Search Results (297)

With the large-scale integration of renewable energy and the deepening of electricity market reform, uncertainty in power system operation has increased significantly. This creates new challenges for multiple generation companies when they work together to develop generation schedules that balance economic efficiency and low-carbon goals. Most existing studies assume fixed loads and ignore the active regulation capability of the demand side under price signals and incentive signals. To address this gap, this paper proposes a low-carbon generation schedule optimization method for multiple generation companies. The method considers heterogeneous flexible loads. First, the paper decomposes flexible load adjustability into two components: price elasticity-based load shifting and incentive-based adjustable capacity. Using the price elasticity matrix method, the market clearing price serves as a known input. The load shifting amount under price elasticity regulation is pre-calculated for each park and treated as an exogenous parameter in the generation schedule model. This allows generation companies to directly use demand-side flexibility information during the planning stage. Second, the paper uses the proportion of residential and industrial loads as a core parameter. It characterizes the heterogeneity of four parks along two dimensions: elasticity coefficients and upper limits of adjustable capacity. Parks with a higher proportion of industrial loads have stronger flexible regulation capability. This result is consistent with real physical characteristics. It also provides a quantitative basis for generation companies to utilize flexible resources differently across parks and optimize their output arrangements. Finally, the paper uses the upward and downward adjustable capacity of each park as decision variables. It builds a multi-generator low-carbon generation schedule optimization model with heterogeneous flexible loads. Generator output constraints, power balance constraints, flexible load adjustable capacity constraints, and carbon quota constraints are all integrated into a single-level mixed-integer linear programming framework. This framework can be solved efficiently using commercial solvers. It helps generation companies develop optimal generation schedules that balance economic efficiency and low-carbon targets. Case study results show that combining price elasticity regulation with incentive-based adjustable capacity can effectively improve both the economic performance and low-carbon performance of generation schedules. Full article

The rapid expansion of e-commerce has reshaped global consumption systems by transforming production processes, logistics infrastructures, and consumer behaviour. While this transformation has generated significant economic opportunities, it has simultaneously intensified environmental pressures, particularly through last-mile delivery emissions, excessive packaging waste, and high return rates. At the same time, the growing diffusion of corporate sustainability reporting has raised increasing concerns about greenwashing, defined as the misrepresentation of environmental performance through selective disclosure or symbolic communication. This study aims to provide a comprehensive assessment of sustainability practices in e-commerce, focusing on the relationship between environmental performance, transparency, and economic outcomes. Particular attention is devoted to the role of blockchain technology as a potential mechanism for enhancing verifiable transparency in complex supply chains. The research adopts a multiple case study design grounded in the methodological frameworks and integrates qualitative analysis with a semi-quantitative evaluation model. Seven companies operating in different segments of the e-commerce ecosystem are analyzed through an extensive review of secondary data sources, including ESG reports, financial disclosures, NGO assessments, and industry benchmarks. The findings reveal a substantial gap between declared sustainability commitments and actual implementation, with significant heterogeneity across firms. Companies that embed sustainability into their strategic core demonstrate stronger alignment between environmental and economic performance, whereas firms relying primarily on communication-driven approaches exhibit higher implementation gaps. The study contributes to the literature by introducing an analytical framework centered on the concept of the implementation gap and by demonstrating the central role of transparency in determining sustainability effectiveness. It also highlights the potential, yet still largely unrealized, role of blockchain technology in addressing information asymmetry and reducing greenwashing in e-commerce. Full article

Complex products continue to face low iterative-design efficiency and poor cross-generation data compatibility, while existing product-gene research is still constrained by the predominance of qualitative approaches, ambiguous representations of hierarchical associations, and insufficient standardization. Based on the principles of decomposition and reconstruction and the systems thinking of genetic engineering, this study develops a generic three-level framework for product genes at the platform, assembly, and component levels. Hierarchical mapping functions and parameter-constraint equations are introduced to enable quantitative representation, and a quantitative product-gene information system is established, including a core-parameter quantification model and inter-/intra-level association-strength models. By integrating multiple international standards, the study further constructs a tripartite standardized description system covering metadata, semantics, and format, and proposes a mathematical mapping method from product information to standardized formats. A case study of Company A’s Platform B and Concept Vehicle C shows that the association-strength model achieves the required adaptation threshold, thereby validating the proposed framework. This study provides quantitative theoretical support for the platform-based and intelligent development of complex products and offers an implementable technical solution for product-gene reuse and data sharing, particularly in the new energy vehicle industry. Full article

The challenge of inventory optimization is extremely important for all manufacturing companies, as inventory costs significantly impact operational efficiency. The Economic Order Quantity (EOQ) model was developed to address this issue, and it is widely used to formulate it, as it generally considers only a few parameters and a single objective. This research develops a simulation-based framework that integrates multiple EOQ-based inventory policies and performs multi-objective optimization using the NSGA-II algorithm. The framework optimizes total cost, fill rate, and average inventory level and finally generates a Pareto front as a result. To reduce computational costs, we use a machine learning-based random forest model, which replaces a significant amount of the simulations with predictions. This reduces the simulation cost to approximately one-sixth of the original, while the quality of the simulation changes only minimally, as the hypervolume value decreases by only 4%. The proposed framework can be used as an effective decision-support tool for inventory optimization under stochastic demand conditions. Full article

Sentiment analysis has become increasingly important across various domains, particularly in business intelligence, where it is crucial for improving the performance of companies by identifying the sentiments and emotions expressed in customer feedback on products and services. Despite its growing relevance, sentiment analysis still faces several challenges, including class imbalance in datasets, limitations in feature extraction techniques, and the selection of appropriate classification models. Effectively addressing these challenges requires the integration of robust representation methods, reliable data balancing strategies, and efficient classification frameworks. In this study, we propose a novel sentiment analysis approach that combines SBERT for contextual feature extraction, WGAN-based synthetic data generation for addressing class imbalance, and a soft voting ensemble classifier for improved prediction. The proposed approach is evaluated on five datasets, including two English datasets and three Arabic datasets, in order to assess its performance in a multilingual setting. We compare the effectiveness of the proposed model with several baseline machine learning classifiers, as well as with commonly used data balancing techniques such as the synthetic minority over-sampling technique (SMOTE) and adaptive synthetic (ADASYN). The evaluation is conducted using multiple performance metrics, including accuracy, precision, recall, F1-score, MCC, ROC–AUC and training and inference time, along with different validation strategies including fixed train–test splits and k-fold cross-validation. The experimental results demonstrate the effectiveness and stability of the proposed approach. In particular, they highlight the importance of capturing sentence-level contextual representations and generating realistic synthetic samples to address class imbalance. Full article

As enterprise social media (ESM) visibility increasingly exposes employees’ work-related behaviors and competencies to organizational audiences, its potential negative psychological consequences remain underexplored. Grounded in social comparison theory and adopting the three-stage “selection, appraisal, and response” research framework, this study investigates whether and how ESM visibility fosters workplace impostor thoughts and, in turn, influences employees’ knowledge-sharing behavior and workplace well-being. Moreover, this research further examines the boundary role of social comparison orientation in shaping these effects. A two-wave, multi-source survey design was employed to test the proposed hypotheses. Data were collected from employees and their immediate supervisors in four companies across the finance, IT, management consulting, and education industries in China. To reduce common method variance, data collection was separated by a two-week interval. The final sample consisted of 447 matched employee–supervisor dyads. Hypotheses were tested using correlation and multiple regression analyses conducted in SPSS 23.0 and Mplus 8.3. Mediation and moderated mediation effects were examined using the PROCESS macro (Version 3.5) with 5000 bootstrap resamples. ESM visibility exhibited a significant positive association with workplace impostor thoughts and exerted a negative indirect effect on employees’ knowledge sharing and workplace well-being through workplace impostor thoughts. Moreover, social comparison orientation strengthens the positive effect of ESM visibility on workplace impostor thoughts, as well as the indirect effects of ESM visibility on knowledge sharing and workplace well-being via workplace impostor thoughts. The findings elucidate the relationship between enterprise social media (ESM) visibility and workplace impostor thoughts, highlighting the mediating role of workplace impostor thoughts and the moderating role of social comparison orientation. These findings suggest that ESM visibility generates unintended negative outcomes and complements research on the contextual antecedents of workplace impostor thoughts. Moreover, this study extends social comparison theory to explain employee responses to digital workplace visibility. Full article

Influenced by factors such as residents’ living habits, commuting patterns, and commercial activity cycles, the generation of domestic waste exhibits a distinct double-peak distribution. To meet the high demand during peak periods, collection companies typically deploy excess transportation capacity, which leads to severe resource idleness during off-peak periods, imposing significant economic and environmental burdens. To address this issue, this study develops a dynamic smart waste collection routing model aimed at minimizing the coordinated collection cost between self-owned and outsourced multi-compartment vehicles, and designs a two-phase algorithm to solve it. In the pre-optimization phase, an improved Harris Hawks Optimization algorithm integrated with multiple heuristic algorithms is employed to generate initial collection routes. In the re-optimization phase, a hybrid strategy combining periodic and continuous re-optimization is used to dynamically update collection routes. Finally, the effectiveness of the proposed model and algorithm is validated through case studies. Furthermore, a systematic sensitivity analysis is conducted to investigate the impact of key parameters, yielding practical insights for waste collection management. Full article

The accelerating global energy transition has substantially increased demand for critical minerals such as copper, nickel, and lithium, positioning mining firms as key actors in the decarbonization of energy systems. However, the expansion of mineral extraction raises important sustainability challenges because mining activities remain highly energy- and carbon-intensive. This study investigates whether green innovation can simultaneously improve environmental performance and financial performance in critical mineral mining firms and examines the moderating role of institutional governance. Using a balanced panel of 35 publicly listed mining companies from Australia, Canada, Chile, Brazil, and Indonesia over the period 2015–2024, the analysis applies fixed-effects panel regressions complemented by dynamic specifications and multiple robustness tests, including alternative variable definitions and System Generalized Method of Moments (GMM) estimation. The results show that green innovation significantly reduces carbon intensity, indicating that environmental investments in renewable energy integration, electrification, and process efficiency contribute to improving emissions performance in mining operations. Green innovation also enhances firm financial performance, although the benefits emerge gradually over time, suggesting delayed financial gains followed by long-term efficiency improvements. Furthermore, governance quality strengthens the positive relationship between green innovation and firm performance, highlighting the importance of institutional environments in shaping the economic returns of sustainability strategies. By providing firm-level evidence across major mineral-producing economies, this study contributes to the literature on critical minerals, environmental finance, and the institutional dimensions of the just energy transition. Full article

Background: Improper disposal of unused and expired medicines represents an environmental and public health concern. In Romania, Law No. 269/2023 assigned the responsibility for collecting household pharmaceutical waste to public and private hospitals, while operational procedures were further detailed in the Ministry of Health (MoH) Instruction No. 6226/2024. Objectives: This study aimed to assess knowledge, attitudes, and practices (KAP) related to the disposal of unused and expired medicines among the general public and community pharmacy staff during the early phase of implementation of the hospital-based medicine take-back system in Romania. Methods: A cross-sectional survey using convenience sampling was conducted between 1 and 31 August 2023. Two structured questionnaires were administered: one targeting the general public/patients and another addressing community pharmacy staff. Data were analyzed descriptively using frequencies and percentages. Several items allowed multiple responses. Results: Among public respondents (n = 108; predominantly male, 90.7%; urban, 75.0%), household waste disposal was the most frequently reported method (58.3%), followed by pharmacy return (43.5%). Willingness to use a dedicated collection system was very high (96.3%). Among pharmacy staff (n = 71; predominantly female, 78.9%; urban, 74.6%), 40.8% reported no collection activity; where collection occurred, it was typically on demand. Disposal routes included transfer to specialized waste companies (56.3%) and regulated destruction (43.7%). Only 1.4% of pharmacies offered incentives, while 45.4% of the public indicated discounts could motivate returns. Conclusions: Findings indicate an implementation and communication gap during the transition to a hospital-based pharmaceutical waste collection system. Strengthening public communication on official collection points and providing clearer operational guidance may support safer disposal practices. Full article

Industrial electrical houses are engineered systems that transform and control electrical power to supply industrial loads. Preparing technical proposals for these rooms requires consistent engineering choices across multiple artifacts while drawing from heterogeneous client documents, historical projects, and supplier catalogs. This paper reports an industrial prototype that integrates generative AI, system modeling, and mathematical decision methods to support that workflow. We represent requested outputs as ordered sequences of functions and link those functions to candidate equipment blocks through functional and physical graphs that enable traceable retrieval and reuse. Using this representation, we compute a minimal internal-cost baseline by solving a mixed-integer assignment model with sizing constraints, and we rank technically feasible alternatives using fuzzy DEMATEL to derive criterion weights and TOPSIS to obtain an overall ordering under multiple criteria. The workflow is illustrated with an example and the prototype tool used in a company operating in Chile, Peru, Ecuador, and Bolivia, where document ingestion and equipment-list extraction are integrated with human validation. The results illustrate how structured representations, optimization, and multi-criteria ranking can support auditable configurations for engineering review and commercial selection. Full article

The effective utilization and effective valorization of various organic industrial wastes have become increasingly important issues. One significant area for enhancing the circular economy is the processing of waste generated from vegetable oils and animal fats. This article focuses on the processing and use of soapstocks, which result from the chemical reaction between fatty acids and alkali. These soapstocks represent the most significant portion (approximately 70–90 wt% by weight) of waste produced by the oil and fat industry. The raw material for this study was soapstock obtained from the neutralization of sunflower oil at the PJSC “Zaporizhzhya Oil and Fat Plant,” designed by the Belgian company “De Smet.” The soapstock yield was found to be 9.95 wt% based on 100 wt% oil. Through a series of treatments involving water, acid, and multiple washes, a low-sulfur fuel component was produced that nearly meets the standards for boiler fuels as outlined in DSTU 4058-2001 and PN-C-96024:2020, except for the heat of combustion. It fully complies with the requirements specified in ISO 8217:2024. The sulfur content of the final product was determined to be 0.12 wt%. Additionally, the fuels produced contained 75.33 wt% carbon, 11.64 wt% hydrogen, and 12.00 wt% oxygen. Due to the relatively low oxygen content, the resulting product exhibits approximately twice the heat of combustion of similar fuels derived from other waste streams in the oil and fat industry. Full article

Daily, monthly, and annual multi-product sales records are stored in databases, but due to the massive amounts of data, they are not used for decision-making when updating product catalogs. Meanwhile, the use of artificial intelligence in business is increasing across all sectors of the economy. Large-scale data handling can be achieved using artificial intelligence techniques. Specifically, ABC inventory classification currently employs artificial intelligence techniques, including neural networks, fuzzy systems, and genetic algorithms. However, a state-of-the-art review has not found any research using vision techniques to classify ABC inventories. To address this gap, this research presents a novel approach to the intelligent classification of a company’s multiple products, using ABC. Recent vision system research often uses the Otsu method or its variants to determine the optimum threshold for binary image segmentation. Unlike this approach, our research does not use a single threshold value; instead, it uses the full binary frequency histogram as an image representation. From this, eight invariant characteristics are extracted from translation, rotation, and scale. The results show that the classification is accurate, clear, and simple as a decision-making tool. The proposed method is general and can be used in any production sector and at any enterprise size. Full article

With the rapid development of smart cities, coordinating diverse distributed energy resources through storage-centric shared management has become a critical challenge. This paper proposes a bi-level energy management framework to support peer-to-peer energy trading among multiple virtual power plants (VPPs) under multidimensional uncertainties. The interaction is modeled as a Stackelberg–Nash equilibrium framework, in which OK, we will make the necessary revisions as per the requirements.a public energy storage operator and a natural gas company act as leaders to maximize social welfare and design differentiated trading strategies for VPPs. The VPPs act as followers and participate in cooperative energy trading based on a generalized Nash equilibrium scheme, sharing surplus energy and allocating cooperative benefits according to their contributions. To address uncertainty, Conditional Value at Risk (CVaR) is adopted to quantify the expected loss of the upper-level decision makers. The lower-level VPP problem is formulated as a three-stage stochastic robust optimization model considering renewable generation uncertainty. To solve the resulting nonlinear bi-level problem, a two-stage solution approach combining particle swarm optimization and KKT-based reformulation is developed to transform it into a tractable mixed-integer linear programming model. Numerical case studies verify the effectiveness of the proposed framework. Full article

Sustainable human resource management (HRM) has attracted growing attention as a new paradigm for enhancing organizational resilience. However, prior studies mainly examined the effects of individual practices, offering a limited explanation of how organizational resilience emerges as an integrated mechanism. To address this theoretical gap, we conceptualize sustainable HRM as an integral talent management process in which multiple practices operate interdependently and investigate the configurational mechanisms through which organizational resilience is generated in Japanese firms and discuss these from the perspective of socioformation. Based on six analytical dimensions derived from a tertiary literature review, we conducted a crisp-set qualitative comparative analysis (csQCA) using securities report data from 36 listed Japanese companies. The results revealed that organizational resilience is not achieved through a single best practice, but rather points to a new form of integrated human resource management aimed at sustainable value creation. From a socioformation perspective, employees are viewed not merely as productive inputs but as agents capable of continuous development through sustained investment in human potential. From this perspective, sustainable social development cannot be reduced to well-being or inclusion indicators alone but also encompasses ethical, collaborative, territorial, and interdisciplinary dimensions of transformation. The findings clarify the theoretical role of integral talent management in sustainable value creation and provide practical implications for human-centred management. Full article

The fashion industry is one of the most resource-intensive sectors, generating major environmental impacts such as greenhouse gas emissions, excessive water and land use, and pollution from waste and microplastics. Fast fashion intensifies these issues through overproduction and overconsumption. However, growing consumer awareness and regulatory pressure are pushing brands to adopt Circular Economy (CE) and sustainability strategies, including resale platforms, recycling programs, and sustainability frameworks. Despite these efforts, their real effectiveness remains uncertain. This study investigates which CE and sustainability strategies are most common among fast fashion companies and how they can mitigate key environmental impacts. Using a Fuzzy Cognitive Maps (FCM) model, the research quantitatively evaluates the effects of various circular and sustainable strategies across the supply chain. Ten key strategies were identified, revealing that isolated actions are often ineffective. Instead, an integrated, systemic approach combining multiple initiatives is essential to achieve meaningful sustainability improvements. Full article