A Deep Learning and Metaheuristic Optimization Framework for Predictive Maintenance Scheduling and Decision Support in Smart Industrial Systems

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dc.contributor.authorYoosomboon, Kanit
dc.contributor.authorThipayasothorn, Pastraporn
dc.contributor.authorKijmongkolvanich, Sirisyos
dc.contributor.authorEgwutvongsa, Songwut
dc.date.accessioned2026-08-06T10:50:56Z
dc.date.available2026-08-06T10:50:56Z
dc.date.issued2025-04-01
dc.description.abstractThis study aimed to construct a comprehensive framework that fosters autonomous decision-making among Thai micro-entrepreneurs located in the lower central region, with an emphasis on enhancing packaging innovation and supporting long-term sustainable development. A quantitative research approach was adopted, utilising a stratified sampling method to gather data from a total of 316 respondents. Path analysis was employed to examine the underlying dimensions that affect self-reliance in packaging innovation. The analysis identified four principal dimensions as vital for achieving sustainable packaging design: technological competence, economic independence, psychological resilience, and socio-cultural integration. The findings also indicated that technological self-reliance exerted an indirect influence on sociocultural integration, with culturally oriented design perspectives accounting for 19.6 percent of the variance observed. The outcomes underscore the significance of community-based decision-making frameworks that empower micro-entrepreneurs. These models hold potential to inform policy interventions and shape educational content directed at sustainable practice adoption, especially within the context of developing nations aiming to reinforce community resilience. The study offers a transferable model that demonstrates how community participation can be effectively connected to innovative packaging strategies. This approach promotes both economic sustainability and the preservation of cultural identity among micro-enterprises operating within the small business sector.
dc.identifier.citationDecision Making Applications in Management and Engineering, 8(1), 657-671, 2025
dc.identifier.doi10.31181/dmame8120251460
dc.identifier.issn25606018
dc.identifier.other2-s2.0-105018098615
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/16920
dc.sourceDecision Making Applications in Management and Engineering
dc.subjectDecision-Making Model
dc.subjectMicro-Entrepreneurs
dc.subjectPackaging Innovation
dc.subjectSelf-Reliance
dc.subjectSustainable Development
dc.titleA Deep Learning and Metaheuristic Optimization Framework for Predictive Maintenance Scheduling and Decision Support in Smart Industrial Systems
dc.typeArticle

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