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Enriching Artificial Intelligence Explanations with Knowledge Fragments
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In: Future Internet; Volume 14; Issue 5; Pages: 134 (2022)
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i-Dataquest: A heterogeneous information retrieval tool using data graph for the manufacturing industry
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In: ISSN: 0166-3615 ; Computers in Industry ; https://hal.archives-ouvertes.fr/hal-03330584 ; Computers in Industry, Elsevier, 2021, 132, pp.103527. ⟨10.1016/j.compind.2021.103527⟩ (2021)
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Retrospective Study of Inspectors Competency in the Act of Writing GMP Inspection Report
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In: BIRS Africa Technical Reports (2021)
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A novel hybrid multi-criteria group decision making approach for failure mode and effect analysis: An essential requirement for sustainable manufacturing
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A model for ITIS project portfolio selection in the presence of uncertainty Combination of fuzzy multi-criteria decision making and fuzzy mathematical programming ...
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A model for ITIS project portfolio selection in the presence of uncertainty Combination of fuzzy multi-criteria decision making and fuzzy mathematical programming ...
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Micro-work, artificial intelligence and the automotive industry
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In: ISSN: 0391-2078 ; EISSN: 1972-4977 ; Journal of Industrial and Business Economics = Economia e politica industriale ; https://hal.archives-ouvertes.fr/hal-02148979 ; Journal of Industrial and Business Economics = Economia e politica industriale, Springer, 2019, pp.1-13. ⟨10.1007/s40812-019-00121-1⟩ ; https://link.springer.com/article/10.1007/s40812-019-00121-1 (2019)
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Multi-Objective Service Selection and Scheduling with Linguistic Preference in Cloud Manufacturing
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In: Sustainability ; Volume 11 ; Issue 9 (2019)
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Abstract:
Service management in cloud manufacturing (CMfg), especially the service selection and scheduling (SSS) problem has aroused general attention due to its broad industrial application prospects. Due to the diversity of CMfg services, SSS usually need to take into account multiple objectives in terms of time, cost, quality, and environment. As one of the keys to solving multi-objective problems, the preference information of decision maker (DM) is less considered in current research. In this paper, linguistic preference is considered, and a novel two-phase model based on a desirable satisfying degree is proposed for solving the multi-objective SSS problem with linguistic preference. In the first phase, the maximum comprehensive satisfying degree is calculated. In the second phase, the satisfying solution is obtained by repeatedly solving the model and interaction with DM. Compared with the traditional model, the two-phase is more effective, which is verified in the calculation experiment. The proposed method could offer useful insights which help DM balance multiple objectives with linguistic preference and promote sustainable development of CMfg.
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Keyword:
cloud manufacturing; genetic algorithm; linguistic preference; multi-objective optimization; service selection and scheduling
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URL: https://doi.org/10.3390/su11092619
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Hesitant Fuzzy Linguistic Term and TOPSIS to Assess Lean Performance
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In: Applied Sciences ; Volume 9 ; Issue 5 (2019)
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The Emergence and Rise of Industry 4.0 Viewed through the Lens of Management Fashion Theory
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In: Administrative Sciences ; Volume 9 ; Issue 3 (2019)
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The emergence and rise of industry 4.0 viewed through the lens of management fashion theory
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Multi-Stage Multi-Period Production Planning In Wire And Cable Industry ...
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Multi-Stage Multi-Period Production Planning In Wire And Cable Industry ...
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imbWBI: Classification of Business Entities on Multilingual Web: configuration optimization, auxiliary experimental reports and other resources ...
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imbWBI: Classification of Business Entities on Multilingual Web: configuration optimization, auxiliary experimental reports and other resources ...
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imbWBI: Classification of Business Entities on Multilingual Web - The Main Results ...
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imbWBI: Classification of Business Entities on Multilingual Web - The Main Results ...
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Delphi Prospection on Additive Manufacturing in 2030: Implications for Education and Employment in Spain
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In: Materials ; Volume 11 ; Issue 9 (2018)
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Designing Efficient Material Handling Systems Via Automated Guided Vehicles (AGVs)
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