Search Results (272)

Reliable electricity supply is essential for healthcare institutions, particularly where grid instability can disrupt service continuity, compromise patient safety, and affect the operation of critical medical equipment. In this context, selecting an appropriate photovoltaic (PV) proposal is a complex decision problem that requires assessing technical, economic, environmental, and regulatory factors jointly. This study develops a hybrid multi-criteria decision-making framework that integrates the Fuzzy Analytic Hierarchy Process (FAHP), the Decision-Making Trial and Evaluation Laboratory (DEMATEL), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to support PV proposal selection in healthcare institutions. The framework was applied to four competing proposals for a hospital case study in Barranquilla, Colombia. After integrating FAHP and DEMATEL, the economic, technical, and environmental criteria received balanced interdependence-adjusted weights of 0.324, 0.337, and 0.338, respectively. At the same time, DEMATEL identified the technical dimension as the main net influencing dimension within the expert-elicited influence network. The final ranking placed Proposal 1 first, followed by Proposal 4, Proposal 2, and Proposal 3, with closeness coefficients of 0.530, 0.518, 0.498, and 0.492, respectively. Additional comparative analysis showed that omitting DEMATEL changed the winning alternative, whereas preserving the FAHP–DEMATEL weighting structure and replacing TOPSIS with MARCOS yielded the same ranking. Robustness analyses further showed that the ranking remained stable in most supplier-exclusion and leave-one-expert-out scenarios. In contrast, bootstrap-based probabilistic sensitivity analysis showed that Proposal 1 ranked first in 96.2% of the replications. These results support the practical usefulness of the proposed framework for decision-making in healthcare energy planning. Full article

This paper considers the task of assessing the relevance of a procurement procedure for a supplier under conditions of growing volumes of open procurement data and increasing complexity of electronic publication services. It is shown that, for a supplier, relevance cannot be reduced to a match between a procurement notice and a search query or classifier code. In the European procurement context, procedure assessment requires consideration of the procurement object, lot structure, selection and award criteria, European Single Procurement Document requirements, procedure language, place of performance, participation through a specific legal entity, contract terms, data-protection requirements, service obligations, risk signals, and retrospective context. A formal mathematical representation of relevance assessment is developed, and on this basis a cascade agent model is proposed in which relevance is defined as the result of matching a procurement procedure, a supplier profile, and a set of evaluation rules. The model includes specialized agents for query normalization, procedure search, primary filtering, documentation preparation, fact extraction, constraint identification, procurement-object identification, customer analysis, contract-terms analysis, supplier-profile matching, dossier checking, relevance calculation, result interpretation, and final-card generation. The key intermediate result is an analytical procurement dossier containing structured facts, constraints, risk signals, matching results, and evidential links to sources. The proposed model is implemented as a research prototype and evaluated on an active stream of procurement procedures published through TED. The prototype evaluation shows that the cascade can transform a broad and heterogeneous set of retrieved procedures into a multilevel operational funnel comprising a shortlist, comparison pool, manual-review routes, and a monitoring layer. Within the reported case, these outputs demonstrate execution of the intended cascade sequence and generation of explainable user-specific assessments; they are not presented as evidence of ranking accuracy, superiority over simpler alternatives, or generalizability. Full article

Although government-directed supply-chain programs increasingly seek to enhance firms’ operational capabilities, their downstream effects on supplier selection—and the firm-level conditions that shape these effects—remain underexplored. Using China’s 2018 Supply Chain Innovation and Application Pilot Program (SCIAPP) as a quasi-natural experiment, we exploit the staggered designation of pilot firms and apply a difference-in-differences framework to a panel of Chinese A-share-listed firms from 2014 to 2023. This design allows us to examine how a government-led supply-chain digitalization initiative reshapes firms’ supplier selection decisions. We find that the SCIAPP designation increases the share of newly added suppliers whose AI capability exceeds the industry-year median by approximately 3.2 percentage points. Event-study estimates provide no evidence of differential pre-designation trends and show that the effect strengthens progressively during the post-treatment period. This temporal pattern suggests a gradual reorientation of procurement routines rather than an immediate or merely ceremonial response to program designation. The effect is weaker among firms with a stronger pre-existing internal AI orientation, indicating that the program primarily influences firms that have not yet incorporated AI capability into their supplier evaluation criteria. Heterogeneity analyses further show that the effect is more pronounced among firms with stronger general digital capabilities, non-manufacturing firms, and smaller firms—contexts in which the capacity to identify and integrate AI-capable suppliers, or the dependence on suppliers’ external AI resources, is relatively high. These findings extend the supply-chain digitalization literature from intraorganizational capability upgrading to interorganizational relationship formation and clarify how public digitalization policy can promote sustainable supply-chain realignment by encouraging firms to select technologically capable suppliers. Full article

Energy efficiency interventions are typically evaluated and implemented at the single-firm level, yet energy use and savings are shaped by interdependent decisions distributed across the supply chain, spanning sourcing, production, inventory, logistics, and financing. A foundational observation motivating this paper is that some energy efficiency actions are only possible through inter-firm cooperation: they require changes to partners’ processes or technologies, create benefits that accrue to different actors than those bearing the investment costs, and demand governance mechanisms (e.g., cost-sharing contract, buyer-financed supplier development, supply chain finance instruments) to be financially viable. This paper proposes CHAIN-EE (Collaborative Holistic Approach for Integrated Network Energy Efficiency), an action-oriented framework that operationalizes systems thinking into a practical roadmap for supply chain decision-makers. CHAIN-EE integrates three interconnected phases: (A) supply-chain energy diagnosis, covering boundary definition, baseline construction, and hotspot identification across nodes and flows; (B) action portfolio design, structured around a six-lever intervention taxonomy and multi-criteria evaluation embedding a cost–benefit alignment map that makes governance feasibility an explicit selection criterion; and (C) governance and continuous improvement, including incentive alignment, investment architecture and ISO 50001-compatible performance management. Evidence from four European research projects spanning the food cold chain, dairy, food-and-beverage/transport value chains, and HORECA illustrates how each phase operates in practice across different sectors and governance contexts. The paper contributes an integrative, sector-adaptable structure for supply chain energy efficiency programmes, grounded in both analytical research and applied project experience, and a targeted research agenda on cross-node rebound effects, data-enabled energy flow mapping, and multi-tier coordination mechanisms. Full article

Selecting suppliers for circular supply chains is an important requirement, demanding evaluation frameworks that capture reuse, reverse flows, and waste minimization beyond traditional metrics. This paper introduces a structured model designed to assess suppliers against specific circularity-oriented criteria. The Best-Worst Method (BWM) derives criteria weights, the Analytic Hierarchy Process (AHP) ratings evaluate alternatives, and a risk assessment stage consolidates the final ranking. The primary insights of this research include: (i) the development of a structured supplier evaluation model that encompasses dimensions like closed-loop integration, end-of-life management, material efficiency, and waste management into a multi-criteria perspective; (ii) applying BWM to derive consistent criteria weights, clarifying how circular performance attributes shape supplier prioritization; (iii) applying AHP ratings and risk assessment to consolidate the evaluation into a final ranking of alternatives; and (iv) demonstrating the operational feasibility and applicability of the framework through a real-world case analysis, providing empirical evidence for assessing circular supplier performance in industrial environments. Full article

Supplier selection in healthcare is a complex multi-criteria decision-making (MCDM) challenge requiring a balance of sustainability, resilience, and operational efficiency. Traditional methods struggle with scalability and subjectivity when applied to large administrative datasets. This study introduces a transparent hybrid Machine Learning–MCDM (ML–MCDM) framework, validated using a U.S. Medicare dataset of 661 suppliers. The framework integrates eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) for criterion prioritization, the Full Consistency Method (FUCOM) for mathematically consistent weighting, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for final ranking. As the dataset lacks direct sustainability metrics, seven indicators were synthetically generated; thus, the results serve as proof-of-concept demonstration of the framework’s architecture. Specifically, XGBoost–SHAP is trained to predict a synthetically constructed Overall Performance Score (OPS), meaning that the resulting feature importance output constitutes an algorithmic consistency check—confirming that the pipeline correctly recovers importance signals deliberately embedded in the training target. For interpretability, suppliers were segmented into five performance profiles via K-Means: Strategic Partners (17.7%), Green Leaders (18.6%), Reliable Emergency Suppliers (18.2%), Balanced Performers (20.4%), and Developing Suppliers (25.1%). Carbon Footprint Score (0.408) and Emergency Response Capability (0.316) achieved the highest feature importance. FUCOM-derived weights prioritized On-Time Delivery Rate (0.272), Carbon Footprint Score (0.222), and Emergency Response Capability (0.220). The top supplier attained a TOPSIS closeness coefficient of 0.800, showing strong discrimination. Sensitivity analysis across four scenarios confirmed ranking robustness, maintaining Spearman correlations ρ ≥ 0.977. This ML–FUCOM–TOPSIS approach provides an auditable, scalable, and policy-relevant decision-support tool, enabling procurement managers to navigate high-dimensional data while ensuring operational continuity and environmental responsibility in healthcare supply chains. Full article

In light of the growing prominence of environmental issues, the frequent occurrence of unexpected incidents, and the dynamic challenges of a changing market environment, suppliers must possess comprehensive capabilities that encompass both green and sustainable development as well as resilience to risks. Consequently, green supplier selection has emerged as a critical research topic. By integrating virtual and physical systems, digital twin technology enhances supply chain transparency and efficiency—a capability that plays a significant role in advancing sustainable supply chain development. In view of this, this study incorporates risk factors into the green supplier evaluation system, introduces indicators related to digital twin technology, and proposes a stochastic slack-based measure data envelopment analysis method, namely SSBM, for evaluating green suppliers. This approach expands and refines the existing evaluation criteria and the decision-making model. Finally, a numerical case study is conducted to validate the feasibility of the proposed method. This research provides more systematic and scientific decision support for green supplier selection, enriching the theoretical and practical applications in the fields of green supply chain and multi-criteria decision-making. Full article

Acquisition costs account for a significant share of total construction project costs, underscoring the importance of purchasing and supply management for organizational success. Supplier selection and evaluation are particularly critical because they involve multiple criteria, qualitative and quantitative attributes, and several decision-makers. In the construction industry, these activities become even more complex due to sector-specific characteristics such as convergent material flows, temporary facilities, buyer–supplier conflicts, price-oriented decisions, and the volatility of project-based markets. This paper investigates the supplier evaluation process in a construction company and identifies the company’s requirements and decision-makers’ expectations. Based on the collected data, this research proposes a model aligned with the company’s characteristics and the decision-makers’ expectations. The model combines two methods: the Intuitionistic Fuzzy approach to aggregate decision-makers’ opinions and ELECTRE TRI to classify suppliers based on predefined criteria and thresholds. The proposed model handles different weights assigned to each decision-maker for each criterion without allowing compensation among criteria. This model also explores the role of symmetry in multicriteria decision-making by combining Intuitionistic Fuzzy Sets with the ELECTRE TRI method. Decision-makers validated the proposal and emphasized its simplicity and flexibility, which allow future adjustments to both the criteria weights and the decision-makers’ assigned weights. Full article

Background: Circular supply chains (CSC) have emerged as a strategic response to sustainability challenges, while adoption remains uneven. Supplier selection is a key driver of effectiveness, shaped by organizational capabilities, institutional support, and leadership. This study develops a structured framework for circular supplier selection (CSS) using a hybrid multi-criteria decision-making approach, addressing fragmented research and strengthening the link between methodological innovation and practice. Methods: The proposed framework integrates fuzzy DEMATEL, the Best-Worst Method (BWM), and the Analytic Hierarchy Process (AHP) within MCDM. Fuzzy DEMATEL identifies cause-and-effect relationships among criteria, distinguishing net causes from net effects. The most influential and dependent criteria serve as anchors for the BWM weighting, followed by AHP to evaluate sub-criteria and alternatives. Results: Environmental governance emerged as the most influential driver in the causal analysis, while circular performance received the highest weight in BWM. The final AHP evaluation ranked Alternative 5 as the most suitable, followed by A9 and A3, confirming the framework’s ability to deliver consistent, actionable insights for circular supplier selection. Conclusions: This integration enables a more granular and robust evaluation of supplier strategies within CSC, reinforcing their role in accelerating sustainability transitions. It establishes a structured framework for CSS, highlighting CSS performance and upstream supply chain decision-making. Full article

This study investigates the role of data structure in multicriteria sorting by integrating supervised and unsupervised approaches. Specifically, a hybrid framework combining TOPSIS-Sort-B and cluster analysis is proposed to define class boundaries and evaluate sorting quality. Unlike traditional studies that focus primarily on methodological performance, this work emphasizes the impact of criteria conflict and trade-offs on class formation and stability. A unified performance-based labeling scheme is introduced, and a Criterion-Driven Consistency Indicator (CDCI) is used to quantify intra-class similarity. This indicator assesses the extent to which alternatives within the same class exhibit similar performance across criteria, offering a complementary perspective to conventional distance-based metrics. The proposed framework is validated through multiple case studies with distinct structural characteristics, including a highly structured dataset, a trade-off-intensive electric vehicle dataset, and an intermediate supplier selection problem. The results show that sorting outcomes are largely driven by the intrinsic structure of the data rather than by the choice of method. Datasets with low criteria conflict yield high class consistency and clear separation, whereas strong trade-offs lead to reduced cohesion and overlapping class boundaries, especially for intermediate alternatives. Overall, the study demonstrates that incorporating criteria-level information is essential for the robust evaluation of multicriteria sorting. The proposed approach enhances interpretability, reduces subjectivity in class definition, and provides new insights into the relationship between data structure and sorting consistency. Full article

Background: Nutrient-enriched sports foods can support efficient nutrient intake in specific circumstances in athletic nutrition management, such as during competition, when training away from the usual environment, or during periods of weight management. Despite their widespread availability, sports foods are not always used appropriately, necessitating tools to support informed product selection. Objective: This study aimed to characterize sports foods consumed by Japanese athletes and to develop a Japanese sports food exchange list to facilitate product selection based on target nutrient requirements. Methods: Seven sports food categories commonly used in Japanese sports settings were examined: sports drinks, energy jellies, energy bars, energy gels, protein drinks, protein bars, and protein powders. Following the methodology of Spain’s sports food exchange list, development proceeded in two stages. First, suppliers were selected based on INFORMED CHOICE certification or listing on the Japan Anti-Doping Agency’s product information website, with input from experienced sports dietitians. Subsequently, 523 products were classified into subcategories based on nutrient content per unit using established statistical criteria, including the mean, standard deviation, coefficient of variation, and z-values. Results: After excluding products with z-values outside ±2 or compositions deemed unsuitable for carbohydrate or protein supplementation, 498 products from 36 suppliers were classified into 24 subcategories. Japanese sports foods exhibited broad distributions in nutrient composition, variability derived from ingredient differences, and a high proportion of plant-based protein powders. Conclusions: This study developed a Japanese sports food exchange list comprising 498 products across 24 subcategories, enabling evidence-based product selection aligned with the nutrient intake goals of Japanese athletes. Full article

Conventional fuzzy multi-criteria decision-making (MCDM) methods support ranking under uncertainty but often provide limited explanation of why alternatives are preferred. This study proposes an explainable fuzzy decision-making framework that integrates the Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy TOPSIS with surrogate modeling, SHAP-based analysis, and linguistic rule extraction. The main contribution is an explanation layer that preserves the original FAHP–FTOPSIS ranking structure while decomposing ranking scores into criterion-level contributions and transforming recurrent attribution patterns into IF–THEN rules. The framework is evaluated through a supplier-selection case study using expert fuzzy evaluations, local perturbation analysis, leave-one-supplier-out cross-validation, and a synthetic benchmark. The results show that the fuzzy MCDM layer produces discriminative rankings and that the top-ranked supplier remains comparatively stable under perturbations. Among the tested surrogates, the Random Forest Regressor achieved the strongest local fidelity, outperforming linear regression and a shallow decision tree. SHAP analysis showed ordinal alignment between FAHP weights and global criterion importance, while the extracted rules achieved high coverage, consistency, and threshold stability. The framework is useful for researchers, decision analysts, procurement managers, and supply chain professionals who require transparent, interpretable, and auditable multicriteria decisions under uncertainty. Full article

The pharmaceutical development of cannabis-based medicinal products is challenged by significant variability in the quality, composition, and standardization of plant-derived active pharmaceutical ingredients (APIs). In Ukraine, despite recent legislative liberalization, a substantial shortage of standardized raw materials continues to limit the development of innovative dosage forms. This study analyses international practices among API manufacturers to identify technological parameters necessary to overcome domestic market barriers and support the implementation of advanced drug delivery systems. Content analysis was conducted on regulatory documentation, professional literature, and manufacturers’ technical specifications. Candidate evaluation followed predefined inclusion and exclusion criteria. The study assessed compliance with Good Manufacturing Practice (GMP) requirements, extraction and purification technologies, the extent of analytical characterization, and batch-to-batch reproducibility. Purposive sampling enabled a comparative analysis of various technological approaches. Marked heterogeneity was observed in API standardization and analytical control indicators among manufacturers. Possession of a GMP certificate was found necessary but may be insufficient to ensure the pharmaceutical equivalence of materials. Differences in extraction methods and purification levels may affect stability profiles, pharmaceutical development strategies, and risk management related to final product quality. The findings demonstrate that manufacturer selection is a critical decision point in pharmaceutical development, with substantiated supplier choice directly influencing dosage form development and regulatory compliance. Full article

In complex multi-criteria decision-making scenarios, the inherent ambiguity of evaluation data and the frequent unavailability of complete attribute weight information pose significant challenges for domain experts. To address these methodological limitations, this study proposes a novel TOPSIS-based decision-making framework that integrates optimization algorithms with probabilistic linguistic term sets (PLTSs). Specifically, a distance measurement optimization model is constructed to objectively resolve the issue of incomplete attribute weight information. This mathematical approach enables the seamless fusion of qualitative expert judgments with quantitative metrics, effectively managing uncertainty and information deficiency in the decision-making process. To validate the practical viability and superiority of the proposed methodology, it is applied to an empirical case study of supplier selection in the cold chain logistics sector for fresh and perishable commodities. The evaluation encompasses three core dimensions: (i) environmental sustainability and energy efficiency, (ii) quality assurance and operational control, and (iii) supply chain collaboration and resilience. Empirical findings demonstrate that the proposed methodological framework substantially strengthens the robustness and reliability of selection outcomes under information-deficient conditions. Relative to conventional approaches, the developed framework demonstrates superior mathematical adaptability and effectively captures decision distortions, thereby offering rigorous theoretical contributions to decision-making under uncertainty and providing actionable practical guidance for complex supply chain evaluations. 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