Solving Distributed Hybrid Flowshop Scheduling Problems by a Hybrid Brain Storm Optimization Algorithm

• Published in: IEEE access Vol. 7; pp. 66879 - 66894
• Main Authors: Hao, Jian-Hua, Li, Jun-Qing, Du, Yu, Song, Mei-Xian, Duan, Peng, Zhang, Ying-Yu
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">K -means method; Algorithms; Clustering; Distributed hybrid flowshop; distributed Nawaz-Enscore-Ham; Factories; Globalization; Hafnium; hybrid brain storm optimization; Industrial plants; Job shop scheduling; Job shops; Optimization; Production facilities; Schedules; Scheduling; Storms

With the trend of manufacturing globalization, distributed production has attracted wide attention from the industry and academia. Nevertheless, there has been little research on the distributed hybrid flowshop scheduling (DHFS) problem. To make up for the gap, this study aims to solve the DHFS prob...