Ordering the chaos of sustainable supply chains: A q-rung fuzzy hypersoft framework for multi-attribute green supplier selection

ABSTRACT Supplier selection in circular supply chain networks poses a multi-criteria decision-making challenge where environmental sustainability and operational responsiveness must be jointly optimized. Although agility and circularity are both essential for competitiveness, prior studies have typically treated them as independent constructs, leaving a methodological gap in their integrated evaluation. This study develops and empirically validates a structured decision framework that integrates agile and circular supplier evaluation. A context-specific set of criteria is established based on the literature and expert input, and an integrated fuzzy PIPRECIA–CODAS methodology is employed to quantify trade-offs under uncertainty. The framework is applied to a medium-sized technology manufacturer in the sustainable mobility sector, demonstrating its ability to identify suppliers that balance flexibility, recyclability, and resource efficiency. Results show that the approach improves decision transparency and consistency compared to conventional weighting–ranking models. The findings indicate that the framework enables a more integrated evaluation of agility and circularity, supporting consistent and strategically aligned supplier selection decisions under uncertainty. Beyond methodological integration, the study offers a replicable decision-support framework that provides both theoretical contribution and practical guidance for aligning sustainability and responsiveness in supplier selection.

This study addresses supplier selection and order allocation problems under the risk of supplier disruption. Motivated by real-world supply chains characterized by supplier concentration, this study introduces the concept of a dominant supplier market into the supplier selection and order allocation framework. Conditional Value-at-Risk (CVaR) is incorporated to capture risk-averse procurement decisions. Single-level optimization models are developed, including a risk-neutral model, a CVaR-based risk-averse model, and a bi-objective model that balances risk-neutral and risk-averse objectives. The framework is then extended to a bi-level optimization model in which a dominant supplier strategically sets prices while anticipating the retailer's response. Computational experiments demonstrate the impact of disruption risk and supplier dominance on procurement decisions. While existing managerial insights in the literature have primarily focused on retailers' procurement strategies, this study provides decision support for both the dominant supplier and the retailer in selecting appropriate strategies in a dominant supplier market.

Supplier selection in new energy vehicle exports: A tri-clustering evolutionary game method for maritime transport

ABSTRACT Supplier selection is a pivotal element in establishing a robust and reliable supply chain. A supply chain network generally has multiple suppliers for product supply, and different suppliers possess distinct supply capacities, with their production processes accompanied by emissions. In addition, carriers with stochastic capacity are responsible for the goods delivery during which transportation emissions are generated. Consequently, this study includes supply capacity and emissions into a unified assessment framework to construct a comprehensive reliability metric, denoting the probability that the supply chain network can satisfy market demand subject to supply capacity and emission constraints. Accordingly, the optimal supplier selection problem is to find the best suppliers such that supply chain network reliability is maximized. A method that combines network reliability algorithm and the genetic algorithm is designed to address the optimal supplier selection alongside the corresponding maximum network reliability. The algorithm’s applicability is confirmed through a case study of a real supply chain network, and sensitivity analysis is carried out to explore the impact of supply capacity, emission and market demand on the optimal supplier selection, providing decision-making support for supply chain management.

Analysis and Priority Determination of Beef Suppliers in a Meatball Grinding SME Using the Analytical Hierarchy Process (AHP) Method Competition in the food industry, including bakso (meatball) milling businesses, demands precise decision-making in selecting suppliers for the main raw material, namely meat. Improper supplier selection can negatively impact product quality, operational costs, and business sustainability. The Analytical Hierarchy Process (AHP) method is used in this study to determine the best meat supplier for a meatball milling business at Tiban Market Center. The Analytical Hierarchy Process (AHP) method is used to structure the multi-criteria selection problem hierarchically and conduct pairwise comparison assessments of various criteria, such as availability, price, quality, and delivery timeliness. Three meat suppliers were evaluated as alternatives. Data collection was carried out through interviews, pairwise comparison questionnaires, and observations. The Analytical Hierarchy Process (AHP) analysis results show that the most important criterion is quality (weight 46.58%), followed by Timeliness (27.71%), Price (16.11%), and Availability (9.60%). The consistency test (CR = 0.0146) indicates that the evaluation is valid. Supplier 2 (0.5614) was chosen as the best supplier due to superior quality, although its price was not the lowest. Supplier 2 outperformed Supplier 1 (0.4714) and Supplier 3 (0.2786), respectively. These results provide a data-driven recommendation for the bakso milling business to select the supplier that best aligns with product quality and customer satisfaction.

Interactive optimisation (IO) combines the analytical power of optimisation frameworks with human’s contextual expertise. However, prior IO approaches require human users to repeatedly provide the same type of input or directly modify the model to incorporate different information. As a result, IO frameworks elicit a narrow range of human knowledge or require substantial optimisation expertise from users. To address these limitations, an IO framework is proposed that allows human users to respond to multiple types of queries. The framework aims to produce higher-fidelity stochastic multi-objective mixed-integer linear programming models. It employs targeted questions to elicit specific information from users, a Monte Carlo-based framework to transform human responses into input data for a scenario-based optimisation model formulation, and uses a Conditional Value at Risk (CVaR) formulation to balance expected performance with risk tolerances. Computational experiments on a supplier selection problem demonstrate that this framework can narrow the reality gap and converge towards the ground-truth solution. Moreover, it dynamically adapts to user feedback, and when the human expresses insufficient confidence in the solution’s performance, it can recommend solutions with narrower performance confidence intervals.

Decision-making (DM) problems in real-world environments are frequently described by ambiguity, expert hesitation, linguistic assessments, and incomplete information, which limit the effectiveness of traditional fuzzy set (FS) and intuitionistic fuzzy set (IFS) frameworks. To deal with such problems, this paper demonstrates an innovative and more expressive model, called linguistic cubic interval-valued intuitionistic fuzzy sets (LCuIVIFSs), which combines interval-valued intuitionistic fuzzy uncertainty, linguistic information, and cubic structures into a single framework. Some traditional operations of the newly defined LCuIVIFS model, such as union, intersection, and complement, are systematically introduced to ensure operational consistency and mathematical soundness. Within the framework of LCuIVIFSs, several aggregation operators (AOs), including arithmetic AO, geometric AO, weighted arithmetic AO, and weighted geometric AO, is presented to significantly combine complex and uncertain information. The key features of the proposed AOs are investigated. Moreover, a novel multi-criteria decision-making (MCDM) technique is developed using the newly defined AOs. To discuss the significance of the proposed approach, it is implemented to a case study of supplier selection problem in smart manufacturing, where both quantitative and qualitative criteria under uncertainty are considered. The final results ensure that the newly defined approach contributes reliable, flexible, and robust decision outcomes compared with existing FS-based models. The proposed study thus provides a valuable decision-support framework for complex DM problems under linguistic and cubic uncertainty.

An integrated framework for solving the green supplier selection and order allocation problem in steam procurement

The plywood industry is one of the manufacturing industries whose raw materials are based on the sustainability of natural resources. The challenges that arise are difficulties in obtaining raw materials especially Sengon wood, low quality wood, delays in delivery, and a critical land crisis in forest areas. This drives the need for supplier assessments that focus on sustainability and recommends developmental steps and ensures supplier compliance with requested sustainability standards. This study aims to formulate supplier performance evaluations based on sustainability aspects. The steps taken are through supplier segmentation using the Triple Bottom Line (TBL) approach to ensure supplier criteria are aligned with sustainability aspects. Furthermore, using TOPSIS, the selected suppliers will be determined according to the established criteria. The supplier development strategy is developed based on the characteristics of each supplier segmentation, namely compiling a written guidebook; company visits to transfer knowledge and share experiences; company reviews; regular oversight for the provision of feedback; and long-term contracts. This study contributes a framework for addressing raw material challenges and applying sustainability principles in supplier selection and development, aligned with the company's mission of sustainable growth.

Procurement — the organizational function responsible for sourcing, evaluating, and acquiring goods, services, and raw materials — is undergoing a profound transformation driven by the adoption of Artificial Intelligence (AI) technologies. Traditional procurement operations, characterized by manual supplier evaluation, reactive demand planning, paper-intensive purchase order management, and limited spend visibility, are being replaced by AI-powered systems capable of analyzing vast datasets, identifying cost-saving opportunities, predicting supply disruptions, automating routine transactional tasks, and generating strategic insights in real time. This research paper examines the role of Artificial Intelligence across the procurement value chain — from spend analytics and demand forecasting to supplier selection, contract management, and risk monitoring — evaluating both the opportunities and challenges of AI adoption in procurement operations. Drawing on secondary research from published academic literature, industry reports, and case studies of AI-enabled procurement transformation, the paper identifies the key AI technologies reshaping procurement — including Machine Learning, Natural Language Processing, Robotic Process Automation, and Predictive Analytics — and assesses their practical applications and measured outcomes. A comparative analysis of procurement performance metrics before and after AI adoption across selected case organizations is presented. The paper proposes an AI-Enabled Procurement Maturity Framework (AIPMF) and offers recommendations for procurement leaders, IT strategists, and policy makers seeking to accelerate AI adoption in procurement operations. Findings confirm that AI adoption in procurement delivers substantial and measurable improvements in cost reduction, process efficiency, supplier relationship management, and supply chain resilience

The accelerated rate of Industry 4.0 development has turned traditional manufacturing systems into highly networked, smart, and data-driven settings, thus making decision-making processes exceptionally complicated. Smart manufacturing systems are characterized by a number of conflicting criteria, interdependencies, and uncertainty, and thus require powerful and systematic decision-support tools. This paper is a systematic review of the use of multi-criteria decision making (MCDM) in smart manufacturing systems within the Industry 4.0 paradigm. A systematic literature review methodology is followed, which includes database selection, a keyword-based search, and inclusion and exclusion criteria based on the PRISMA framework. The analyzed literature is categorized into major areas of application, such as technology choice, supplier selection, production optimization, sustainability measurement, and risk management. Moreover, a comparative study of the popular application of MCDM techniques, including AHP, ANP, DEMATEL, TOPSIS, and hybrid methods, is conducted to outline their strengths and weaknesses and their applicability to various decision settings. The research points out key research gaps, such as the lack of full integration of artificial intelligence, inadequate treatment of uncertainty, and the absence of real-time decision frameworks. Lastly, possible future research directions are suggested, focusing on the creation of hybrid and AI-enhanced MCDM models for smart manufacturing systems. This review presents important lessons for researchers and practitioners who are interested in adopting effective decision-making models in Industry 4.0 settings.

An extended EDAS model for sustainable supplier selection in food supply chain management using interval-valued Pythagorean fuzzy soft set

Suppliers play a critical role in advancing sustainability and competitiveness within supply chains, particularly when circular economy principles are integrated into supplier selection processes. This integration not only supports environmental harm reduction and resource efficiency but also enhances cost savings and overall network performance. The healthcare sector, characterized by high material consumption, energy use, and regulatory demands, faces significant environmental and social challenges. Despite the importance of promoting circularity in healthcare supply chains to reduce medical waste and improve resource efficiency, sustainable circular supplier selection in this sector remains largely underexplored in the literature. To address this gap, an integrated MCDM approach combining the BWM and CODAS was applied in this study. The framework uses SDGs-focused criteria to evaluate supplier alternatives, aligning circular economy objectives with global sustainability targets. BWM ensures a consistent and efficient weighting process with reduced cognitive effort, while CODAS provides a straightforward yet robust method for ranking suppliers. Using the CODAS technique, the supplier alternatives were ranked based on their Hi values, where A1 achieved the top position with a score of 0.504, followed by A4 with 0.434, A2 at 0.082, A5 at -0.250, and A3 with the lowest score of -0.757. This ranking highlights A1 as the most favorable supplier according to the integrated evaluation criteria. The findings offer practical guidance for healthcare organizations seeking to strengthen circular practices and contribute to the Sustainable Development Goals. This study demonstrates the effectiveness of an integrated MCDM approach in supporting strategic sustainable circular supplier selection decisions in the healthcare sector.

This paper proposes a multi-criteria decision-making model based on the Probabilistic Uncertain Linguistic T‑Spherical Fuzzy Set (PULTSFS), integrating FUCOM, ITARA, and CRADIS, to address the ambiguity and uncertainty inherent in evaluation information for circular supplier selection. It also tackles the limitations of existing CRADIS methods in complex fuzzy environments, the lack of a scientific mechanism that synergizes subjective and objective weighting, and the incomplete depiction of evaluation information. First, a Euclidean distance measure is defined based on the relevant concepts of PULTSFS. Next, the subjective weights of the criteria are derived using the Full Consistency Method (FUCOM), while the objective weights are determined by extending the ITARA method within the PULTSFS framework, thereby establishing a combined weighting mechanism that integrates subjective and objective considerations. Finally, a multi-criteria decision-making model is constructed based on the novel PULTSF-CRADIS approach. Using circular supplier selection as a case study, sensitivity analysis and comparative validation demonstrate that the proposed model yields robust and reliable ranking results, with significantly enhanced capability in handling uncertain information and improved adaptability to complex decision-making scenarios, offering a more precise decision-making tool for supplier selection in the context of the circular economy.

Purpose Multi-Attribute Decision-Making (MADM) methods often yield inconsistent rankings of alternatives due to their distinct computational mechanisms, complicating the selection of optimal solutions. This study proposes a novel grey-based aggregation framework to systematically resolve such inconsistencies. Design/methodology/approach The framework employs Grey Systems Theory to quantify ranking uncertainties through interval grey numbers. A Python-coded algorithm compares these grey numbers using possibility degree functions, enabling systematic aggregation of divergent rankings. The approach is demonstrated through a supplier selection case study combining results from nine MADM techniques. Findings The framework effectively resolves ranking conflicts while preserving the strengths of individual MADM methods. Computational results validate its ability to produce stable, consensus rankings from inconsistent inputs. Practical implications Organizations can apply this approach to integrate diverse MADM recommendations in complex decisions like sustainability assessment, supply chain management, resource allocation, healthcare systems or performance evaluation, particularly where methods disagree. Originality/value While MADM methods each offer unique advantages in handling specific decision scenarios, their inconsistent outcomes create practical challenges. This research provides a GST-based solution that computationally reconciles these differences through grey number comparison, implemented via Python for practical application.

ABSTRACT In today’s competitive business, accurate evaluation of suppliers and relevant optimal order allocation to them is quite important and challenging. To overcome its underlying difficulties, this research proposes a hybrid approach for evaluating suppliers, forecasting demands, and allocating orders. In its implementation process, it initially applies Data Envelopment Analysis method based on Z-numbers to obtain more accurate, transparent and reliable data on suppliers. It then uses numerous machine learning algorithms to forecast future demands for various relevant items. Finally, it employs a multi-objective optimization model to minimize total supply costs, while maximizing order allocation to efficient suppliers and minimizing the number of suppliers to whom orders are placed subject to constraints of authorized delay in order delivery, capacity and demand satisfaction. The good accuracy of its generated results is related to its innovative part of using different machine-learning algorithms and integrating supply chain operation for more accurate evaluation of suppliers and relevant optimal order allocation to them. Its multi-purpose approach integrates supply chain decision-making regarding procurement costs while focusing on efficient and collaborative suppliers with strong, sustainable network connections. Its fuzzy programming approach has also played an effective role in the optimization process of its nonlinear multi-objective model.

Industry 5.0 requires manufacturing firms to redesign supplier portfolios by jointly optimizing economic performance and environmental and social responsibilities. Conventional Multi-Criteria Decision Making (MCDM) approaches often rely on static expert weights and rich ESG data, which are rarely available for small and medium-sized enterprises and sub-tier suppliers. This study proposes an Explainable Recommender System-Based Decision Support Model (XRS-DSM) that leverages operational logistics data to approximate sustainability and risk, thereby enabling pragmatic yet transparent supplier selection. The model constructs proxy indicators for carbon impact from transportation modes and for quality from defect and inspection records, and then applies K-Means clustering to group suppliers into interpretable strategic archetypes along a Sustainability–Efficiency Frontier. A weighted utility scoring mechanism with adjustable priorities allows managers to dynamically emphasize low carbon, high quality, or low cost and immediately observe the resulting re-ranking of suppliers. The XRS-DSM is evaluated on a real-world Supply Chain Logistics Dataset from Kaggle, containing multimodal transport, inventory, and performance variables for 100 SKUs in the FMCG sector. Experimental results indicate that the model can identify Pareto-efficient supplier sets that reduce defect rates and carbon footprint with only marginal increases in landed cost, thus narrowing the “Green Premium” associated with sustainable sourcing. The proposed framework offers a scalable, data-light, and interpretable tool for manufacturing decision-makers seeking to align profitability with Industry 5.0 and ESG agendas.

In this paper, we study a generalised two-tier supply chain wherein a firm with multiple facilities seeks to select a subset of suppliers with different prices, qualities, capacities, and carbon emissions. Exogenous demand in the second tier then is satisfied by the selected suppliers in a multi-sourcing framework. The firm in our setting seeks to minimise the integrated cost of sourcing, inventory planning, and emission penalties while adhering to operational limitations as well as regulatory constraints. In our setting, we assume that each supplier produces a stochastic amount of greenhouse gas emissions per unit supplied, leading to environmental cost for the sourcing facilities proportionate to the amount sourced. We develop an iterative heuristic coupled with an accelerated Bender's decomposition to solve the underlying NP-hard MINLP robust formulation. First, we demonstrate the superior performance of our methodology against a benchmark commercial solver in terms of both solution quality and run time. Next, utilising data motivated by a real case study, we derive extensive managerial insights with regards to robustness analysis, price of sustainability, and supplier selection. We conclude our analysis by explaining the implications of various parameter settings in practical decision-making.

UD. MKKG is a screen printing company facing product defect issues, such as ink not adhering, cracked prints, and imprecise results. This study aims to identify the primary causes of these defects and establish priorities for improving production quality in the screen printing unit. The research method used is the integration of Failure Mode and Effect Analysis (FMEA) with a Fuzzy approach to address subjectivity in risk assessment. Conventional RPN calculations showed the highest values for cracked prints (240), imprecision (150), and ink not adhering (75). The application of Fuzzy FMEA yielded more accurate and continuous FRPN values, namely cracked prints (249.5), imprecision (158), and ink not adhering (81.5). Proposed improvements include strict supplier selection, the use of periodic temperature thermometers, fabric pre-press techniques, and scheduling routine machine maintenance. The implementation of these steps is expected to minimize defective products and maintain customer confidence in product quality.