{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T11:05:27Z","timestamp":1779275127039,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>Computer Numerical Control (CNC) machining centers are critical assets in discrete manufacturing, yet many shop floors still rely on periodic expert judgment for machine selection and workload allocation. This practice is unsuitable for high-mix production because machine condition and risk can change rapidly due to tool wear, thermal drift, coolant variation, and alarms. Moreover, decision evidence is fragmented and often incomplete across controller and programmable logic controller signals, production records, and inspection results, making manual evaluation time-consuming and prone to misjudgment. Static rankings can also break down under unforeseen shop-floor disruptions, requiring rapid event-driven re-prioritization and rescheduling. To address these challenges, this research proposes a shop-floor decision intelligence pipeline that executes a rolling-window, uncertainty-aware ranking-and-dispatch loop directly on the shop floor. The industrial compute node continuously collects multi-source operational evidence, normalizes it into a unified event representation, and aggregates rolling-window indicators for each machine. A mapping structure then converts these indicators into neutrosophic triplets that separate performance from evidence credibility. Using this representation, a shop-floor decision procedure continuously updates machine priority scores using a TOPSIS procedure, which are further translated into workload allocation and persistence-confirmed protective action requests. A case study demonstrates end-to-end operation. It shows that the top-ranked machines remain stable under risk-aversion and weight-uncertainty analyses, while the protective logic prevents unsafe dispatching when reject-level conditions persist under reliable evidence. Overall, the proposed pipeline reframes CNC machine selection as a rolling-window, evidence-driven decision process and provides a pathway toward near-real-time and safety-aware shop-floor coordination.<\/jats:p>","DOI":"10.3390\/systems14050530","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T20:11:13Z","timestamp":1778271073000},"page":"530","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real-Time-Oriented Decision-Making for Computer Numerical Control Machine Selection Under Uncertain Evidence"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1795-1123","authenticated-orcid":false,"given":"Amirhossein","family":"Nafei","sequence":"first","affiliation":[{"name":"School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rong-Ho","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 10608, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hsien-Ming","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Green Energy and Environmental Resources, Chang Jung Christian University, Tainan 711301, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shu-Chuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Business and Management, Ming Chi University of Technology, New Taipei 243303, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seyed Mohammadtaghi","family":"Azimi","sequence":"additional","affiliation":[{"name":"College of Management, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,8]]},"reference":[{"key":"ref_1","unstructured":"Barua, R. (2024). Robotics, Automation and Computer Numerical Control, Cambridge Scholars Publishing."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.1016\/j.procs.2025.01.165","article-title":"Human-machine interaction design in adaptive automation","volume":"253","author":"Pollini","year":"2025","journal-title":"Procedia Comput. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2474","DOI":"10.1080\/01605682.2025.2477665","article-title":"Improving industrial automation selection with dynamic exponential distance in neutrosophic group decision-making framework","volume":"76","author":"Nafei","year":"2025","journal-title":"J. Oper. Res. Soc."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mu, S., Yu, C., Lin, K., Lu, C., Wang, X., Wang, T., and Fu, G. (2025). A Review of Machine Learning-Based Thermal Error Modeling Methods for CNC Machine Tools. Machines, 13.","DOI":"10.3390\/machines13020153"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tur\u0161i\u010d, N., and Klan\u010dnik, S. (2024). Tool condition monitoring using machine tool spindle current and long short-term memory neural network model analysis. Sensors, 24.","DOI":"10.20944\/preprints202402.1471.v1"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Figueroa, A.J., Poler, R., and Andres, B. (2024). Adaptive Production Rescheduling System for Managing Unforeseen Disruptions. Mathematics, 12.","DOI":"10.3390\/math12223478"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1007\/s40815-023-01640-9","article-title":"Neutrosophic fuzzy decision-making using TOPSIS and autocratic methodology for machine selection in an industrial factory","volume":"26","author":"Nafei","year":"2024","journal-title":"Int. J. Fuzzy Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"81","DOI":"10.15388\/21-INFOR461","article-title":"A comprehensive solution approach for CNC machine tool selection problem","volume":"33","author":"Sahin","year":"2022","journal-title":"Informatica"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Berthet, V. (2022). The impact of cognitive biases on professionals\u2019 decision-making: A review of four occupational areas. Front. Psychol., 12.","DOI":"10.3389\/fpsyg.2021.802439"},{"key":"ref_10","first-page":"861","article-title":"Automated process planning and dynamic scheduling for smart manufacturing: A systematic literature review","volume":"35","author":"Marzia","year":"2023","journal-title":"Manuf. Lett."},{"key":"ref_11","first-page":"322","article-title":"CODAS, TOPSIS, and AHP methods application for machine selection","volume":"2","author":"Borroel","year":"2023","journal-title":"J. Comput. Cogn. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7653292","DOI":"10.1155\/2022\/7653292","article-title":"A Decision-making model for selection of the suitable FDM machine using fuzzy TOPSIS","volume":"2022","author":"Raja","year":"2022","journal-title":"Math. Probl. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Raman, V.V., Badarinath, R., and Prabhu, V.V. (2025). TOPSIS-Based Methodology for Selecting Fused Filament Fabrication Machines. Machines, 13.","DOI":"10.3390\/machines13070574"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Hwang, C.-L., and Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications, Springer.","DOI":"10.1007\/978-3-642-48318-9"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1007\/s00521-016-2535-x","article-title":"Neutrosophic triplet group","volume":"29","author":"Smarandache","year":"2018","journal-title":"Neural Comput. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5409","DOI":"10.1007\/s00170-025-16238-8","article-title":"Current trends in vibration control and computational optimization for CNC machine tools: A comprehensive review","volume":"139","author":"Ullah","year":"2025","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jia, S., Wang, S., Zhang, N., Cai, W., Liu, Y., Hao, J., Zhang, Z., Yang, Y., and Sui, Y. (2022). Multi-objective parameter optimization of CNC plane milling for sustainable manufacturing. Environ. Sci. Pollut. Res., 1\u201322.","DOI":"10.1007\/s11356-022-24908-3"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1007\/s00170-022-09343-5","article-title":"Minimizing the energy consumption of hole machining integrating the optimization of tool path and cutting parameters on CNC machines","volume":"121","author":"Feng","year":"2022","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2969","DOI":"10.1007\/s00170-023-12500-z","article-title":"Effect of spatial moving structure and topology optimization of the CNC turning machine tools","volume":"129","author":"Chan","year":"2023","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1007\/s00170-024-13415-z","article-title":"Research and application of simulation and optimization for CNC machine tool machining process under data semantic model reconstruction","volume":"132","author":"Hu","year":"2024","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"115","DOI":"10.57062\/ijpem-st.2023.0010","article-title":"Digital twin based machining condition optimization for CNC machining center","volume":"1","author":"Sim","year":"2023","journal-title":"Int. J. Precis. Eng. Manuf. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"111331","DOI":"10.1016\/j.asoc.2024.111331","article-title":"A picture fuzzy set multi criteria decision-making approach to customize hospital recommendations based on patient feedback","volume":"153","author":"Chiclana","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"102118","DOI":"10.1016\/j.inffus.2023.102118","article-title":"Personalized fuzzy semantic model of PHFLTS: Application to linguistic group decision making","volume":"103","author":"Liu","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_24","first-page":"28","article-title":"Energy of a neutrosophic soft set and its applications to multi-criteria decision-making problems","volume":"79","author":"Baser","year":"2025","journal-title":"Neutrosophic Sets Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"43","DOI":"10.61356\/j.nswa.2024.22387","article-title":"An approach to multi-attribute decision-making based on single-valued neutrosophic hesitant fuzzy aczel-alsina aggregation operator","volume":"22","author":"Imran","year":"2024","journal-title":"Neutrosophic Syst. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"10029","DOI":"10.1109\/TASE.2024.3516049","article-title":"Fleet service reliability analysis of self-service systems subject to failure-induced demand switching and a two-dimensional inspection and maintenance policy","volume":"22","author":"Wei","year":"2024","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1853","DOI":"10.1108\/IMDS-09-2021-0584","article-title":"Developing a prescriptive decision support system for shop floor control","volume":"122","author":"Kumari","year":"2022","journal-title":"Ind. Manag. Data Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Vitaliy, M., Sabrina, S., Barbara, M., and Katharina, L. (2023). Development of an expert system to support the decision-making process on the shop floor. New Perspectives and Paradigms in Applied Economics and Business: Select Proceedings of the 2022 6th International Conference on Applied Economics and Business, Springer International Publishing.","DOI":"10.1007\/978-3-031-23844-4_14"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"109377","DOI":"10.1016\/j.ijpe.2024.109377","article-title":"Disentangling the socio-technical impacts of digitalization: What changes for shop-floor decision-makers?","volume":"276","author":"Colombari","year":"2024","journal-title":"Int. J. Prod. Econ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Schamberger, M., Breu, M., and Bodendorf, F. (2024). Data-Driven Decision-Making in Shop Floor Quality Management\u2014A Systematic Literature Review. International Conference on Flexible Automation and Intelligent Manufacturing, Springer Nature.","DOI":"10.1007\/978-3-031-74485-3_47"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/s40171-022-00299-9","article-title":"Moving to a flexible shop floor by analyzing the information flow coming from levels of decision on the shop floor of developing countries using artificial neural network: Cameroon, case study","volume":"23","author":"Mbakop","year":"2022","journal-title":"Glob. J. Flex. Syst. Manag."},{"key":"ref_32","first-page":"1","article-title":"Kendall rank correlation and Mann-Kendall trend test","volume":"602","author":"McLeod","year":"2005","journal-title":"R Package Kendall"},{"key":"ref_33","unstructured":"Durand, F. (2011). A Frequency Analysis of Monte-Carlo and Other Numerical Integration Schemes, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology. Available online: http:\/\/hdl.handle.net\/1721.1\/67677."},{"key":"ref_34","first-page":"121","article-title":"Neutrosophic VIKOR approach for multi-attribute group decision-making","volume":"34","author":"Tooranloo","year":"2024","journal-title":"Oper. Res. Decis."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1142\/S0219686719500094","article-title":"Comparison of fuzzy and crisp versions of an AHP and TOPSIS model for nontraditional manufacturing process ranking decision","volume":"18","author":"Yurdakul","year":"2019","journal-title":"J. Adv. Manuf. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1007\/s12597-024-00858-x","article-title":"A narrative literature review on optimization of manufacturing processes using weighted aggregated sum product assessment (WASPAS) method","volume":"62","author":"Chakraborty","year":"2025","journal-title":"OPSEARCH"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"144264","DOI":"10.1016\/j.chemosphere.2025.144264","article-title":"Use of COPRAS decision model for the selection of best treatment alternatives in removing micropollutants: Diclofenac example","volume":"375","author":"Yesil","year":"2025","journal-title":"Chemosphere"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3873","DOI":"10.1007\/s11135-025-02399-x","article-title":"A new interval-valued neutrosophic AHP-MOORA approach: Application to project manager selection","volume":"60","year":"2025","journal-title":"Qual. Quant."}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/5\/530\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T10:06:16Z","timestamp":1779271576000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/14\/5\/530"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,8]]},"references-count":38,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["systems14050530"],"URL":"https:\/\/doi.org\/10.3390\/systems14050530","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,8]]}}}