Search Results (359)

Large inland waterway infrastructure projects are increasingly exposed to supply disruptions, logistics uncertainty, carbon-control pressure, and dredged-material management challenges. Although resilient supplier selection, closed-loop supply chains, and ESG-oriented optimization have been widely studied, existing models rarely integrate resilient sourcing, hub configuration, forward material supply, reverse dredged-material resourceization, and social externality penalties within a unified maritime infrastructure decision framework. To fill this gap, this study proposes an ESG-endogenous closed-loop supply-chain optimization model for construction of an inland waterway navigation hub. The model jointly optimizes resilient supplier selection, transshipment/resourceization hub activation, equipment deployment, forward material flows, and reverse dredged-material flows. Three objectives are considered: minimizing economic cost, minimizing carbon emissions, and maximizing net social benefit. In particular, a social benefit and ecological-debt penalty function is introduced to quantify the transition from beneficial reuse to disposal-related negative externalities. NSGA-II is adopted as a multi-objective solver, with parameter calibration, convergence analysis, and benchmark comparison used to evaluate computational performance. The Pinglu Canal project is used as a case study. The results produce 14 Pareto-optimal solutions and show that the lowest-cost and lowest-emission configurations may still generate negative social benefits. A low-cost ESG transition region around 197.3–197.8 million CNY is identified, where limited additional investment can activate resourceization pathways and shift the system from ecological debt to near-saturated social benefit. These findings suggest that sustainable infrastructure planning should move beyond isolated cost or carbon minimization and instead identify balanced supplier–hub–equipment–flow configurations that jointly support resilience, circularity, and ESG performance. Full article

Reliable prediction of supplier lead times is important for understanding resilience in complex adaptive supply chains, which function as socio-technical systems characterized by high variability, dynamic interactions, and operational unpredictability. This study proposes a simulation-based adaptive lead-time prediction framework that unifies uncertainty-aware statistical modeling, digital twin-enabled simulation, IoT-linked operational adjustment, and AI-driven temporal learning within a single system-oriented architecture. Semi-synthetic datasets are used to emulate lead-time variability and disruption patterns across multiple operating scenarios under intermediate and elevated levels of uncertainty. The novelty of the study lies not in the use of individual techniques in isolation, but in their integration within a closed-loop predictive framework that links probabilistic modeling, adaptive correction, and digital twin-based system updating. The results indicate that the baseline statistical model performs satisfactorily under stable conditions; however, its performance declines significantly when exposed to parameter variations and extreme disruptions. Under high-variability conditions, for example, RMSE at μ = 3.0 and σ = 1.2 decreases from 65.00 weeks in the baseline model to 13.45 weeks in the IoT-adaptive model and to 3.00 weeks in the AI-enhanced model. These findings show that the proposed framework improves predictive accuracy, robustness, and adaptability relative to both the baseline statistical and IoT-adaptive alternatives. Overall, the proposed framework contributes to supply chain analytics by providing an integrated and simulation-based proof-of-concept for resilient lead-time prediction in complex supply environments. Its sustainability relevance should be understood as prospective: although the study does not directly measure emissions, energy use, or waste reduction, improved predictive stability and adaptive decision support may inform future sustainability-oriented planning and empirical evaluation. Full article

Food systems are currently challenged by a difficult balance: they rely heavily on natural resources while simultaneously generating significant volumes of waste, all under increasing pressure to decarbonize operations and close material loops. In this context, this study proposes strategic pillars for circular practices in the food industry, with an emphasis on the transformation of waste and by-products into high value-added resources through bio-based processes supported by biorefineries, in line with the Sustainable Development Goals (SDGs). To underpin this proposal, a PRISMA-guided content analysis of the literature published between 2019 and 2024 (Scopus) identified 30 recurrent CE elements. These elements were systematized into five strategic pillars: valorization of residues and by-products; digitalization of the food supply chain; sustainable education and stakeholder engagement; strategic partnerships for circular business; and regenerative practices based on renewable resources. Together, these pillars point to practical pathways, including the reuse of residues to produce functional ingredients and nutraceuticals, the creation of innovative, sustainable packaging, the generation of renewable energy from biomass, the strengthening of local supply networks, and the use of digital technologies to enhance traceability and transparency. By integrating and organizing fragmented evidence, the proposed framework delivers effective guidance to food industry actors, helping overcome economic and operational barriers to circular practices while supporting collaboration with local partners and research institutions. In doing so, it additionally contributes to advancing key SDGs, particularly SDGs 2, 7, 9, 12, 13, and 17. Full article

Considering that consumers have dual reference effects of price and goodwill; that is, consumers have psychological expectations for price and brand goodwill when making consumption decisions. A difference game model with reference effects is established for a closed-loop supply chain composed of a manufacturer, a retailer and a recycler, and a bidirectional cost-sharing contract is adopted for coordination. At the same time, the impact of dual reference effects and the bidirectional cost sharing contract on supply chain members’ profits are further analyzed by numerical simulation. We find that: (1) The impact of the price reference effect and the goodwill reference effect on supply chain decisions and market demand exhibits significant cost interval dependence. Notably, within a specific cost interval and under the influence of the dual reference effects, the market exhibits a phenomenon of “high price, high demand.” (2) The price reference effect influences the power structure of the supply chain. Specifically, when the price reference effect exceeds a certain threshold, the retailer’s profit surpasses the manufacturer’s profit. (3) The bidirectional cost-sharing contract coordinates the discrete dynamic closed-loop supply chain under dual reference effects. Consequently, it achieves a double Pareto improvement in supply chain members’ profits and brand goodwill. Full article

This study addresses the increasing need to structure knowledge in the field of circular resource management, with a focus on energy efficiency and sustainable resilience. Previous studies have examined various taxonomies for the circular economy, yet a clear gap remains in understanding how energy efficiency and resilience serve as the main pillars for operational stability. This study is designed as a bibliometric analysis based on a selection of relevant scientific articles. The identified factors were extracted based on their frequency of occurrence in the literature and processed using statistical clustering techniques to group them into coherent categories. The results show that the field is defined by a set of interconnected factors that can be structured into distinct clusters, reflecting key dimensions such as operational performance, environmental impact, and system resilience. Specifically, the analysis demonstrates how energy-related attributes and resilience attributes act as stabilizing factors within closed-loop systems. Based on these findings, this study proposes a structured framework that organizes the identified factors into a clear configuration. This framework provides a reference point for researchers who aim to develop models in this area and for practitioners involved in the design and optimization of circular systems. This study contributes by offering a structured view of the field and by supporting the development of consistent analytical and decision-making approaches grounded in the necessity of balancing resource recovery with system stability. Full article

Fuzzing is an essential element of software supply chain security governance. Despite its importance, the widespread adoption of library fuzzing is limited by the significant costs associated with constructing fuzz drivers. Without a clear entry point, the reachable path space of the target library is determined by the interplay of API call sequences, parameter dependencies, and state constraints. As a result, fuzz drivers must achieve not only successful builds but also provide sufficient semantic context to enable exploration of deeper state machine interactions, thereby avoiding premature stagnation at superficial validation logic. To systematically assess advancements in automated fuzz driver generation, this paper develops a taxonomy organized around the primary data sources used to derive driver-generation constraints, categorizing existing approaches into four technological trajectories: Usage Artifact Mining, Source Code Constraint Inference, Binary Semantics Recovery, and Heterogeneous Data Fusion. Large language models are increasingly integrated into these workflows as generators and as components for constraint alignment and repair. To address inconsistencies in experimental methodologies, this paper introduces a bounded comparability-oriented evaluation perspective focused on three dimensions: validity, reachability-related evidence, and reproducibility and cost. Together with a disclosure and reporting protocol for metric comparability, this perspective clarifies the information needed for cross-study comparison and examines the unique features and inherent limitations of each technical trajectory. Based on these findings, three key directions for future research are identified: facilitating structural evolution in response to coverage plateaus to address deep logic unreachability; coordinating dynamic closed-loop orchestration that utilizes on-demand heterogeneous data retrieval to resolve context challenges; and developing language-agnostic driver representations with pluggable adaptation mechanisms to improve cross-ecosystem portability and scalability. Full article

This study presents the first cradle-to-gate life cycle assessment (LCA) of T-shirt production using viscose and Lyocell fibers, benchmarked against cotton and polyester under consistent system boundaries. The analysis covers spinning, knitting, wet processing, garment assembly, and regionalized energy supply. Results show that cotton T-shirts exhibit the lowest global warming potential (14.1 kg CO₂eq/kg) but the highest water demand (2.9 m³/kg) in China. Polyester garments, although less water-intensive, contribute significantly to plastic accumulation (1.0 kg/kg shirt) compared to cellulose-based fibers (0.1 kg/kg shirt). Within man-made cellulose fibers, Lyocell generally outperforms viscose in toxicity-related categories—reducing freshwater ecotoxicity by 35% and human non-carcinogenic toxicity by 62%—thanks to its closed-loop solvent recovery. However, Lyocell also shows the highest carbon footprint (21.6 kg CO₂eq/kg) unless produced in regions with cleaner energy mixes. Regional sensitivity analysis indicates that shifting production from China to Brazil could reduce global warming impacts by up to 38%. Overall, these results highlight the trade-offs across fiber types and demonstrate the importance of both material choice and production geography in driving sustainability within textile supply chains. Full article

The increasing pressure on natural resources and the environment has intensified the need for sustainable cascade utilization in closed-loop supply chains (CLSCs). This study develops a game-theoretic framework to examine cascade utilization under both constant and heterogeneous quality restoration costs across three collection structures: centralized, manufacturer-led, and third-party collection. The results show that the relative performance of different structures depends on key economic conditions, including material recycling revenue and the comparative advantage of remanufacturing. No single structure dominates across all dimensions: a manufacturer-led collection tends to promote new product sales, while a third-party collection enhances remanufacturing and recovery levels, particularly under cost heterogeneity. Environmental performance, evaluated through collection quantity, cascade utilization efficiency, and an environmental impact indicator, also varies across structures, with cost heterogeneity shifting advantages toward the third-party collection. Policy analysis further indicates that both collection and remanufacturing subsidies increase recovery volumes but operate through distinct mechanisms. The collection subsidy expands return flows but may reduce cascade utilization efficiency by directing more low-quality products to recycling, whereas remanufacturing subsidy promotes higher-value reuse pathways and improves environmental performance. These findings highlight the importance of aligning collection structures and policy instruments under different cost conditions to enhance resource efficiency and support the circular economy and sustainable consumption and production objectives. Full article

Background: The growing emphasis on sustainability has increased interest in understanding how environmentally oriented supply chain practices translate into organizational outcomes. However, empirical research examining how circular economy principles, technological integration, and sustainable supply chain management (SSCM) practices jointly influence green supply chain performance remains limited, particularly in developing economies. Methods: A quantitative research design was employed using survey data collected from 333 professionals in the Lebanese consumer goods industry through structured Likert-scale questionnaires. The proposed conceptual model was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to evaluate the measurement model and test the relationships among circular economy practices, technological integration, SSCM practices, green supply chain performance, and organizational performance. Results: The findings indicate that technological integration, circular economy practices, and SSCM practices collectively enhance green supply chain performance. The results further show that improved green supply chain performance supports stronger organizational outcomes. Conclusions: This study contributes to sustainable supply chain literature by integrating circular economy principles, technological capabilities, and SSCM practices within a unified framework. It highlights the strategic role of green supply chain performance in linking sustainability initiatives to organizational outcomes and provides insights for managers seeking to implement integrated sustainability strategies. Full article

The transition toward sustainable food systems requires innovative approaches to managing perishable products, where inefficient inventory practices contribute significantly to global food loss and environmental degradation. This study develops a circular-economy-oriented inventory optimisation framework for dairy supply chains that integrates environmental externalities and waste valorisation pathways into operational decision-making. Departing from traditional linear “produce–consume–dispose” models, this study embeds three core sustainability mechanisms into a stochastic dynamic-programming framework: (1) progressive environmental cost internalisation aligned with EU Emissions-Trading System carbon pricing, capturing both waste-related emissions and cold-chain energy footprints; (2) circular-economy value-recovery channels that redirect near-expiry products to secondary applications (animal feed, biogas production, industrial processing) rather than disposal; and (3) deterioration-aware demand management that minimises resource throughput while maintaining service levels. Empirical calibration using Ukrainian dairy industry data demonstrates that sustainability-integrated inventory policies reduce waste generation by 4.8–10% relative to conventional approaches, with high-deterioration products showing the greatest potential for improvement. The authors identify a critical threshold in the circular economy: when salvage recovery rates exceed 35%, waste becomes an economic and ecological asset, fundamentally altering the sustainability calculus of inventory decisions. Environmental costs account for 4.6% of total operating expenses at current carbon prices, a share projected to increase substantially as climate regulations tighten. The findings provide actionable guidance for dairy supply chain stakeholders pursuing the Sustainable Development Goals (SDGs 2, 12, 13): processors should establish circular-economy partnerships that achieve salvage rates above 35%, implement product-specific policies for high-deterioration items, and proactively integrate carbon pricing into inventory optimisation. The framework bridges sustainable operations theory and circular economy practice, offering a replicable model for transitioning perishable food supply chains toward closed-loop, low-waste configurations that simultaneously reduce environmental impact and enhance economic performance. Full article

This study investigates trade-in channel selection in a closed-loop supply chain under a hybrid carbon policy framework that integrates cap-and-trade and carbon taxation. Game-theoretic models are developed for three manufacturer-led channels: manufacturer trade-in (M-CX), retailer trade-in (R-CX), and third-party trade-in (T-CX). The analysis examines pricing strategies, profitability, and carbon emission reductions across these channels. The key findings are as follows: (1) Carbon tax consistently compresses manufacturer profits, whereas cap-and-trade mechanisms exhibit a non-linear U-shaped effect. Manufacturer profits remain highest under the M-CX channel, irrespective of policy intensity. (2) Retail prices are most sensitive to carbon policies under the T-CX channel, where trade-in rebates increase with carbon intensity. The R-CX channel sustains higher retail prices and rebates than M-CX, while T-CX surpasses both under conditions of high carbon intensity. (3) Carbon emission reductions decline sharply under M-CX and R-CX as policy stringency increases. In contrast, the T-CX channel establishes a buffering mechanism through rising rebates, exhibiting the slowest rate of decline. At low carbon intensity, T-CX yields the lowest reduction levels; however, under high intensity, it overtakes the other channels to achieve the highest reduction. This study offers insights for manufacturers’ channel selection and government policy coordination under hybrid carbon regulation regimes. Full article

Background: Circular economy (CE) practices, such as glass bottle refilling, are critical to the beverage industry’s sustainability. However, coordinating manufacturer marketing efforts with collector reverse logistics investment remains a strategic challenge. Methods: This study develops a Stackelberg game-theoretic model featuring a manufacturer and a collector. The model incorporates communication effort as a demand driver and analyzes the role of bottle quality (damage rates) and the reusable bottle unit cost on the optimal decisions of the players and the collection rate. Results: Equilibrium analysis shows that the quality of the reusable bottle and the rate of bottle damage are crucial in reducing the operational costs of the refilling program. Additionally, these factors significantly influence the decisions made by manufacturers and collectors regarding their investments in communication and collection systems. Conclusions: The study demonstrates that successful refilling requires strategic coordination between manufacturers and collectors, particularly in terms of communication and investment in reverse logistics. Managerial insights indicate that investing in the quality of bottles is the key factor for achieving joint profitability. Full article

The transition from linear production and consumption models toward circular economy (CE) systems represents a key pathway for improving the sustainability and resilience of energy supply chains. This review provides a structured synthesis of circular economy approaches applied across the full lifecycle of energy systems, encompassing resource sourcing, energy generation and conversion, processing, distribution, and end-of-life recovery. The analysis integrates conceptual frameworks with system-based and analytical modelling approaches, as well as environmental, economic, and operational performance assessment methods. The results reveal that current research remains largely fragmented across material, energy, and residual flow perspectives, with limited system-level integration and persistent inconsistencies in modelling and evaluation approaches. While circular strategies such as resource recovery, energy recirculation, and industrial symbiosis demonstrate significant potential for improving resource efficiency and reducing environmental impacts, their implementation continues to be constrained by data limitations, technological maturity, and coordination complexity across stakeholders. By consolidating the dispersed literature into a coherent analytical structure, this review clarifies the critical interdependencies between circularity strategies, modelling approaches, and performance metrics, and identifies the methodological gaps that currently limit progress toward integrated circular energy supply chains. The findings offer a structured foundation for researchers and practitioners working to develop more robust evaluation frameworks and governance mechanisms in this field, and point toward the convergence of digital technologies, multi-stakeholder governance, and lifecycle thinking as a productive direction for advancing the field. Full article

Global supply chains face intensifying pressures from disruption, regulatory complexity, and sustainability mandates, requiring a shift toward more resilient and adaptive coordination. While artificial intelligence (AI) and blockchain have been recognised as complementary enablers, their implementation remains largely fragmented, existing as isolated tools linked by manual data exchange rather than integrated, programmable logic. This paper addresses this orchestration gap by proposing the Dynamic Resource Orchestration Framework for AI-Blockchain Integrated Supply Chains (DROF-AIBC). Grounded in Resource Orchestration Theory (ROT) and Dynamic Capabilities Theory (DCT), the framework provides a theoretical foundation for the strategic and autonomous orchestration of digital resources. Unlike classic supply chain orchestration, which focuses on the linear coordination of physical assets and legacy systems, DROF-AIBC conceptualises an “intelligent conductor” as a coordination mechanism combining AI-driven analytics and smart contract-based execution. This mechanism supports the configuration, optimisation, and monitoring of resources in response to changing external signals, effectively closing the loop between analytical insights and verifiable execution. The paper further substantiates how this autonomous capability serves as a foundational roadmap for the Industry 5.0 paradigm, embedding human-centricity through Explainable AI (XAI) to provide a “provenance of logic”, promoting circular economy sustainability, and fostering systemic resilience in turbulent environments. The framework aims to provide both a theoretical foundation and a practical roadmap for orchestrating AI and blockchain to advance resilient, sustainable and adaptive supply chains. Full article

The adoption of circular economy (CE) practices and artificial intelligence (AI) in the supply chain (SC) has become extremely significant in manufacturing organizations. The CE seeks to facilitate sustainable growth by managing the flow of materials and energy within closed-loop systems. The CE has resulted in the development of sustainable business models. AI capabilities transform work activities, data flows, and organizational processes. Therefore, the present study aims to develop a framework to improve circular supply chain (CSC) adoption in the automobile manufacturing sector by identifying and analyzing CE practices and AI-enabled digital strategies. The proposed framework was analyzed by employing a hybrid approach of Prioritized Weighted Average–Criteria Importance Through Intercriteria Correlation–Preference Ranking Organization Method for Enrichment Evaluations-II (PWA-CRITIC-PROMETHEE-II) under an Interval-Valued Fermatean Fuzzy (IVFF) environment. IVFF-CRITIC was employed to determine the CE practices’ weights, while IVFF-PROMETHEE-II was utilized to establish the relative index of AI-enabled digital strategies to enhance the CSC adoption. The key findings of the current study indicate that “AI-enabled infrastructure configuration for circular economy adoption in the supply chain”, “AI-integrated equipment to facilitate adaptability and mass personalization”, and “Robotics and AI-driven manufacturing and material reclamation” are the most significant AI-based digital strategies that support CE practices to enhance the adoption of a CSC and encourage case example manufacturing organizations to align their operations with AI and CE. Moreover, the outcomes of the study will deliver a comprehensive evaluation of CE practices and AI-enabled digital strategies for SC managers, based on the relative indexing obtained through the implementation of the hybrid approach. Full article