Dataset of metaheuristics for the flow shop scheduling problem with maintenance activities integrated
Antonella Branda, Davide Castellano, Guido Guizzi and 1 more
Data in Brief · 2021
doi:10.1016/j.dib.2021.106985 (opens in a new tab)
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Abstract
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.
Abstract licence: CC BY-NC
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