A benchmark dataset for multi-objective flexible job shop cell scheduling

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.

Keywords

• Cell scheduling
• Flexible job shop scheduling
• Multi-objective model
• Sequence-dependent family setup times
• Intercellular transportation times
• Exceptional and reentrant parts