Search Results (2,073)

The flexible job shop scheduling problem (FJSP), which simultaneously involves machine assignment and operation sequencing under multiple constraints, is a typical NP-hard combinatorial optimization problem, and efficient scheduling is of great importance for improving production efficiency and manufacturing flexibility. To address this problem, the success-history beaver behavior optimizer (SHBBO) is introduced to solve FJSP with the objective of minimizing the makespan. First, considering the discrete characteristics of FJSP, an effective encoding and decoding scheme is designed to represent operation sequences and machine assignments. Then, the adaptive success-history mechanism of SHBBO is employed to dynamically adjust the search parameters during the optimization process, enabling a better balance between global exploration and local exploitation. Meanwhile, the behavioral update strategy of SHBBO is adapted to the scheduling environment so that candidate solutions can be effectively evolved in the discrete solution space. In addition, a population updating strategy and elite-guided search mechanism are incorporated to enhance solution quality and convergence performance. Finally, extensive experiments are conducted on benchmark FJSP instances to verify the effectiveness of the proposed method. Experimental results show that SHBBO achieves the best average results on 11 out of 12 CEC2022 benchmark functions, with particularly notable improvements over the original beaver behavior optimizer (BBO) on functions such as F6 (56.69%), F5 (12.20%), and F10 (9.18%). On the BRdata benchmark instances, SHBBO obtains the best or tied-best makespan on all 10 instances, with an average percentage relative deviation (PRD) of 0, and reduces the makespan by 7.69% on MK10 and 6.25% on MK06 compared with BBO. Full article

Indoor environmental quality (IEQ) is increasingly recognized as a critical factor in shaping employee well-being, satisfaction, and work performance, particularly in hybrid workplace settings. This mixed-methods study examined how integrated IEQ conditions influence employee experience in a public-sector hybrid workplace through a case study of the WorkHub, a technology-enabled flexible workspace embedded within a large municipal utility. Quantitative data were collected from 93 valid survey responses using the Workplace Environment Satisfaction and Performance Questionnaire (WESP-Q™), and qualitative insights were obtained from a 90-min participatory think tank session with 24 employees. Results showed that WorkHub users reported significantly higher satisfaction across 15 of 18 environmental and spatial dimensions, including layout, thermal comfort, air quality, lighting, furnishings, cleanliness, and overall building experience. They also reported significantly stronger outcomes in collaboration access, work transition, focus support, work efficiency, workspace productivity, pride in work, and job satisfaction. Qualitative findings reinforced these results, highlighting technology integration, daylight, and spatial flexibility as key strengths, while identifying acoustics, thermal discomfort, and limited privacy as persistent challenges. These findings support a systems-oriented, human-centered approach to workplace design, demonstrating that integrated IEQ can enhance employee experience, collaboration, and organizational performance in hybrid public-sector environments. Full article

Rapid urbanization has intensified jobs–housing separation and increased commuting distances in megacities, posing challenges for sustainable urban development. Existing studies often examine commuting behavior at a single spatial scale or focus on either residential or employment locations. Using mobile phone signaling data, this study derives network-based commuting distances within the suburban ring of Shanghai and integrates multiple built environment indicators. A multiscale framework is developed using six spatial units, ranging from 2 to 4 km grids to street-level zones, to assess spatial scale effects and support the selection of an appropriate analytical unit. The 3.5 km grid was selected for subsequent analysis as a balance between spatial detail and statistical stability. Within this framework, Multiscale Geographically Weighted Regression (MGWR) examines the spatial heterogeneity and scale effects of built environment factors from both residential and employment perspectives. The results show: (1) The choice of spatial unit significantly affects model performance, with the 3.5 km grid providing a suitable balance between spatial detail and statistical stability. (2) Built environment indicators exhibit clear multiscale effects, with different variables operating at global and local spatial scales. (3) Residential and employment locations show significant asymmetric effects, as enterprise density is associated with shorter commuting distances at residential locations but longer distances at employment centers. These findings indicate the joint role of multiscale spatial structure and dual-end built environments, supporting spatially differentiated planning and transport policies. Full article

This study examines the relationships between green workplace mindfulness, employee productivity, green work engagement, and perceptions of a green workplace climate within healthcare organizations. Green workplace mindfulness (GWM) refers to employees’ awareness of how their daily work activities influence environmental sustainability and resource use. Drawing on the Job Demands–Resources (JD-R) and Conservation of Resources (COR) theoretical perspectives, the study proposes that sustainability-oriented mindfulness may function as a personal resource associated with employee engagement and work outcomes. Data were collected through a cross-sectional survey of 473 employees working in public and private hospitals in Libya. The study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships among the study variables. The findings indicate that green workplace mindfulness is positively associated with employee productivity, both directly and indirectly through green work engagement (GWE). In addition, perceptions of a supportive green work climate (GWC) perception strengthen the relationships between mindfulness, engagement, and productivity. Specifically, the indirect association between mindfulness and productivity through engagement becomes stronger when employees perceive stronger environmental support within their organizations. These findings contribute to sustainability and organizational behavior research by demonstrating how individual awareness of environmental responsibility and supportive workplace climates jointly relate to employee engagement and productivity in healthcare settings. From a practical perspective, the results suggest that healthcare organizations can encourage sustainable performance by promoting environmental awareness among employees and by developing workplace climates that support environmentally responsible practices. Such initiatives may help healthcare institutions improve operational effectiveness while contributing to broader sustainability goals. Full article

Job satisfaction (JS) is a key indicator in health-related professions. This variable can be crucial to the performance of healthcare workers as well as to patients’ perception of the care provided. However, JS can be influenced by personal and organizational variables and the variables that interface between these two domains, such as work–family conflict (WFC), organizational support, and work–family support programs. In this sense, the main objective of this study is to analyze the relationship between family-friendly practices (FFPs) made available by organizations and used by workers, WFC, and JS. To this end, the present study uses mixed data (quantitative and qualitative) obtained from healthcare workers in Portugal. A total of 1495 workers participated in the study. The results indicate that the number of FFPs made available and used by healthcare workers is very small. There is a negative relationship between FFP availability and WFC as well as between WFC and JS. Conversely, FFP availability is positively associated with JS. Finally, FFP availability partially mediates the relationship between WFC and JS. Qualitative data support the importance of practices that help reconcile work and family life, which may have an impact on occupational indicators such as JS. Full article

Across the globe, companies are facing significant pressure to reduce waste, improve resource efficiency, and report their sustainability efforts transparently. ESG frameworks have become essential tools for sustainability transformation. However, traditional business models, based on a linear “take–make–dispose” approach, continue to dominate industries, limiting the impact of ESG efforts. The circular economy offers a compelling alternative: it encourages designing products for reuse, recycling, and regeneration, thus aligning closely with ESG principles. When businesses transition to circular models, they reduce their environmental footprint, create new green jobs and social inclusion opportunities, and strengthen accountability across business value chains. This study explores how selected firms in the mining, energy, consumer cyclical, technology, and healthcare sectors are aligning circular principles with ESG practices. Using a longitudinal, multi-sector comparative analysis of ESG indicators spanning 2014–2024, the research examines sector-level ESG evolution, firm-level ESG leadership, and the alignment of ESG performance with circular business model pathways. Rather than directly measuring circular transformation, ESG indicators are interpreted as signals of emerging circular business model pathways. This study identifies ESG-based ways and enabling conditions through which circularity may be increasingly embedded across different sectors. Full article

This study examines the increasing concern regarding teacher job satisfaction, which has a direct impact on retention, instructional quality, and student outcomes. Traditionally, teacher satisfaction has been evaluated through questionnaires, which present limitations in terms of data efficiency and analyses. In this study, machine learning techniques were applied to data from the PISA 2022 teacher questionnaire in Morocco (N = 2998 lower-secondary teachers). Two multiclass classification targets were defined: overall job satisfaction (SATJOB_class) and satisfaction with the teaching profession (SATTEACH_class), each categorised into three balanced classes: low (<−0.5), medium (−0.5 to 0.5), and high (>0.5) classes. The methodology comprised four key stages. Initially, comprehensive pre-processing was conducted to address missing values, retaining features with fewer than 300 missing entries and applying mode imputation. Subsequently, nine classifiers, including logistic regression, K-nearest neighbours, multinomial naïve Bayes, support vector machine, decision tree, random forest, XGBoost, AdaBoost, and a feed-forward Artificial Neural Network, were evaluated using identical train/test splits and hyperparameter tuning. Third, the model performance was assessed using accuracy, precision, recall, and F1-score. Finally, the feature importance was derived from tree-based and permutation methods. The results indicated that XGBoost outperformed the other models for SATJOB_class with an accuracy (0.61), precision (0.62), recall (0.61), and F1-score (0.61), followed by Random Forest (accuracy = 0.59), Logistic Regression (accuracy = 0.59), and AdaBoost (accuracy = 0.59). For SATTEACH_class, Random Forest led with accuracy (0.59), followed closely by XGBoost (0.58), ANN (0.57), and AdaBoost (0.56). Key predictors of teacher job satisfaction included workload-related variables and school-environment factors, which consistently emerged as the most important features across the best-performing models. The methodology and open-source pipeline provide a reproducible framework for evidence-based interventions to improve teacher retention and instructional quality, offering valuable insights for policymakers and educational administrators. Full article

Background/Objectives: Radiologic technologists are frequently exposed to occupational stressors that heighten the risk of burnout, compromising well-being and job performance. Workplace exercise programs have been identified as promising strategies to enhance physical and mental health across occupational groups; however, robust experimental evidence among radiologic technologists remains limited. This study aimed to evaluate the effects of a structured workplace exercise program on perceived stress, burnout, and quality of life among radiologic technologists. Methods: A small-scale randomized controlled experimental study was conducted with 19 radiologic technologists from the Local Health Unit of Castelo Branco, Portugal. Participants were randomly assigned to an experimental group (n = 10, age mean = 43.8 ± 9.92 years old) or a control group (n = 9, age mean = 48.2 ± 7.86 years old). The intervention consisted of a six-week workplace exercise program conducted during work hours, comprising sessions three times per week, twice per day. Each session lasted approximately 15–20 min and included balance, stretching, and light resistance exercises. Outcomes were assessed pre- and post-intervention using the Perceived Stress Scale, the Copenhagen Burnout Inventory, and the WHOQOL-BREF. Results: The experimental group showed significant reductions in perceived stress (p = 0.013, d = −0.697 (−1.6–0.206) [moderate]) and in personal (p = 0.004, d = −0.834 (−1.748–0.08) [moderate]) and work-related burnout (p = 0.026, d = −0.756 (−1.664–0.151) [moderate]), as well as improvements in the physical (p = 0.046, d = 0.592 (−0.303–1.488) [small]) and environmental (p = 0.032, d = 0.991 (0.062–1.92) [moderate]) domains of quality of life. No significant changes occurred in the control group. Conclusions: These preliminary findings suggest that a brief, low-cost workplace exercise program may reduce stress and burnout and improve quality of life among radiologic technologists. These findings support the integration of structured physical activity into healthcare work settings as a feasible, preventive, and health-promoting strategy. Full article

Background: Healthcare professionals are exposed to high emotional demands, including repeated contact with suffering, death, moral distress, and organizational pressure. These factors are associated with psychological distress, burnout, and secondary traumatic stress. Expressive Writing (EW) has been proposed as a psychological intervention, but evidence of its effectiveness among healthcare professionals remains heterogeneous. Objectives: To examine the effects of EW on psychological health, psychophysical well-being, and professional satisfaction among healthcare professionals. Methods: A systematic review was conducted in accordance with the Cochrane Handbook for Systematic Reviews of Interventions and the PRISMA 2020 guidelines. Searches were performed in PubMed, CINAHL, CENRAL, CENTRAL Scopus, Embase, and PsycINFO from database inception to January 2025. Quantitative studies involving healthcare professionals and evaluating structured expressive writing interventions were considered for inclusion, including randomized and non-randomized, controlled and uncontrolled designs. Studies reporting psychological, psychophysical, or work-related outcomes were eligible. Only full-text articles published in English or Italian were considered. The review protocol was registered and archived in the Open Science Framework. Methodological quality was assessed using CASP checklists, the RoB 2 tool, and the Newcastle–Ottawa Scale. Results: Seven studies published between 2017 and 2023 were included. EW interventions were associated with reductions in psychological distress, particularly perceived stress, depressive symptoms, and post-traumatic stress symptoms. Findings regarding burnout and compassion fatigue were mixed. Organizational and job-related outcomes, such as job satisfaction and organizational commitment, showed limited and heterogeneous improvements. No consistent effects were observed for resilience or social support. Overall, the methodological quality of the included studies was generally good. Conclusions: EW appears to be a promising, low-cost intervention for reducing psychological distress among healthcare professionals. However, heterogeneity in study designs, intervention protocols, and outcome measures limits the strength of the evidence. Further high-quality, controlled studies using standardized EW protocols are needed. Full article

Smoking remains one of the leading preventable causes of chronic disease and premature workplace mortality worldwide. This study examined the association between smoking and nutritional status and hypertension and hematological disorders among hotel workers and occupational health nurses’ role in Indonesia. This cross-sectional study examined associations between smoking, nutritional status, and selected health outcomes among 366 hotel workers in Indonesia using routine medical check-up data. Logistic regression analyses were performed to assess the associations of smoking status and body mass index (BMI) categories with hypertension and hematological abnormalities (leukocytosis and anemia), after adjusting for age, gender, and job level. Older workers (40–69 years) and those categorized as overweight or obese had higher odds of hypertension than younger workers and those with normal BMI (ORs 2.63 and 1.37, respectively). Smoking was associated with a higher risk of leukocytosis (OR 0.395), reflecting increased risk among smokers due to variable coding. Older age and overweight status were strong predictors of hypertension, whereas smoking was associated with increased leukocytosis among hotel workers. These findings highlight the need for targeted OH interventions. Occupational health nurses should collaborate with management to strengthen WHP programs that encourage healthier lifestyles among employees. Full article

Transformational leadership and internal communication are widely studied variables in organizational management; however, their joint effect as simultaneous predictors of multidimensional job performance remains underexplored, particularly in Latin American higher education contexts. This study examines whether transformational leadership and internal communication jointly predict task performance, contextual performance, and counterproductive job performance among Peruvian university workers. A quantitative, non-experimental, cross-sectional, and predictive correlational design was applied to a sample of 385 workers from a private Peruvian university, analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). Results confirm that both variables significantly predict job performance across its dimensions, with internal communication showing stronger predictive effects on task and contextual performance than transformational leadership. These findings contribute to organizational and management theory by proposing and validating a joint predictive model that addresses existing conceptual and empirical gaps in the literature, while providing evidence-based recommendations for leadership development and communication management in university institutions operating in emerging economy contexts. Full article

Teacher well-being and performance represent critical challenges for educational systems worldwide. While organizational compassion has been identified as a protective resource, it remains unclear for whom compassion is most beneficial. Drawing on Job Demands–Resources (JD-R) theory and Conservation of Resources (COR) theory, we examined whether teachers’ self-efficacy moderates the relationship between workplace compassion and performance perceptions, testing differential patterns for individual versus organizational performance evaluations. Italian public-school teachers (N = 218; 82% female; M teaching experience = 11.6 years) completed an online survey measuring compassion at work, self-efficacy, and perceptions of individual and organizational performance. We employed a two-stage approach, first validating the measurement model through Confirmatory Factor Analysis (CFA), then testing moderation hypotheses using path analysis with mean-centered variables. Bootstrap confidence intervals (5000 iterations) verified the reliability of interaction effects. Self-efficacy significantly moderated the effect of compassion on individual performance perceptions (β = −0.006, p = 0.006; bootstrap 95% CI: [−0.010, −0.002]), revealing a compensatory pattern. Teachers with lower self-efficacy benefited substantially from workplace compassion (simple slope β = 0.31, p < 0.001), whereas teachers with high self-efficacy showed no significant benefit (β = 0.06, ns). The hypothesized synergistic effect on organizational performance perceptions was not supported (β = 0.006, p = 0.027; bootstrap CI included zero). Organizational compassion functions as a compensatory resource, most powerfully supporting teachers who lack personal resources. This challenges assumptions that organizational interventions uniformly benefit all employees and suggests that compassion-based interventions should be strategically targeted toward teachers experiencing lower self-efficacy. The study advances theoretical understanding of resource substitution mechanisms and provides actionable guidance for optimizing limited organizational resources in educational settings. Full article

Background: Intensive care units (ICUs) are high-acuity environments that require respiratory therapists (RTs) to maintain vigilance, manage emotions, and make rapid clinical decisions. In such settings, performance stability is critical for patient safety. Although emotional intelligence (EI) and work–life balance (WLB) have been linked to professional outcomes in health care, their independent and direction-specific associations with job performance among ICU respiratory therapists remain underexamined. Methods: A national cross-sectional survey was conducted among respiratory therapists working in ICUs across Saudi Arabia (June 2025–January 2026). EI was measured using the Wong and Law Emotional Intelligence Scale. WLB was assessed using the work interference with personal life (WIPL), personal life interference with work (PLIW), and work–personal life enhancement (WPLE) scales. Job performance was evaluated using the Individual Work Performance Questionnaire. Correlation and multivariable linear regression analyses were performed to estimate independent associations. Results: A total of 392 RTs were included in the final analysis. Higher EI was independently associated with greater task performance (B = 0.21, p < 0.01) and contextual performance (B = 0.30, p < 0.001), and with lower counterproductive work behaviours (B = −0.24, p < 0.001). Among WLB dimensions, PLIW showed the strongest adverse association, predicting lower task performance (B = −0.20, p < 0.05) and higher counterproductive behaviours (B = 0.39, p < 0.001), but was not significantly associated with contextual performance in the fully adjusted model. WPLE demonstrated modest positive associations with performance, whereas WIPL was not significant in adjusted models. Conclusions: Job performance among ICU respiratory therapists is shaped by both emotional regulatory capacity and cross-domain strain. Personal life interference with work emerged as the most influential adverse predictor, whereas EI was associated with constructive performance patterns. Findings should be interpreted in light of the cross-sectional design and self-reported data. Sustaining performance in high-acuity settings requires attention to emotional competencies and structural sources of role conflict alongside clinical expertise. These findings inform workforce strategies to support performance and sustainability in critical care settings. Full article

This paper investigates a tri-objective green flexible job shop rescheduling problem under urgent order insertion and multi-speed machining conditions, where makespan, total energy consumption, and total tool wear are jointly optimized. First, an event-driven freezing mechanism is introduced, in which completed and ongoing operations are fixed, while only the rescheduling window composed of waiting operations and urgent-order operations is re-optimized. On this basis, two rescheduling strategies, namely complete rescheduling and deferred rescheduling, are designed and compared. Second, to improve the solution capability in complex dynamic environments, an improved multi-objective evolutionary algorithm based on decomposition (IMOEA/D) with a three-layer encoding scheme is proposed. The algorithm incorporates hybrid initialization, tabu-guided crossover, simulated annealing mutation, and critical-path-based variable neighborhood search. Experimental results show that the proposed method performs well in energy consumption optimization and tool wear control, while effectively improving the diversity and distribution quality of the Pareto solution set. Further analysis indicates that deferred rescheduling generally outperforms complete rescheduling, while the original-orders-first and urgents-first strategies exhibit different strengths in convergence, solution quality, and objective optimization. The proposed study provides an effective modeling and optimization framework for multi-objective green rescheduling problems and offers theoretical support for production scheduling decisions that need to balance production efficiency, energy saving, and tool-related cost control in practical manufacturing systems. Full article

Gender bias embedded in legal and professional texts perpetuates systemic inequality, yet research on bias identification and mitigation remains largely confined to English. Morphologically rich languages such as Greek, where grammatical gender pervades nouns, adjectives, pronouns, and participles, present unique challenges that existing approaches fail to address. This paper elaborates on a systematic methodology primarily focusing on identifying and mitigating gender bias in Greek-language job advertisements and legal documents. To accomplish that task, we define a taxonomy of nine gender bias rules tailored to the linguistic properties of Greek and construct domain-specific annotated datasets comprising 90 expert-curated few-shot examples across both textual domains. Using these resources, we employ XML-structured prompt engineering with in-context learning (ICL)and systematically compare three classes of models: (i) commercial large language models (LLMs), namely Claude Sonnet 4.5 and GPT-5.2, (ii) two open-weight small language models (SLMs), Mistral Small (24B) and Ministral (14B), and (iii) Llama Krikri (8B), a Greek-native language model built on Llama 3.1 and fine-tuned on high-quality Greek corpora. For each input text, the system identifies biased expressions, maps them to specific bias rules, provides explanations, and generates a fully corrected inclusive version. Our experiments reveal substantial performance disparities across model scales and linguistic specialization, with LLMs demonstrating superior contextual reasoning and SLMs exhibiting systematic over-correction and grammatical errors in Greek morphology. We further introduce a critical meta-rule addressing gender agreement with named entities to prevent spurious corrections in legal texts referencing identified individuals. The findings highlight the importance of model scale, language-specific adaptation, and carefully designed prompting strategies for bias mitigation in underrepresented languages. Full article