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Updated: Jul 7, 2025

An Efficient and Flexible Cell Aggregation Method for 3D Spheroid Production
Published on: March 27, 2017
A benchmark dataset for multi-objective flexible job shop cell scheduling
Derya Deliktaş1,2, Ender Özcan1, Ozden Ustun3
1Computational Optimisation and Learning (COL) Lab, School of Computer Science, University of Nottingham, NG8 1BB, Nottingham, UK.
Abstract:
This data article presents a description of a benchmark dataset for the multi-objective flexible job shop scheduling problem in a cellular manufacturing environment. This problem considers intercellular moves, exceptional parts, sequence-dependent family setup and intercellular transportation times, and recirculation requiring minimization of makespan and total tardiness simultaneously. It is called a flexible job shop cell scheduling problem with sequence-dependent family setup times and intercellular transportation times (FJCS-SDFSTs-ITTs) problem. The dataset has been developed to evaluate the multi-objective evolutionary algorithms of the FJCS-SDFSTs-ITTs problems that are presented in 'Evolutionary algorithms for multi-objective flexible job shop cell scheduling'. The dataset contains forty- three benchmark instances from 'small' to 'large', including a large real-world problem instance. Researchers can use the dataset to evaluate the future algorithms for the FJCS-SDFSTs- ITTs problems and compare the performance with the existing algorithms.
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