Piñeros Juan, Toscano Alyne, Ferreira Deisemara, Morabito Reinaldo
Department of Production Engineering, Federal University of São Carlos, Sorocaba-SP, Brazil.
Department of Production Engineering, Federal University of Triângulo Mineiro, Uberaba-MG, Brazil.
Data Brief. 2021 Jan 29;35:106810. doi: 10.1016/j.dib.2021.106810. eCollection 2021 Apr.
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. Thus, 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 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.
这里展示的数据集部分用于《具有时间清洗的批量规模与调度问题的公式化与混合整数规划启发式算法》（托斯卡诺，A.，费雷拉，D.，莫拉比托，R.，《计算机与化工工程》）[1]、《一种用于解决具有时间清洗的两阶段批量规模与调度问题的分解启发式算法》（托斯卡诺，A.，费雷拉，D.，莫拉比托，R.，《柔性服务与制造杂志》）[2]以及《一种优化水果基饮料生产调度的启发式方法》（托斯卡诺等人，《管理与生产》，2020年）[3]。在水果基生产过程中，有两个生产阶段：调配罐和生产线。这个生产过程具有一些特定于该过程的特征，例如时间清洗以及两个生产阶段之间的同步性，这使得优化生产计划和调度变得更加困难。因此，文献中的一些论文提出了不同的方法来解决这个问题。据我们所知，文献中没有研究人员使用的标准数据集来验证所提出方法的准确性和性能，或者作为考虑这个问题的其他研究人员的基准。作者一直在使用不能令人满意地代表不同生产场景的小数据集。由于饮料行业的需求是季节性的，广泛的场景使我们能够评估科学文献中所提出方法在解决该问题实际场景中的有效性。这里展示的数据集包括基于从五家饮料公司收集的真实数据的数据。我们展示了四个专门针对容量受限和成本平衡场景构建的数据集。
