Search Results (3,076)

Amid China’s “Manufacturing Power” push, full-chain digital restructuring in the Guangdong–Hong Kong–Macao Greater Bay Area remains hampered by mismatches among technology, organization, and environment. We therefore explored how shop floor actors perceive and shape this Technology–Organization–Environment (TOE) interplay. Semi-structured interviews with frontline operators, maintainers, and supply chain staff from GBA manufacturers were inductively coded, yielding 36 concepts, 10 categories, and 3 core TOE aggregates that were woven into a grounded model. The analysis shows that industrial internet platforms and smart equipment only create value when matched by flexible shop floor structures, cross-department data protocols, and skilled teams; otherwise, data silos, simulation–production deviations, and “buy-but-not-build” procurement stall adoption. Market pressure for customized, short-lead-time products and divergent municipal pilot policies further intensify the TOE balancing act, particularly for SMEs with weak absorptive capacity. By revealing a grassroots “technology-driven → organization-adapted → environment-adjusted” spiral that is moderated by frontline feedback, the study extends the TOE framework to micro-level, regional innovation theory and offers policy–practice levers for differentiated, cross-city manufacturing upgrading. Full article

This paper addresses the limitations of genetic algorithms in solving the Flexible Job-Shop Scheduling Problem (FJSP) including slow convergence, susceptibility to local optima, and sensitivity to parameter settings. The paper proposes an Improved Genetic Algorithm based on Reinforcement Learning (IGARL). First, a hybrid population selection mechanism that combines the Queen Bee Mating Flight (QBMF) strategy with the Tournament Selection (TS) method is introduced. This mechanism significantly accelerates convergence by optimizing the population structure. Second, a dynamic population update strategy based on tunnel vision, termed the Solution Space Diversity Awakening (SSDA) strategy, is developed. When the population becomes trapped in local optima, this strategy intelligently triggers random perturbations and introduces high-potential individuals to enhance the algorithm’s ability to escape local optima and promote population diversity. Third, a novel multi-Q-table reinforcement learning framework is embedded within the iterative process to dynamically adjust key genetic algorithm parameters (such as selection, mutation, and crossover rates) and enable multi-dimensional performance evaluation, thereby effectively guiding the search toward better solutions. Experimental results demonstrate that the IGARL algorithm achieves a 10% to 60% improvement in convergence speed on Brandimarte benchmark instances, with solution quality significantly surpassing that of the basic genetic algorithm. Moreover, the fluctuation of the average optimal solution remains within 20%, indicating strong stability and robustness. Full article

International shopping is a significant motive for outbound travel; however, evidence on the experiential drivers of luxury travel retail among Chinese luxury travelers remains limited. This study investigates the factors shaping overseas shopping experiences and assesses the adequacy of the luxury customer experience (LCX) framework in this episodic, time-constrained, cross-border context. A quantitative survey of Chinese luxury travelers (N = 407) was conducted and analyzed using IBM SPSS Statistics (Version [30.0], Mac) within the LCX framework. The results show that modern artistic visual merchandising positively predicts overall experience evaluation (β = 0.162, p < 0.001), and emotional connection significantly predicts repurchase intention (β = 0.197, p < 0.001). We find that overall experience evaluation and subsequent behavioral responses are shaped by specific drivers, including service-related post-purchase factors, emotional fulfillment and brand trust, visual appeal, and affective/cognitive evaluations. These results point to possible gaps in theory when LCX is used in short-term travel retail contexts. To address these gaps, we propose the transient experience, relationship quality, action outcomes, connection, and engagement (TRACE) conceptual framework for analyzing feedback-driven encounters throughout the travel experience. Overall, this study extends LCX to episodic, time-constrained contexts and introduces TRACE as a conceptual complementary model to guide future theory testing and model validation in luxury travel retail contexts. Full article

Determination of the right price is vital for the success of newly developed food products. This study examined the market prices and their determinants for five ready-to-cook catfish products: Panko-Breaded Standard Strips (PBSS), Panko-Breaded Standard Fillet (PBSF), Panko-Breaded Delacata Fillet (PBDF), Sriracha-Marinated Delacata Fillet (SMDF), and Sesame-Ginger-Marinated Delacata Fillet (SGMDF). Market prices were derived using Vickrey’s second-price auction, where the second-highest bid represents the market price. We analyzed experimental auction data from 121 consumers using a logit model to estimate the probability of offering the market price based on product sensory attributes, socio-demographic characteristics of the participants, and the level of competition (panel size). Consumers’ willingness-to-pay (WTP) was elicited in two rounds: before tasting (visual evaluation) and after tasting (organoleptic evaluation) the products. Breaded products received higher market prices than marinated products, with PBDF ranked highest. Sensory traits, especially taste, along with income, education, and grocery shopping involvement, significantly influenced the formation of market price. Increased competition elevated the market prices. Both product features and consumer characteristics significantly affect market price outcomes, and experimental auctions provide a robust tool for understanding consumer behavior toward newly developed food products. Full article

Traditional sustainability frameworks in large scale production systems, such as Process Industry (PI) ones, often overlook operational resilience, creating a “resiliency gap” where systems optimized for efficiency remain vulnerable to disruptions. This study addresses this gap by proposing and empirically validating a Quadruple Bottom Line (4BL) framework that integrates resilience as the fourth pillar alongside economic, environmental, and social goals. The purpose is to evaluate the impact that Autonomic Computing (AC) can imply in this perspective. A Procedural Action Research (PAR) methodology was conducted across four distinct PI industrial cases (asphalt, steel, pharma, and aluminum). This involved the ECOGRAI framework to qualitatively link strategic companies’ objectives to shop-floor Key Performance Indicators (KPIs), guiding the assessment of AC systems. The results show benefits at a business level observed following the introduction of AC systems, which were implemented for enhancing resilience by managing ML model drift. Key findings include reduction in plant downtimes, decreases in waste (steel), reductions in gas consumption, and improved operator trust. This research provides empirical evidence that AC can make resilience an actionable component of industrial strategy, leading to measurable improvements across all four pillars of the 4BL framework. Its contribution is methodological and operational, aiming to demonstrate feasibility and causal plausibility. Full article

With the development of manufacturing industry, traditional fixed process processing methods cannot adapt to the changes in workshop operations and the demand for small batches and multiple orders. Therefore, it is necessary to introduce multiple robots to provide a more flexible production mode. Currently, some Job Shop Scheduling Problems with Transportation (JSP-T) only consider job scheduling and vehicle task allocation, and does not focus on the problem of collision free paths between vehicles. This article proposes a novel solution framework that integrates workshop scheduling, material handling robot task allocation, and conflict free path planning between robots. With the goal of minimizing the maximum completion time (Makespan) that includes handling, this paper first establishes an extended JSP-T problem model that integrates handling time and robot paths, and provides the corresponding workshop layout map. Secondly, in the scheduling layer, an improved Deep Q-Network (DQN) method is used for dynamic scheduling to generate a feasible and optimal machining scheduling scheme. Subsequently, considering the robot’s position information, the task sequence is assigned to the robot path execution layer. Finally, at the path execution layer, the Priority Based Search (PBS) algorithm is applied to solve conflict free paths for the handling robot. The optimized solution for obtaining the maximum completion time of all jobs under the condition of conflict free path handling. The experimental results show that compared with algorithms such as PPO, the scheduling algorithm proposed in this paper has improved performance by 9.7% in Makespan, and the PBS algorithm can obtain optimized paths for multiple handling robots under conflict free conditions. The framework can handle scheduling, task allocation, and conflict-free path planning in a unified optimization process, which can adapt well to job changes and then flexible manufacturing. Full article

Lean implementations often deploy tools in isolation, leaving gaps in how abnormalities are exposed, resolved at the root cause, escalated when needed, and converted into organisational learning. This study proposes a five-stage closed-loop routine for daily factory management that integrates problem visibility, standardised shop-floor cadence, disciplined problem-solving, and tiered escalation within a single operating logic. The novelty lies not in the individual Lean tools, but in the specification of cadence, triggers, accountable roles, and verification steps that connect them into a replicable end-to-end routine. The model was evaluated through a 19-month longitudinal, single-site field intervention (quasi-experimental before–and–after) on the bottleneck production line of a pharmaceutical plant in Hengoed, Wales (UK). Line OEE increased by over 50% in relative terms. At factory level, total output increased by 20% year-on-year in 2024 (context indicator), alongside qualitative field observations of shorter time-to-resolution and improved cross-functional coordination. As a single-site study, external validity is context-dependent; nevertheless, the paper provides a specified closed-loop routine and field evidence on the operational effects of embedding an integrated Lean cycle into daily management. Practically, the study provides a specified routine that practitioners can replicate and adapt; academically, it contributes to Lean implementation research by showing how tool bundles can be operationalised as an end-to-end daily management routine with observable performance effects. Full article

Objective: Cosmetic products are widely used, yet public awareness of their potential health risks and of cosmetovigilance remains limited. Given that studies increasingly highlight chemical exposure associated with cosmetics, this study aimed to assess public knowledge, attitudes, and behaviours regarding cosmetic use, toxicity, and cosmetovigilance in Türkiye. Methods: A cross-sectional study was conducted among the general population living in Türkiye, consisting of 700 people between January and May 2024. The study was conducted using a Google survey form. Results: Among 700 participants, 91.6% reported regular cosmetic use and 47.6% experienced at least one adverse effect, most commonly redness, itching, and burning. Adverse effects were more frequently associated with products purchased from shopping malls/cosmetic stores. Education level was significantly linked to awareness of cosmetovigilance and product preferences, with university graduates showing higher awareness and favoring both local and international brands. Conclusion: The study revealed that although cosmetic use is common in Türkiye, awareness of cosmetovigilance remains low, even among well-educated consumers. Many participants reported adverse effects but did not seek professional consultation, indicating gaps in safety practices and reporting. Strengthening public awareness and establishing effective cosmetovigilance systems are essential to ensure safer cosmetic use and protect public health. Full article

The opto-mechanical automated manufacturing process, characterized by stringent process constraints, dynamic disturbances, and conflicting optimization objectives, presents significant control challenges for traditional scheduling and control approaches. We formulate the scheduling problem within a closed-loop control paradigm and propose a novel bi-level intelligent control framework integrating Deep Reinforcement Learning (DRL) and Bayesian Optimization (BO). The core of our approach is a bi-level intelligent control framework. An inner DRL agent acts as an adaptive controller, generating control actions (scheduling decisions) by perceiving the system state and learning a near-optimal policy through a carefully designed reward function, while an outer BO loop automatically tunes the DRL’s hyperparameters and reward weights for superior performance. This synergistic BO-DRL mechanism facilitates intelligent and adaptive decision-making. The proposed method is extensively evaluated against standard meta-heuristics, including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), on a complex 20-jobs × 20-machines flexible job shop scheduling benchmark specific to opto-mechanical automated manufacturing. The experimental results demonstrate that our BO-DRL algorithm significantly outperforms these benchmarks, achieving reductions in makespan of 13.37% and 25.51% compared to GA and PSO, respectively, alongside higher machine utilization and better on-time delivery. Furthermore, the algorithm exhibits enhanced convergence speed, superior robustness under dynamic disruptions (e.g., machine failures, urgent orders), and excellent scalability to larger problem instances. This study confirms that integrating DRL’s perceptual decision-making capability with BO’s efficient parameter optimization yields a powerful and effective solution for intelligent scheduling in high-precision manufacturing environments. Full article

As one of the major sources of global carbon emissions, the manufacturing industry urgently requires green transformation. The utilization of renewable energy in production workshop offers a promising route toward zero-carbon manufacturing. However, renewable energy fluctuations and dynamic workshop events make efficient scheduling increasingly challenging. This paper introduces a low-carbon and energy-efficient dynamic flexible job shop scheduling problem oriented towards renewable energy integration, and develops a multi-agent deep reinforcement learning framework for dynamic and intelligent production scheduling. Inspired by the Proximal Policy Optimization (PPO) algorithm, a routing agent and a sequencing agent are designed for machine assignment and job sequencing, respectively. Customized state representations and reward functions are also designed to enhance learning performance and scheduling efficiency. Simulation results demonstrate that the proposed method achieves superior performance in multi-objective optimization, effectively balancing production efficiency, energy consumption, and carbon emission reduction across various job shop scheduling scenarios. Full article

Digital retail is undergoing a paradigm shift driven by the deep integration of artificial intelligence (AI) and augmented reality (AR). Although prior studies have examined the independent effects of AI-based personalized recommendation (cognitive path) and AR-enabled immersion (experiential path), how their integration systematically shapes user behavior through internal psychological mechanisms remains an important unresolved theoretical gap. To address this gap, this study develops an integrated model grounded in the stimulus–organism–response (S-O-R) framework and trust transfer theory. Specifically, the model examines how personalized recommendation, as a dynamic external stimulus, influences users’ cognitive state (perceived usefulness) and experiential state (immersion); how the overall trust of users in the integrated platform can be used as a key boundary condition to adjust the transformation efficiency from the above stimulus to the internal state; and how the above cognitive and experiential states can ultimately drive the continued usage intention through the mediation of positive emotional response. Based on survey data from 400 Chinese consumers with AR shopping experience on Taobao, analyzed using structural equation modeling (SEM), the results indicate that (1) personalized recommendation positively affects both immersion and perceived usefulness; (2) platform trust significantly and positively moderates the effects of personalized recommendation on both immersion and perceived usefulness; (3) both cognitive and experiential states stimulate positive emotions, which in turn enhance continued usage intention, with perceived usefulness exerting a stronger effect; (4) a key theoretical finding is that there is a significant positive correlation between perceived usefulness and immersion, revealing the coupling of psychological paths in an integrated environment; however, immersion does not moderate the effect of personalized recommendation on emotional responses, suggesting that the current integration mode emphasizes the formation of a stable psychological structure rather than real-time interaction. This study makes three contributions to the existing literature. First, it extends the application of S–O–R theory in a complex technological environment by analyzing the “organism” as a parallel and related cognitive-experience dual path and confirming its coupling relationship. Second, it elucidates the enabling role of trust as a moderating mechanism rather than a direct antecedent, thereby enriching micro-level evidence for trust transfer theory in the context of technology integration. Finally, by contrasting path coupling with process regulation, this study provides a more detailed distinction for understanding the theoretical connotations and boundaries of AI–AR technology integration, which may mainly be a kind of structural integration. Full article

Staphylococcus aureus has been recognized as an important foodborne pathogen and methicillin-resistant S. aureus (MRSA) can cause fatal infections worldwide. Of great concern is that MRSA have been found in animals and non-healthcare settings; however, knowledge about the prevalence and genetic characteristics of S. aureus, especially MRSA from animals, retail meats and market shared shopping vehicles in the same district, is limited. In this study, we collected 423 samples including handrail swabs (n = 226) of shopping trolleys and baskets from 18 supermarkets, retail meats (n = 137) and swine nasal swabs (n = 60) between 2018 and 2020 in China. S. aureus isolates were isolated and identified by PCR, and then the mecA was used to confirm the MRSA. The antibiotic resistance and virulence genes among S. aureus were also analyzed, followed by whole genome sequencing (WGS). S. aureus isolates were widely distributed in shared shopping vehicles (8.0%, 18/226), retail meats (14.6%, 20/137) and swine (18.3%, 11/60). In total, 49 S. aureus were obtained and 20 of the 49 isolates were MRSA. We firstly reported a high prevalence of MRSA in shared shopping vehicles (7.5%, 17/226), followed by raw meats (2.2%, 3/137), and 44.4% (8/18) of the 18 supermarkets possessed MRSA-positive shopping vehicles. All 20 MRSA isolates were SCCmec IVa MRSA clones. Enterotoxin genes (sea/seb) associated with S. aureus food poisoning were present in 45.0% of the 20 S. aureus isolates from retail meats and 25.0% of the 20 MRSA isolates carried enterotoxin genes. Retail meats in this study carried ST6-MRSA, a common ST type of S. aureus from food-poisoning outbreaks in China. WGS showed that the MRSA from meats harbored enterotoxin gene sea and immune evasion genes (sak and scn) associated with human infections, and were clustered with previously reported MRSA isolates from animals and humans. The MRSA isolates carrying multiple virulence genes from shopping vehicles were also clustered with previously reported MRSA isolates from humans and animals, suggesting that the exchange of MRSA isolates might occur among different niches. Our results highlighted the risk of retail meats and shared shopping vehicles in spreading antimicrobial-resistant pathogens including MRSA. To our knowledge, this is the first report of the wide spread of MRSA in shared shopping vehicles in China. Full article

Most multi-objective studies on distributed hybrid flow shops that include tardiness-related objectives focus solely on optimizing makespan alongside a single tardiness objective. However, in real-world scenarios with strict contractual deadlines or high penalty costs for delays, minimizing both total tardiness and the number of tardy jobs becomes critically important. This paper addresses this gap by prioritizing tardiness-related objectives while simultaneously optimizing makespan, total tardiness, and the number of tardy jobs. It investigates a distributed hybrid flow shop scheduling problem (DHFSP), which has some symmetries on machines. We propose an improved multi-objective memetic algorithm incorporating Q-learning (IMOMA-QL) to solve this problem, featuring (1) a hybrid initialization method that generates high-quality, diverse solutions by balancing all three objectives; (2) a multi-factory SB2OX crossover operator preserving high-performance job sequences across factories; (3) six problem-specific neighborhood structures for efficient solution space exploration; and (4) a Q-learning-guided variable neighborhood search that adaptively selects neighborhood structures. Based on extensive numerical experiments across 100 generated instances and a comprehensive comparison with four comparative algorithms, the proposed IMOMA demonstrates its effectiveness and proves to be a competitive method for solving the DHFSP. Full article

Food waste undermines the four dimensions of food security, availability, accessibility, utilization, and stability, while imposing adverse economic, social, and environmental impacts on sustainable food systems. Understanding the behavioral determinants of food consumption rationalization is essential for addressing this challenge in the Kingdom of Saudi Arabia. This study examines household food waste behaviors within a knowledge-based framework that integrates three interconnected constructs: awareness of food waste consequences, behavioral knowledge of waste-reduction practices, and actual engagement in conservation strategies. Data were collected from 255 households (response rate: 66%) in Buraydah City through an electronic questionnaire administered in shopping malls. Using Baron and Kenny mediation analysis and multiple linear regression, awareness of waste consequences influences conservation practices both directly (β = 0.132, p < 0.001) and indirectly through behavioral knowledge (accounting for 68.6% of the total effect), explaining 74.9% of the variance in household conservation behaviors (R² = 0.749). The analysis reveals that awareness of waste consequences influences conservation practices both directly and indirectly through behavioral knowledge, establishing a mediation pathway. Together, these knowledge dimensions significantly explain variations in household conservation behaviors. The findings highlight the critical interplay between awareness and practical behavioral knowledge in driving sustainable food consumption practices. These insights provide empirical guidance for policymakers and agencies seeking to develop targeted interventions that integrate consequence messaging with practical behavioral training to effectively reduce household food waste and promote food security in Saudi Arabia. Full article

The aim of this paper is to identify and psychographically characterize consumers operating in the phygital environment based on their emotional responses to tools used in commerce and services. The theoretical section involves a bibliometric analysis (Web of Science and Scopus papers from 2015 to 2024) using Bibliometrix and Biblioshiny in RStudio. The empirical study was conducted using the Internet survey technique in February 2025 on a nationwide random-quota sample of 2160 adult internet users. Based on cluster analysis, three types of consumers were identified: solution skeptics (48.1%), cautious explorers (20.1%), and tool enthusiasts (31.9%). The results indicate that emotions play a key role in the perception of phygital experiences. The article provides practical guidance for companies, including approaches for designing more inclusive and accessible shopping environments. A positive attitude toward these tools can foster more efficient use of services, reducing overconsumption and improving quality of life. In the context of sustainable development, these results point to the need for further research into the real impact of phygital solutions on consumer wellbeing—social, economic, and environmental. Full article