Minimizing flowtime in a flowshop scheduling problem with a biased random-key genetic algorithm

作者：

Highlights：

• Proposition of a new feature for the Biased Random-Key Genetic Algorithm.

• Identification of lower and upper bounds, as well as some optimal values for classical instances.

• Developed genetic algorithm are strong contenders for large scale problems.

摘要

•Proposition of a new feature for the Biased Random-Key Genetic Algorithm.•Identification of lower and upper bounds, as well as some optimal values for classical instances.•Developed genetic algorithm are strong contenders for large scale problems.

论文关键词：Flowshop scheduling,Biased random-key genetic algorithm,Metaheuristics

论文评审过程：Received 26 October 2018, Revised 3 March 2019, Accepted 4 March 2019, Available online 9 March 2019, Version of Record 23 March 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.03.007