ABSTRACT Determining the value of data products remains a challenge for enterprises and academia, despite the growing recognition of data as a strategic asset across their business operations. This complexity arises from varying definitions of data value, diverse stakeholder perspectives, and the interdisciplinarity of data valuation. To address these challenges, this study develops a multi-criteria evaluation model based on the MACBETH approach to help Galp Energia, a Portuguese energy company, assess the value of data products in its renewables division. The developed model incorporates seven criteria across an enterprise architecture’s business, data, and application/technology layer, providing a comprehensive assessment of five data products. The study contributes to the literature by proposing a tailorable data valuation approach that may be applicable to other industries. Beyond quantifying the data product value, the resulting model serves as a managerial tool to support data-driven decision-making. The model is constructed using a robust approach and overcomes the limitations of existing models, such as oversimplification and practical implementation challenges. Additionally, it fosters interdisciplinary collaboration between research and industry. Future research directions include using the model as a foundation for developing modular data valuation frameworks, exploring its application across sectors, and integrating cross-sector benchmarks.

ABSTRACT Managing perishable goods in supply chains requires strategies that account for limited shelf life, consumer sensitivity to freshness, and flexible payment structures. Traditional inventory models often neglect these complexities, resulting in limited practical relevance. This study develops a comprehensive two-warehouse inventory model that incorporates non-instantaneous deterioration, freshness-sensitive demand, hybrid payment schemes, and partial backlogging during shortages. Numerical experiments and sensitivity analyses demonstrate that the proposed model achieves lower total costs and greater adaptability than conventional models by explicitly capturing the interactions among deterioration dynamics, freshness, and financial policies. The results highlight the significant role of freshness and advertisement in driving demand, the effectiveness of hybrid payments in enhancing financial flexibility, and the benefits of controlled backlogging in reducing overall costs. These insights provide practical guidance for industries such as food and pharmaceuticals, where balancing perishability, demand variability, and financial constraints is critical for effective inventory management.

ABSTRACT Rail-truck intermodal networks serve as major freight infrastructure, transporting both regular and hazardous material. Accidents and infrastructure failures pose a significant threat to these networks due to associated losses to life, the environment, and the economy. Dealing with these risks is challenging due to the physical and economic scale of the problem. Developing efficient disaster management plans is thus operationally and economically quite challenging. We propose an optimization and machine learning methodology for this problem. In this methodology, impact-based categorization and classification of unknown service legs or intermodal terminals are done via appropriate clustering and classification models, while for the optimization of the shipment plans, a bi-objective model is developed that employs network criticality measures as determined in the machine learning phase. The methodology was applied to a rail-truck intermodal network in the United States. The results indicate that post-disruption consideration should be incorporated into the transportation planning problem; machine learning algorithms can efficiently categorize network elements with high accuracy; and efficient pro-active post-disruption management can avoid a significant increase in cost and associated risks.

ABSTRACT The rapid development of the shared manufacturing model makes it a central challenge to ensure efficient and stable matching of manufacturing resources in random, dynamic environments. This paper employs the Markov Decision Process (MDP) to characterize the complex scenario involving random arrival and departure of suppliers and demanders in shared manufacturing platforms. It applies cumulative prospect theory to calculate the satisfaction of both parties, which forms the basis for single-stage matching decisions. Then, a temporally correlated one-to-many stable matching model is established by integrating stable matching theory to maximize the long-term average matching utility. This paper proposes an Improved Q-Learning algorithm (IQL) that enhances adaptability to the matching environment while incorporating an improved Gale-Shapley (GS) algorithm within its framework to ensure matching stability. Numerical results show that, compared to the Traditional Q-Learning algorithm (TQL), the proposed IQL significantly enhances long-term average utility while reducing global optimal solution deviation, customer churn rate, and matching frequency. Furthermore, compared to the Fixed Horizon Matching (FHM) and Event-Triggered Matching (ETM) methods, IQL adaptively adjusts matching frequency across diverse scenarios and consistently achieves higher average matching utility. The findings provide insights for the shared manufacturing resource matching problem.

ABSTRACT The proliferation of misinformation on social media has heightened the demand for high-quality science popularization. However, systematically evaluating the effectiveness of science communication accounts remains challenging due to multidimensional uncertainties and the semantic complexity of content. To bridge this gap, this research constructs the Probabilistic Linguistic Term Sets (PLTS)-Large Language Model (LLM)-Entropy Method (EM)-VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) framework to conduct an empirical evaluation of 35,037 original blog posts from 32 science popularization accounts on Weibo. The approach integrates PLTS to model linguistic and behavioral uncertainty, LLM for semantic feature extraction and dynamic benchmarking, EM for objective weight determination, and VIKOR for robust compromise ranking. This synergy enables a more nuanced and interpretable evaluation compared to conventional methods. The results show that comprehensive interaction volume is the most influential criterion and reveal the critical threshold role of credibility and topic consistency, and also demonstrate the framework’s fine capacity to model complex account performance dynamics. Moreover, perturbation experiments confirm that balanced accounts exhibit high ranking stability and effectively detects and explains the volatility of accounts with isolated weaknesses. Multi-method comparisons show robustness, while illustrates that the compromise logic that emphasizes both interaction and credibility often achieves better rankings. Those with balanced indicators always lead the way, while with prominent weaknesses experience sharp fluctuations. These insights provide a validated methodological foundation and actionable strategies for enhancing science communication governance.

ABSTRACT Sustainability is among the greatest concerns of supply chain (SC) professionals, academia and policy planners. Recently, the notion of an Industry 4.0 and Closed Loop Supply Chain (CLSC) has gained attention to achieve sustainability. The integration of Internet of Things (IoT), with CLSC presents substantial potential for advancing sustainability. However, the integration of IoT into CLSC faces several barriers that hinder the effective adoption and implementation of IoT technologies. Hence, this study identifies and analyses the IoT adoption barriers in CLSC. A literature survey, along with input from domain experts, has revealed ten key barriers of IoT adoption within CLSC. Further, an integration of Interpretive Structural Modelling (ISM), DEMATEL, and fuzzy MICMAC is applied to explore the structural relationship among these barriers. The ISM model is structured into four hierarchical levels. Findings show that ‘risk-aversive culture’, ‘low awareness about IoT benefits’ and ‘poor IT infrastructure’ are significant barriers. The strength of the relationships among the barriers is further quantified using the DEMATEL method, through which the barriers are classified into cause-and-effect groups. Fuzzy MICMAC analysis is also utilised to cluster the barriers into four categories. This study could be useful for developing strategies/solutions to overcome the identified barriers to IoT adoption.

ABSTRACT This study introduces the Type-NS Possibility Neutrosophic Hypersoft Set (Type-NS PNHSS), a novel framework that integrates possibility theory with neutrosophic hypersoft sets through neutrosophic possibility numbers to manage uncertain, indeterminate, and often inconsistent information across multiple attributes simultaneously in multi-criteria decision-making (MCDM). We establish the foundational theory of the Type-NS PNHSS, including its fundamental operations, measures, and aggregation operators. Building upon this theoretical framework, we propose a multi-criteria decision-making model tailored to address decision-making problems within the Type-NS PNHSS environment. Finally, a real-world case study, the selection of tobacco control strategies, is conducted to demonstrate the efficacy and advantages of the proposed theory and approach. The proposed model evaluates tobacco control strategies based on 08 policies, 05 attribute groups, and 20 sub-attributes. The results indicate that the ‘mass media communications’ strategy is the best choice, and this finding is also further supported by comparative analyses with other existing methods, confirming the model’s utility as a robust tool for advanced decision analysis.

ABSTRACT This study evaluates the knowledge structure of disruptive innovation and its associated business model through a science mapping analysis employing two bibliometric approaches. Disruptive innovation and technologies have abruptly changed the market orientation due to the abrupt digital transformation and business innovation. It alters how businesses operate by changing the relationship between consumers and industries. Firms must develop distinct business models tailored to disruptive innovation in order to meet market needs. Applying bibliographic coupling and co-word analysis, this study explores and reviews the knowledge structure of the disruptive innovation business model, uncovering current, emerging, and future trends. The current stream suggests three themes: 1) Past experience of disruptive innovation, 2) digital transformation in disruptive innovation, and 3) Disruptive innovation and business model innovation. Meanwhile, future trends are associated with business orientation, value creation, and sustainable business models. The implications of this study are primarily associated with managerial and practical interventions in designing business models related to disruptive innovation. This study is relevant to business owners and startup entrepreneurs who are constantly keeping up with the pace of digital transformation in maintaining and managing their businesses.

ABSTRACT This paper presents a new variant of the two-echelon vehicle routing problem for a distribution network that incorporates time windows, occasional trucks, occasional drivers, and heterogeneous regular vehicles. For the first time, this study proposes applying the crowdsourcing model to the first echelon, utilizing crowdsourced vehicles for end-of-pipe delivery as well as for the first-echelon routes, thereby further leveraging the advantages of the crowdsourcing model. Given the significant differences in service scope, vehicle attributes, distribution rules, costs, and regulations between the crowdsourcing models at different echelons, the vehicle routes and crowdsourcing mode for the first echelon are newly designed. The objective is to balance transportation costs with customer satisfaction to achieve an optimal solution. A two-echelon vehicle routing optimization model under a crowdsourcing mode is established to address this specific problem. This model is solved using an improved multi-objective sparrow search algorithm (IMOSSA). Comparative results demonstrate that IMOSSA effectively solves the problem and yields more accurate solutions than existing algorithms. Finally, a sensitivity analysis compares the proposed model with three other distribution modes regarding the use of crowdsourced vehicles. Experimental results confirm that the proposed model outperforms the alternatives, achieving reduced transportation costs and improved customer satisfaction.

ABSTRACT This research introduces a novel modified cheetah optimizer (MCO) designed to address the complexities encountered in optimization problems. By building on the foundation of the original cheetah optimizer (CO), the algorithm’s performance is enhanced through the integration of various opposition-based learning (OBL) variants, which improve its ability to expand the search space and prevent becoming trapped in local optima. Three distinct OBL techniques are incorporated and applied iteratively across the two operational phases of MCO, thereby promoting greater solution diversity and accelerating convergence. The effectiveness of MCO is evaluated through tests conducted using the CEC 2020 benchmark functions. The results demonstrate superior results in terms of both accuracy and computational efficiency when compared to other state-of-the-art algorithms. To further validate its practicality, MCO is applied to four decision-making problems, where it consistently delivers high-quality, feasible solutions with lower computational effort and greater stability than well-established methods. These findings highlight the significant potential of MCO algorithm as a robust and versatile tool for optimization, contributing to the advancement of metaheuristic techniques in real-world applications.