In this paper, a feature selection (FS) method is proposed to identify key quality features
(KQFs) in complex manufacturing processes. We propose a multi-objective binary particle …

A multi-objective feature selection approach for selecting key quality characteristics (KQCs)
of unbalanced production data is proposed. We define KQC (feature) selection as a bi …

Nowadays, modern production patterns, such as batch production, have brought new
challenges for multi-unit maintenance decision-making. The maintenance scheduling …

The massive amount of data collected from industrial processes has challenged researchers
and practitioners, turning variable selection into a research topic of interest both in academia …

Resource-constrained classification tasks are common in real-world applications such as
allocating tests for disease diagnosis, hiring decisions when filling a limited number of …

Identifying key quality characteristics (QCs) in production processes is essential for product
quality control and improvement. This paper proposes a multiobjective wrapper-based …

Identifying the key quality characteristics (KQCs)(including part and process parameters) in
production processes is essential for quality control. In this paper, we propose a data-driven …

With the development of the product-service paradigm, many original equipment
manufacturers (OEMs) have begun to provide remanufacturing services besides …

A product, or even a part, may contain hundreds of quality characteristics (QCs), but not all of
them are key characteristics that determine product quality. Selecting possible key quality …

The decision makers always suffer from predicament in choosing appropriate variable set to
evaluate/improve production efficiencies in many applications of data envelopment analysis …