Computers & Chemical Engineering, 2020, 142: 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[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. A heuristic approach to optimize the production scheduling of fruit-based beverages[J]. Gestão & Produção, 2020, 27(4): e4869. DOI: 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[J]. Data in Brief, 2021.