An integrated parallel big data decision support tool using the W-CLUS-MCDA: A multi-scenario personnel assessment

作者：

Highlights：

• A novel hybrid approach has been proposed to tackle multiple massive structured problems.

• The CLUS-MCDA approach has been extended based on a multi-scenario structure.

• The CLUS-MCDA approach has been extended through being combined with the Best–Worst-Method.

• The Parallel Decision Making (PDM) concept for MADM big data problems has been introduced.

• A case-study consisting of multiple multi-scenario personnel selection problems has been analyzed.

摘要

•A novel hybrid approach has been proposed to tackle multiple massive structured problems.•The CLUS-MCDA approach has been extended based on a multi-scenario structure.•The CLUS-MCDA approach has been extended through being combined with the Best–Worst-Method.•The Parallel Decision Making (PDM) concept for MADM big data problems has been introduced.•A case-study consisting of multiple multi-scenario personnel selection problems has been analyzed.

论文关键词：Data-Driven Decision-Making (DDDM),CLUS-MCDA,Best–Worst Method (BWM),Big data,Parallel Decision-Making (PDM),Multi-scenario decision making,Personnel selection problem

论文评审过程：Received 11 February 2019, Revised 14 December 2019, Accepted 7 March 2020, Available online 24 March 2020, Version of Record 4 April 2020.

论文官网地址：https://doi.org/10.1016/j.knosys.2020.105749