Hasil untuk "Technological innovations. Automation"

I. Halachmi, M. Guarino, J. Bewley et al.

Consumption of animal products such as meat, milk, and eggs in first-world countries has leveled off, but it is rising precipitously in developing countries. Agriculture will have to increase its output to meet demand, opening the door to increased automation and technological innovation; intensified, sustainable farming; and precision livestock farming (PLF) applications. Early indicators of medical problems, which use sensors to alert cattle farmers early concerning individual animals that need special care, are proliferating. Wearable technologies dominate the market. In less-value-per-animal systems like sheep, goat, pig, poultry, and fish, one sensor, like a camera or robot per herd/flock/school, rather than one sensor per animal, will become common. PLF sensors generate huge amounts of data, and many actors benefit from PLF data. No standards currently exist for sharing sensor-generated data, limiting the use of commercial sensors. Technologies providing accurate data can enhance a well-managed farm. Development of methods to turn the data into actionable solutions is critical.

Masaya Onuma, Tatsuya Kubota, Atsushi Tsumita

Resource constraints often hinder companies from balancing economic goals with Responsible Research and Innovation (RRI). In the medical device sector, doctors are critical stakeholders in addressing this challenge, as they integrate patient, clinical, and societal needs into medical innovation. This study examines doctors' knowledge types and the timing of their engagement in innovation. Drawing on a survey of doctors at a hospital that has successfully integrated RRI into medical device development, we analyze involvement across innovation stages using three types of knowledge: needs-knowledge, solution knowledge, and regulatory-knowledge. The results show that solution knowledge is particularly important for early-stage participation, whereas all three knowledge types are significantly associated with involvement in later stages. These findings suggest that companies can more effectively implement both RRI and User Innovation by strategically selecting partners based on stage-specific knowledge requirements. Overall, the study highlights the value of a dynamic partner selection strategy as innovation progresses.

N. Rajagopal, Naila Iqbal Qureshi, S. Durga et al.

Technological efforts are currently being used across a broad array of industries. Through the combination of consumer choice and matching principle, the goal of this paper is to investigate the prospective implications of artificial intelligence systems on businesses’ outcomes. From an entrepreneurship standpoint, the research revealed that artificial intelligence systems can help with better decision‐making. What impact does the introduction of AI‐based decision‐making technologies have on organizational policymaking? The quirks of human and AI‐based policymaking are identified in this research based on five important contextual factors: precision of the choice search area, contribution to the innovation of the policymaking process and result, volume of the replacement collection, policymaking pace, and generalizability. We create a novel paradigm comparative analysis of conventional and automation judgment along these criteria, demonstrating how both judgment modalities can be used to improve organizational judgment efficiency. Furthermore, the research shows that, by involving internal stakeholders, they can manage the correlation among AI technologies and improve decision for businessmen. Furthermore, the research shows that customer preferences and industry norms can moderate the link between AI systems and superior entrepreneurial judgment. The goal of this work is to conduct a thorough literature analysis examining the confluence of AI and marketing philosophy, as well as construct a theoretical model that incorporates concerns based on established studies in the areas. This research shows that, in a setting with artificial intelligence systems, customer expectation, industry standards, and participative management, entrepreneurial strategic decisions are enhanced. This research provides entrepreneurs with technology means for enhancing decision‐making, illustrating the limitless possibilities given by AI systems. A conceptual approach is also formed, which discusses the four factors of profit maximization: relationship of AI tools and IT with corporate objectives; AI, organizational learning, and decision‐making methodology; and AI, service development, and value. This study proposes a way to exploit this innovative innovation without destroying society. We show real‐world examples of each of these frameworks, indicate circumstances in which they are likely to improve decision‐making performance in organizations, and provide actionable implications into their constraints. These observations have a wide variety of implications for establishing new management methods and practices from both academic and conceptual viewpoints.

Hoa Thi Nguyen, Md. Arifur Rahman Khan, Thu Thi Bich Nguyen et al.

Climate change intensifies biotic and abiotic stresses, threatening global crop productivity. High-throughput phenotyping (HTP) technologies provide a non-destructive approach to monitor plant responses to environmental stresses, offering new opportunities for both crop stress resilience and breeding research. Innovations, such as hyperspectral imaging, unmanned aerial vehicles, and machine learning, enhance our ability to assess plant traits under various environmental stresses, including drought, salinity, extreme temperatures, and pest and disease infestations. These tools facilitate the identification of stress-tolerant genotypes within large segregating populations, improving selection efficiency for breeding programs. HTP can also play a vital role by accelerating genetic gain through precise trait evaluation for hybridization and genetic enhancement. However, challenges such as data standardization, phenotyping data management, high costs of HTP equipment, and the complexity of linking phenotypic observations to genetic improvements limit its broader application. Additionally, environmental variability and genotype-by-environment interactions complicate reliable trait selection. Despite these challenges, advancements in robotics, artificial intelligence, and automation are improving the precision and scalability of phenotypic data analyses. This review critically examines the dual role of HTP in assessment of plant stress tolerance and crop performance, highlighting both its transformative potential and existing limitations. By addressing key challenges and leveraging technological advancements, HTP can significantly enhance genetic research, including trait discovery, parental selection, and hybridization scheme optimization. While current methodologies still face constraints in fully translating phenotypic insights into practical breeding applications, continuous innovation in high-throughput precision phenotyping holds promise for revolutionizing crop resilience and ensuring sustainable agricultural production in a changing climate.

Ferenc Erdős, R. Thinakaran, Bayboboeva Firuza et al.

Tourism ranks among the world's largest industries, and its sustained expansion has paralleled swift advancements in technology. Artificial Intelligence (AI) is increasingly recognized as a transformative force in tourism, offering human-like capabilities that enhance decision-making and service automation. Its application across the sector improves operational efficiency and personalizes customer experiences, thereby fostering innovation and competitiveness. However, the rapid integration of AI also presents conceptual, theoretical, and societal challenges that require critical examination. The research aims to synthesize the conceptual and theoretical research on AI in tourism from 2019 onwards. It examines key themes, theoretical perspectives, methodological rigor, and research gaps in the existing literature. Further goal is to identify thematic areas with a specific focus on AI applications. The study followed the PRISMA guidelines to conduct a systematic literature review (SLR). Academic databases, including Scopus and Web of Science, were searched to identify scientific-relevant peer-reviewed articles. From an initial pool of over 400 studies, we identified 45 significant journal articles and selected them for an in-depth analysis, that collectively illuminate how AI is reshaping tourism research and practice. Studies have drawn on innovation diffusion theory to explain adoption patterns, technology acceptance models to gauge user and employee attitudes, and service quality and cocreation theories to understand how AI can add value to the customer experience. It also highlighted the evolution of AI research in tourism, from conceptual discussions to empirical investigations. Gaps and challenges in the research were identified, including a limited focus on human-AI interaction, ethical concerns, and methodological rigor. The review concludes that AI has the potential to transform tourism by enhancing efficiency, personalization, and sustainability. The findings reveal that AI has been envisioned as a catalyst for transformation in the tourism industry, with applications ranging from intelligent forecasting and revenue management to service automation via robots and hyper-personalized travel experiences. AI-driven analytics can improve decision support for revenue management, capacity planning, and marketing strategy. However, realizing this potential requires addressing the improvement of technological competence of human resources, ethical issues, and implementation strategies.

Andreea-Ruxandra Ioniță, D. Anghel, T. Boudouh

In the context of rapidly evolving industrial ecosystems, the human–machine interaction (HMI) has shifted from basic interface control toward complex, adaptive, and human-centered systems. This review explores the multidisciplinary foundations and technological advancements driving this transformation within Industry 4.0 and the emerging paradigm of Industry 5.0. Through a comprehensive synthesis of the recent literature, we examine the cognitive, physiological, psychological, and organizational factors that shape operator performance, safety, and satisfaction. A particular emphasis is placed on ergonomic interface design, real-time physiological sensing (e.g., EEG, EMG, and eye-tracking), and the integration of collaborative robots, exoskeletons, and extended reality (XR) systems. We further analyze methodological frameworks such as RULA, OWAS, and Human Reliability Analysis (HRA), highlighting their digital extensions and applicability in industrial contexts. This review also discusses challenges related to cognitive overload, trust in automation, and the ethical implications of adaptive systems. Our findings suggest that an effective HMI must go beyond usability and embrace a human-centric philosophy that aligns technological innovation with sustainability, personalization, and resilience. This study provides a roadmap for researchers, designers, and practitioners seeking to enhance interaction quality in smart manufacturing through cognitive ergonomics and intelligent system integration.

Allen Munoriyarwa, Mathias-Felipe de-Lima-Santos

ABSTRACT This special issue interrogates how artificial intelligence (AI), particularly generative AI (GenAI), is reshaping journalism at a moment of profound uncertainty for the profession. The rapid rise of GenAI technologies, particularly following the release of tools like ChatGPT, has intensified longstanding tensions between economic precarity, technological innovation, and journalistic values. Across diverse contexts in the Global North and South, articles examine how AI is simultaneously heralded as a source of efficiency, personalization, and newsroom survival, while also feared as a destabilizing force that threatens jobs, erodes professional norms, and concentrates power in the hands of technology corporations. The collection foregrounds three interlocking themes: (i) the reconfiguration of journalistic agency, as decision-making increasingly shifts toward technological systems; (ii) the renegotiation of power within newsrooms, between journalism and the tech industry, and across global regions marked by an “AI divide”; and (iii) the contestation of journalistic authority, as human oversight, ethics, and accountability are defended as safeguards in an age of automation. By weaving together these studies, our special issue highlights both convergences—such as the importance of a “human-in-the-loop” model when adopting these technologies—and divergences, particularly between resource-rich and resource-constrained media environments. Collectively, these contributions demonstrate that AI’s impact on journalism is not deterministic but contingent, shaped by institutional norms, political economies, and local realities. Rather than offering predictions, this volume maps the contested terrain of AI-enabled journalism, offering a critical resource for scholars, practitioners, and students seeking to understand and shape the future of news in an age of intelligent machines.

Jianzheng Shi, Yue Wang

PurposeThe integration of blockchain technology and artificial intelligence (AI) is reshaping the financial services industry, offering transformative solutions in areas such as risk management, fraud detection, regulatory compliance and operational efficiency.Design/methodology/approachThis paper presents a systematic literature review of over 100 peer-reviewed studies published between 2020 and 2024, analyzing the benefits, challenges and future directions of blockchain-AI applications in financial services. Our findings reveal that while blockchain enhances data integrity, security and transparency, AI drives predictive analytics, automation and decision-making efficiency.FindingsThe synergy of these technologies holds significant potential yet faces critical challenges related to scalability, interoperability, regulatory compliance and ethical AI governance. We identify key research gaps, including the lack of standardized regulatory frameworks, limited real-world case studies and technical barriers to integration. To address these gaps, we propose a comprehensive theoretical framework linking technological advancements to regulatory and ethical considerations. This study contributes to both academic discourse and industry practice, offering actionable insights for financial institutions, technology developers and policymakers navigating the rapidly evolving FinTech landscape.Research limitations/implicationsThe rapidly evolving nature of blockchain and AI technologies may limit the long-term applicability of some findings. The study primarily focuses on published academic literature, potentially overlooking some industry-specific developments. Future research should address the identified gaps, particularly in cross-chain interoperability, ethical AI frameworks, and long-term economic impacts. Empirical studies and case analyses could further validate the theoretical insights presented in this review.Originality/valueThis study provides a novel, comprehensive synthesis of blockchain and AI applications in financial services, offering valuable insights for both academics and practitioners. By critically examining the synergies and challenges of these technologies, it presents a unique perspective on their transformative potential in FinTech. The proposed research agenda addresses crucial gaps in current knowledge, guiding future investigations. The findings contribute to a deeper understanding of the complex interplay between technological innovation, regulatory frameworks and ethical considerations in the evolving landscape of financial services.

Iboro Akpan Essien, Emmanuel Cadet, Joshua Oluwagbenga Ajayi et al.

The accelerating complexity of global regulatory frameworks, driven by rapid technological advancements, cross-border transactions, and shifting socio-economic priorities, has placed unprecedented demands on organizations to maintain continuous compliance. Traditional compliance management systems, often rule-based and manually updated, struggle to adapt to the dynamic and fragmented nature of these evolving landscapes. This paper proposes the design of intelligent compliance systems that leverage artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) to automate regulatory monitoring, interpretation, and enforcement. By integrating real-time data streams from multiple jurisdictions, the proposed system employs semantic analysis to extract, classify, and map regulatory requirements to organizational policies, operational processes, and risk controls. A modular architecture is developed to ensure scalability, interoperability, and adaptability, enabling sector-specific customization and rapid incorporation of regulatory changes. The system incorporates predictive analytics to forecast regulatory trends, simulate compliance scenarios, and recommend proactive adjustments, thereby transforming compliance from a reactive obligation into a strategic advantage. Furthermore, explainable AI techniques are embedded to enhance transparency and trust, ensuring that automated decisions align with both legal mandates and ethical standards. Case studies across finance, healthcare, and energy sectors illustrate how intelligent compliance systems reduce operational risk, lower compliance costs, and improve audit readiness. The research underscores the importance of harmonizing technological innovation with robust governance frameworks to mitigate algorithmic bias, protect sensitive data, and meet jurisdiction-specific legal obligations such as GDPR, CCPA, and sectoral regulations. This work concludes that intelligent compliance systems represent a paradigm shift, enabling organizations to navigate the evolving global regulatory landscape with agility, accuracy, and strategic foresight, while fostering regulatory harmonization and operational resilience in an increasingly interconnected world. Keywords: Intelligent Compliance Systems, Artificial Intelligence, Machine Learning, Regulatory Technology, RegTech, Global Regulations, Natural Language Processing, Predictive Analytics, Explainable AI, Compliance Automation, Governance Frameworks, Operational Resilience, Risk Management, Legal Technology, Regulatory Harmonization.

Fuqing Gao, Shaoran Shi, Yang Zhao et al.

Precision fermentation represents an innovative cell-based production approach that employs synthetic biology and metabolic engineering tools, revolutionizing global food production by utilizing "microbial cell factories" to produce added-value ingredients. However, its global implementation is hindered by technological and scalability bottlenecks, regulatory fragmentation, regional accessibility and consumer acceptance, and nutritional trade-offs challenges. This review utilizes illustrated case studies and modeling analysis to present a detailed exploration of precision fermentation intersecting with global cell-based food production, discussing actionable research gaps and insights as well as advanced bioengineering practices and analytical techniques, to address these challenges for ongoing academic research, industrial applications and policy initiatives, thus supporting the transition of fermentation-enabled food production toward efficient and sustainable manufacturing. Moreover, attention is also dedicated to ethical concerns such as intellectual property monopolies and equitable technology access in low-resource regions. We highlighted crucial elements such as synthetic biology and metabolic engineering tools with advancements in precision nutrition, recognizing their crucial roles in large-scale fermentation-enabled production, market adoption, and elaborating on the "hidden hunger" hypotheses regarding the mechanism of potential "Nutritional starvation" risk and proposing mitigation strategies. Adopting computer-aided engineering (CAE), artificial intelligence (AI), and automation to refine fermentation processes presents promising avenues for enhancing production efficiency and sustainability, while the limitations of these tools and research priorities are also discussed. We further propose a visionary framework where upcoming food innovations meet consumer expectations for health and environmental responsibility, ultimately propelling the field of fermented food into a new era of technological sophistication and societal impact.

Rezwanul Islam Rezvi, Kazi Obaidur Rahman, Md Asif Hasan et al.

Today’s business world is heading towards digital platforms aiming to reach more customers by performing sentiment analysis and recommending related products considering customers’ choice and preferences to enhance sales revenue and maximize profitability. Digital marketing plays a crucial role in this transformation by connecting digital stores with their target audiences through various online channels. This article explores how digital marketing affiliates digital stores in terms of sales enhancement, visibility, and customer engagement on platforms such as Shopify, Amazon, and Walmart. It also explores the challenges of affiliate fraud, SEO competition, and transparency issues in digital retail. Thematic analysis of the existing literature was performed with an emphasis on crucial themes of social media's role in affiliate marketing, search engine optimization for traffic generation, content marketing effectiveness, AI-embedded automation, and customer retention strategies. Only peer-reviewed studies and industry reports were considered. Digital marketing affiliates use influencer promotions, SEO optimization, and AI-driven automation to promote digital stores and increase conversions. Although its amplification of reach and engagement are significant benefits, the concerns of fraudulent actions, algorithmic bias and saturation within affiliate schemes pose questions about its effectiveness as a business practice. Leveraging AI-powered tracking tools, streamlining SEO strategies, and upholding ethical transparency in influencer partnerships position businesses to harness the full potential of affiliate marketing. Using blockchain for secure affiliate tracking can add to increasing credibility and trust. Future research should focus on long-term perspectives regarding the effects of AI in affiliate marketing, changing dynamics of influencer-led marketing strategies, and the effect of overall technological innovations on the effectiveness of affiliate programs.

Sijie Fu, Xianqing Xiong, Ruiying Wan et al.

The Chinese furniture business has experienced substantial transformation due to swift progress in materials, production, and supply chain technologies. This study comprehensively analyses China's furniture sector's current and future problems, emphasizing alignment with global sector 4.0 trends. The approach utilizes a combination of literature evaluation, trend analysis, and empirical research across five domains: materials, design, production, management, and supply chain. The results indicate swift progress in material innovation, design variety, intelligent production, and supply chain enhancement. Nonetheless, deficiencies persist in design innovation and brand development relative to the European furniture sector, especially in premium customization and technical advancement. Chinese furniture companies exhibit adaptability and localization benefits in global supply chains, enhancing their competitiveness; nonetheless, greater alignment with European counterparts is necessary regarding automation technology and environmental requirements. The research underscores disparities in the digital transformation of solid wood, aluminium, and bamboo rattan furniture, which hinder comprehensive technological advancement. Future advancement needs enhanced international cooperation and technological exchange to propel industrial development. Emphasizing sustainable methods and eco-friendly production is crucial to attaining high-quality development. This research presents a thorough framework for comprehending the industry's present condition and prospects, providing strategic direction for professionals and academics. It emphasizes the significance of cross-regional collaboration and dialogue in promoting global innovation and sustainability in the furniture sector.

Kurniawan Arif Maspul, N. Putri

The rapid integration of big data and artificial intelligence (AI) is fundamentally reshaping Indonesia’s financial sector, driving unprecedented efficiency, innovation, and financial inclusion. As Southeast Asia’s largest digital economy, Indonesia has embraced fintech solutions that leverage predictive analytics, machine learning, and automation to enhance risk management, streamline transactions, and expand financial services to previously underserved populations. This transformation aligns with global financial trends, yet it presents distinct regulatory, infrastructural, and ethical challenges. Drawing from Schumpeter’s Innovation Theory, Information Asymmetry Theory, and Transaction Cost Economics, this study explores how big data and AI redefine financial operations, improve decision-making, and reduce market inefficiencies in the Indonesian banking ecosystem. Utilizing a qualitative phenomenological approach, this research synthesizes insights from industry experts, regulatory bodies, and financial analysts to assess the implications of data-driven strategies. Findings reveal that while big data optimizes risk assessment, fraud detection, and customer segmentation, regulatory hurdles, cybersecurity risks, and digital literacy gaps remain key barriers to sustainable adoption. As Indonesia continues its trajectory toward a data-centric financial infrastructure, balancing technological advancement with regulatory prudence will be critical in shaping an inclusive and resilient financial future. This study contributes to ongoing discourse on the intersection of financial digitalization, economic policy, and ethical AI deployment in emerging markets.

AbdelKarim Fawwaz AlBataineh

Prachi Radadiya, K. Shah, Nishant Doshi

The increasing complexity of cyber threats has exceed traditional security responses requiring intelligible, sophisticated approaches to securing digital assets and infrastructures. It investigates the role of Artificial Intelligence (AI) automation in cybersecurity, while machine learning and data analytics are employed to monitor real-time threats, manage predictive vulnerabilities, and automate incident response. Improved response time, problem scalability, and increased resource efficiency are some of the key advantages. The significant difference that this ongoing research brings to the already existent ones is in the introduction of novel strategies for integrating ethical frameworks aimed at minimizing algorithmic biases and providing transparency into AI-driven security programs. The text also takes into account, case studies and emerging trends while tackling such critical challenges as adversarial attacks, data integrity problems, and system integration complexities. The findings offer useful and pragmatic policy recommendations for developing elegantly adaptive and resilient cyber-secure ecosystems further embodying intervention policies in the interest of harmonizing technological innovation and ethical governance.

Mostafa Elmarzouki, Wang Jiuhe

Purpose This study aims to bridge the gap between traditional continuous improvement methodologies and emerging digital technologies by systematically analyzing how hybrid innovation models – integrating Lean Manufacturing, Six Sigma and Industry 4.0 tools – drive productivity gains across industries. Design/methodology/approach A systematic literature review of 96 empirical studies was conducted, synthesizing evidence from diverse sectors (e.g. manufacturing, construction, services), geographical regions and temporal contexts. The analysis focused on operationalizing incremental innovation through the lens of Lean, Six Sigma and Industry 4.0 integration. Findings Lean principles (e.g. Kaizen, 5S) and Six Sigma methodologies (e.g. DMAIC, defect reduction) remain critical for process optimization, particularly in resource-constrained settings. Industry 4.0 tools (e.g. IoT, automation) amplify these gains through real-time data analytics and workflow automation. Sectoral analysis reveals significant productivity improvements in manufacturing (15%–25% waste reduction) and construction (20% efficiency gains), while health care and finance exhibit implementation gaps because of rapid technological evolution. Practical implications Practitioners can adopt a phased integration strategy, prioritize Lean/Six Sigma for foundational process stability and layer Industry 4.0 tools for advanced analytics and automation; sector-specific roadmaps are provided to address challenges like workforce readiness and legacy system compatibility. Originality/value This study contributes to the literature by synthesizing 96 empirical studies to offer a comprehensive view of how traditional continuous improvement methods (Lean, Six Sigma) are increasingly integrated with digital tools (Industry 4.0) to drive incremental innovation. Unlike prior research that treats these mechanisms separately, this review maps out hybrid models, highlights sectoral and geographical variations and provides a temporal analysis of incremental innovation’s evolution.

Manevannan Ramasamy

The evolution of network management has witnessed a transformative integration of artificial intelligence with human expertise, revolutionizing how organizations handle their network infrastructure. This integration addresses the growing complexity of modern networks while enhancing operational efficiency and decision-making capabilities. The synergy between human expertise and AI automation has enabled organizations to optimize resource allocation, improve security measures, and maintain network reliability. Through balanced automation and strategic oversight, organizations can leverage AI capabilities while ensuring human insight remains central to critical decision-making processes. This collaborative approach marks a paradigm shift in network management, where AI systems handle routine tasks and data processing while human experts focus on strategic planning and complex problem-solving. The integration has particularly impacted areas such as predictive maintenance, security threat detection, and performance optimization, leading to more resilient and adaptive network infrastructures. Organizations implementing this human-AI collaboration model have demonstrated enhanced ability to manage increasing network complexity, respond to emerging challenges, and maintain competitive advantages in rapidly evolving technological landscapes. The transformation extends beyond operational improvements, fostering innovation in network engineering roles and creating new opportunities for professional development in emerging technologies while maintaining the essential human element in network oversight and strategic direction.

Howard Jun Hao Oh, Kia Wai Liew, Poh Kiat Ng et al.

The objective of this study is to design, develop, and analyze a tri-wheeled trolley integrated with a motor that incorporates adjustable and foldable features. The purpose of a trolley is to allow users to easily transport items from one place to another. However, problems arise when transporting objects across challenging surfaces, such as up a flight of stairs, using a conventional cart. This innovation uses multiple engineering skills to determine and develop the best possible design for a stair-climbing trolley. A tri-wheel mechanism is integrated into its motorized design, meticulously engineered for adjustability, ensuring compatibility with a wide range of staircase dimensions. The designed trolley was constructed considering elements and processes such as a literature review, conceptual design, concept screening, concept scoring, 3D modelling, engineering design calculations, and simulations. The trolley was tested, and the measured pulling force data were compared with the theoretical calculations. A graph of the pulling force vs. load was plotted, in which both datasets showed similar increasing trends; hence, the designed trolley worked as expected. The development of this stair-climbing trolley can benefit people living in rural areas or low-cost buildings that are not equipped with elevators and can reduce injuries among the elderly. The designed stair-climbing trolley will not only minimize the user’s physical effort but also enhance safety. On top of that, the adjustable and foldable features of the stair-climbing trolley would benefit users living in areas with limited space.

Zhengrui Jiang, Wang Chen, Xiaojun Zheng et al.

Abstract The authors investigate new Automated Guided Vehicle (AGV) Routing Problem with Simultaneous Pickup and Delivery with Mixed Time Windows (VRPSPDMTW) in smart workshops, a variation of the classic Vehicle Routing Problem (VRP). A mixed time window vehicle routing model was developed for simultaneous deliveries. This model reduces the cost of AGVs used and distribution cost, along with time window penalties. To address this complex challenge, a Hybrid Adaptive Genetic Algorithm using Variable Neighbourhood Search (AGA‐VNS) is proposed. This algorithm enhances the genetic algorithm's local search capabilities while preserving solution diversity, thereby improving both efficiency and quality of solutions. Comprehensive computational experiments are conducted, which include both VRPSPDTW test benchmark and real‐world smart factory instance studies. The outcomes reveal that the AGA‐VNS algorithm outperforms both professional solver software and advanced heuristic methods significantly. Moreover, the newly developed mixed time window model is more aligned with the requirements of real‐world production processes compared to the traditional time window model. Thus, this research not only presents novel insights into the domain of vehicle routing problems but also demonstrates its significant applicability and potential in the background of intelligent workshops.

Yasmin da Silva Gonçalves da Rocha, Lucas Galdioli, Rita de Cassia Maria Garcia

O objetivo foi criar uma ferramenta tecnológica para mapear os abrigos de animais e formar um banco de dados nacional padronizado, com estatísticas sobre a dinâmica populacional desses locais, promovendo a ciência da medicina de abrigos no Brasil. Para isso, uma pesquisa de opinião foi analisada estatisticamente com o software R 4.2.2 e Statistica 12.1, utilizando os testes ANOVA de Kruskal-Wallis e Mann-Whitney, com significância de 5%. A análise temática de conteúdo das respostas discursivas ocorreu por meio do software MAXQDA 24 e a metodologia DADI foi utilizada para o desenvolvimento do site. A pesquisa de opinião contou com 300 respondentes, predominantemente de São Paulo (43,3%) com faixa etária de 26 a 35 anos (30,7%). Os gestores dos abrigos manifestaram interesse em atualizar as informações (94,1%) e cadastrar as instituições em site de mapeamento (97,1%). No geral, 90,3% consideraram a ideia do site interessante, com opiniões estatisticamente consistentes. As respostas discursivas destacaram a importância de promover informações sobre a medicina de abrigos. O site é robusto, interativo e educativo com potencial para se tornar uma referência nacional em mapeamento e dados estatísticos de abrigos de animais, possibilitando grandes mudanças no cenário nacional.