Branda Antonella, Castellano Davide, Guizzi Guido, Popolo Valentina
Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli Federico II, Piazzale Tecchio, 80 80125 Napoli, Italy.
Data Brief. 2021 Mar 22;36:106985. doi: 10.1016/j.dib.2021.106985. eCollection 2021 Jun.
This data article presents a flow shop scheduling problem in which machines are not available during the whole planning horizon and the periods of unavailability are due to random faults. The experimental dataset consists of two problems with different sizes. In the largest one, about 2400 problems were analysed and compared with two diffuse metaheuristics: Genetic Algorithm (GA) and Harmony Search (HS). In the smallest, about 600 problems were analysed comparing the solution obtained with an exhaustive algorithm with those obtained by means of GA and HS. This dataset represents a test-bed for further works, allowing a comparison between the solution quality and the computation time obtained with different optimization methods. The substantial computational effort spent to generate the dataset undoubtedly represents a significant asset for the scientific community.
本文介绍了一个流水车间调度问题，其中机器在整个规划期内并非一直可用，不可用期是由随机故障导致的。实验数据集包含两个不同规模的问题。在规模最大的问题中，分析了约2400个问题，并与两种扩散型元启发式算法进行比较：遗传算法（GA）和和声搜索算法（HS）。在规模最小的问题中，分析了约600个问题，将穷举算法得到的解与通过遗传算法和和声搜索算法得到的解进行比较。该数据集为进一步的研究提供了一个测试平台，可用于比较不同优化方法获得的解质量和计算时间。生成该数据集所花费的大量计算工作无疑是科学界的一项重要资产。
