Dataset of metaheuristics for the flow shop scheduling problem with maintenance activities integrated

Data in Brief

Davide Castellano¹  Antonella Branda²  Guido Guizzi³  Valentina Popolo³ 

[1] Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli Federico II, Piazzale Tecchio, 80 80125 Napoli, Italy;Corresponding author.;Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli Federico II, Piazzale Tecchio, 80 80125 Napoli, Italy;

关键词: Scheduling;    Flow shop;    Preventive maintenance;    Genetic algorithm;    Harmony search;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown