A hybrid algorithm for task sequencing problems with iteration in product development

Ebufegha, Akposeiyifa; Li, Simon

doi:10.1080/01605682.2021.1923376

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…Dynamic job shop or flow shop scheduling problems have gathered much attention from the soft computing research community employing novel approaches based on genetic programming (Braune et al ., 2022; Shady et al ., 2022) and gene expression programming (Shady et al ., 2023). The other uses of soft computing in solving business problems related to production and manufacturing are task sequencing in product development (Ebufegha and Li, 2022), optimization of dynamic facility layout (Zouein and Kattan, 2022) and spare parts inventory optimization (Zhang et al ., 2022b) to name a few.…”

Section: Resultsmentioning

confidence: 99%

Soft computing in business: exploring current research and outlining future research directions

Singh

Koohang

et al. 2023

IMDS

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PurposeThe primary aim of this study is to detail the use of soft computing techniques in business and management research. Its objectives are as follows: to conduct a comprehensive scientometric analysis of publications in the field of soft computing, to explore the evolution of keywords, to identify key research themes and latent topics and to map the intellectual structure of soft computing in the business literature.Design/methodology/approachThis research offers a comprehensive overview of the field by synthesising 43 years (1980–2022) of soft computing research from the Scopus database. It employs descriptive analysis, topic modelling (TM) and scientometric analysis.FindingsThis study's co-citation analysis identifies three primary categories of research in the field: the components, the techniques and the benefits of soft computing. Additionally, this study identifies 16 key study themes in the soft computing literature using TM, including decision-making under uncertainty, multi-criteria decision-making (MCDM), the application of deep learning in object detection and fault diagnosis, circular economy and sustainable development and a few others.Practical implicationsThis analysis offers a valuable understanding of soft computing for researchers and industry experts and highlights potential areas for future research.Originality/valueThis study uses scientific mapping and performance indicators to analyse a large corpus of 4,512 articles in the field of soft computing. It makes significant contributions to the intellectual and conceptual framework of soft computing research by providing a comprehensive overview of the literature on soft computing literature covering a period of four decades and identifying significant trends and topics to direct future research.

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