Search Results (1,365)

This article examines whether treaty-based and cooperation-oriented institutions can perform complementary functions in Indo-Pacific economic governance. It compares the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) and the Indo-Pacific Economic Framework for Prosperity (IPEF), while treating the Regional Comprehensive Economic Partnership (RCEP) and other regional mechanisms as contextual alternatives. Drawing on a structured 53-document corpus of legal, policy, institutional, scholarly, and project-level materials, the study maps overlapping participation and agreement-specific legal status, then analyses labour and supply-chain governance in Malaysia and Vietnam and an attribution boundary case on clean-energy cooperation in Indonesia. It develops selective complementarity as an issue-specific evaluative framework distinguishing thematic overlap, functional differentiation, prospective compatibility, operational interaction, and institutionalised interaction. The findings show functional differentiation and prospective compatibility in Malaysia and Vietnam, but no cross-framework linkage, transmission, uptake, or additional implementation value was documented within the defined corpus and period. Indonesia demonstrates that policy alignment does not convert bilateral, development-finance, or adjacent regional initiatives into IPEF activities. Japan is best understood as a cross-case facilitating actor because the evidence does not establish a stable bridging mechanism between the two frameworks. By setting clear evidentiary thresholds and identifying evidence that would count against complementarity, the study contributes to research on regime complexity, institutional interaction, and sustainability-oriented regionalism. Full article

This study addresses a large-scale location–allocation problem in a three-echelon automotive supply chain comprising 382 suppliers, candidate distribution centers, and six assembly plants. The planning task is to redesign the inbound consolidation network while minimizing transportation and distribution center operating costs, enforcing a 480 km supplier-to-center service radius, and achieving at least 90% demand-weighted coverage. We formulate a mixed discrete-continuous model with supplier-to-center assignment, center location, throughput, and flow decisions. A feasibility-oriented hybrid algorithm uses a genetic algorithm as the main search engine, ant colony construction to seed solutions near the feasible region, adaptive mutation and simulated annealing to preserve exploration and refine elite solutions, and an online neural surrogate to avoid a subset of costly exact fitness evaluations. The design differs from a simple collection of metaheuristics: all components share one variable-length encoding, the same feasibility metrics, and periodic exact reevaluation of candidate solutions. Using the competition case data, the redesigned network reduces total cost by 27.0% relative to the six-center baseline, decreases the demand-weighted average supplier-to-center distance from 461.3 km to 53.0 km, lowers the maximum distance from 2807.22 km to 441.78 km, and raises coverage from 45.0% to 100%. Across ten independent runs, the hybrid method obtains a mean cost 10.3% below that of a standard genetic algorithm, with lower run-to-run dispersion. The results show that feasibility-aware initialization, adaptive search, and selective surrogate evaluation can support practical redesign of a strongly constrained, national-scale inbound logistics network. The directly attached reproducibility package provides the MATLAB implementation and the seven supplied input workbooks used by the reported model. The evidence is limited to one deterministic competition instance, a fixed cost schedule, and fixed-topology sensitivity calculations; generalization under demand uncertainty, facility disruption, and alternative road conditions remains to be tested. Full article

To address the issue where information asymmetry in third-party logistics (3PL)-led low-carbon supply chain coordination undermines coordination efficiency, thereby threatening the sustainability and resilience of supply chain cooperation, this paper develops a Stackelberg dynamic game model with the 3PL as the leader. This model is constructed within the context where consumers’ low-carbon preferences influence product demand, and a government carbon cap policy is implemented. By comparing decentralized and centralized equilibria, we verify that centralized collaboration achieves dual gains: higher carbon reduction levels and greater overall supply chain profits, which strengthens sustainability and resilience. To address efficiency losses from three types of information asymmetry, we propose a two-stage dynamic coordination mechanism adapted to evolving cooperation transparency. At the initial stage with opaque information, a bargaining-power-weighted profit-sharing contract is adopted, where negotiation weights are quantified by enterprise scale, resource control and industry influence. After data transparency improves, the system switches to a Nash bargaining framework supported by blockchain carbon data sharing to realize stable long-term collaboration. Numerical cases and sensitivity analysis demonstrate that manufacturer cost information asymmetry is the primary constraint on coordination efficiency. The proposed dynamic coordination scheme effectively mitigates systemic complexity, balancing economic benefits and carbon reduction targets. This study provides practical pathways for supply chain participants to navigate complex low-carbon environments and advance sustainable, resilient supply chain operation. Full article

With the continued advancement of low-carbon campus transformation and the increasing penetration of renewable energy, campus integrated energy systems have become key infrastructure for green campus development. However, the highly random nature of student behavior causes dynamic fluctuations in electricity, heating, and cooling loads, creating major challenges for real-time supply-demand balance and economic system scheduling. To address this problem, this paper takes student behavior uncertainty as the core disturbance factor and proposes a flexible architecture-driven autonomous adaptation and multi-energy complementary optimization strategy. A closed-loop operation paradigm of signal–response–complementarity–regulation is established, in which dynamic electricity price signals, comfort-oriented guidance, and campus functional energy-zone division are combined to form a multi-level autonomous response chain. To improve solution efficiency, the electromagnetic wave propagation algorithm is further enhanced, and a Multi-Objective Electromagnetic Wave Propagation Algorithm (MEMWPA) is developed. Wave-impedance matching and energy-flux-density feedback mechanisms are introduced to strengthen convergence performance in complex multi-objective optimization problems. Comparative case studies show that the proposed strategy can effectively smooth the net load curve, reduce the campus peak load by 26.73%, and increase the load factor by 14.533 percentage points, thereby improving both operational flexibility and energy efficiency. Full article

Steel production contributes significantly to global resource consumption and accounts for nearly 8% of worldwide CO₂ emissions. As the construction industry moves toward decarbonization, the reuse of structural steel has emerged as a highly impactful strategy for reducing embodied carbon and extending material life cycles. However, most end-of-life structures are still demolished using conventional methods that prioritize recycling for scrap value rather than recovering reusable steel components, resulting in the loss of high-quality material and reduced environmental and economic benefits. Despite growing interest in circular construction, systematic analyses of real-world structural steel reuse projects remain limited. This restricts the understanding of the practical conditions required for scalable implementation. Moreover, there is a lack of structured approaches for identifying the factors influencing deconstruction and large-scale structural steel reuse across reverse supply chains. This study addresses these gaps by developing a framework of factors affecting efficient deconstruction and structural steel reuse, identified through a thematic analysis of the literature and structured around structure attributes, business attributes, value chain activities, project management, and regulations. Fifteen case studies were analyzed: two from North America, twelve from Europe, and one from Asia. A structural steel reuse classification model is introduced to support cross-case comparison of coordination demands, implementation barriers, and scalability potential within reverse supply chains. The results demonstrate that reuse pathways differ substantially in coordination requirements, implementation complexity, and scalability outcomes. They further indicate that the dominant challenges to structural steel reuse are no longer primarily technical, but instead stem from system-level coordination gaps, limited information availability, and insufficient integration across reverse supply chains. Based on these findings, targeted solutions are identified, including improved dismantling strategies, enhanced documentation systems, dedicated storage infrastructure, and stronger regulatory alignment. This study provides a structured analytical approach and practical recommendations to support decision-making, stakeholder coordination, and the scalable implementation of structural steel reuse. Full article

Geopolitical conflicts, resource-protection policies, and unexpected disruptions have heightened supply-security concerns across the global nickel industry chain. This study constructs a multilayer trade network based on complex network theory to characterize structural evolution across the upstream, midstream, and downstream segments and applies a cascading-failure model to simulate the propagation of supply risks. There are four main findings: (1) The global nickel trade network exhibits pronounced layer heterogeneity, with the midstream layer acting as the principal amplifier of cascading failure risks. (2) Nodes with high centrality and broad cross-layer participation largely coincide with the countries that generate the largest systemic risks. (3) A small group of countries controls most trade flows and dominates risk transmission. (4) The center of systemic risk is shifting from traditional industrial and trading economies toward resource suppliers and countries that integrate resource extraction with processing. These findings support a risk-governance strategy based on diversified supply sources, dynamic monitoring of critical nodes, improved resilience in midstream smelting and refining, strategic resource stockpiling, and the circular utilization of nickel resources. Full article

Background: Australia has experienced high rates of antibiotic shortages, yet little is known about how Australian pharmacists experience and manage them. This study aimed to explore Australian community and hospital pharmacists’ experiences of antibiotic shortages, including their perceived clinical impact and approaches to managing shortages. Methods: An exploratory qualitative study using semi-structured interviews was conducted with Australian community and hospital pharmacists between May and August 2024. Participants were recruited via convenience sampling through professional networks and social media. Interviews were audio-recorded, transcribed verbatim, and analysed thematically using an inductive–deductive approach, with coding performed independently by two researchers. Results: Fifteen interviews were conducted before data saturation was reached. Sixty percent of participants were female, 67% worked in hospital settings, and pharmacists were located across five Australian states. Four overarching themes were identified: factors driving shortages, communication and collaboration, impact of shortages, and mitigation and prevention. Pharmacists described antibiotic shortages as frequent and unpredictable, attributing them to supply-chain vulnerabilities, limited manufacturing, and market competition. Shortages were reported to affect treatment choice, contribute to antimicrobial resistance concerns, increase pharmacist workload and stress, and negatively affect patients. Pharmacists used strategies such as stock management, collaboration, and importing alternatives, but felt that lasting solutions required coordinated national action. Conclusions: Australian pharmacists perceive antibiotic shortages as a complex challenge affecting patient care, antimicrobial stewardship, workload, and healthcare efficiency. While pharmacists have developed reactive strategies to manage shortages, stronger communication, collaboration, and national policy coordination, alongside improved supply-chain resilience, are needed to reduce the impact of future shortages. Full article

Ti-6Al-4V has seen widespread adoption in the aerospace industry due to its advantageous material properties, but the alloy is costly to produce with vulnerable supply chains. Repair and remanufacture offer economic and environmental benefits over scrapping components. Powder-based additive-manufacturing processes have been investigated; however, coaxial wire laser metal deposition (LMD) remains understudied in repair scenarios, and the transfer of planar process parameters to inclined geometries for complex repairs has not been established. This paper identifies a process window for 1.2 mm Ti-6Al-4V wire and applies the parameters to repair a trapezoidal groove. Tensile and fatigue properties are evaluated, and fractography is conducted using optical and scanning electron microscopy. Findings show the transfer from planar to inclined geometry induces evolving geometric and thermal boundary conditions, including underbuilding and thermal accumulation. Nonetheless, the repair exhibited a yield strength of 868.2 MPa, an ultimate tensile strength of 920.5 MPa, and an elongation of 8.9%. Moreover, UFT revealed a fatigue performance of the repairs that was consistent with heat-treated SLM materials. Fractographic analysis revealed triangular defects and feedstock contamination that contribute to reduced repair properties. As such, this paper demonstrated that coaxial wire-LMD can be used to deposit material into complex geometries, but a complete, defect free repair was not achieved. Full article

The need for intelligent optimization of supply chain operations has become imperative for organizations seeking to adapt quickly to an increasingly complex logistics environment. The complexity of predicting delivery delays stems from the large number of interdependent operational and external factors that may affect delivery performance. In this study, we propose a hybrid machine learning framework that employs both the Random Forest Classifier (RF) and the Particle Swarm Optimization (PSO) to provide more accurate predictions of delivery delays through automated hyperparameter tuning. We evaluated our proposed PSO–RF model using a publicly available benchmark dataset of shipment records, along with their respective operational characteristics and contextual risk indicator values. In the comparative experiments PSO–RF produced the best single configuration among the metaheuristic-tuned models, reaching an accuracy of 94.6% and a Matthews correlation coefficient of 0.739 at a swarm size of 10. This advantage is specific to the small-swarm setting: at swarm sizes of 20 and 40 the four optimizers examined the (PSO, SCA, GWO, DE) cluster within a narrow MCC band of 0.71–0.74, and SCA–RF is the most consistent, so no optimizer is uniformly superior across swarm sizes. Against the conventionally configured baseline classifiers the improvement is far larger and holds in every configuration, which indicates that the principal gain arises from optimizing the Random Forest hyperparameters rather than from the choice of search algorithm. Additionally, to provide actionable supply chain intelligence, we employed SHapley Additive exPlanations (SHAP) to conduct model interpretability analyses. The SHAP analysis identified that the most influential features within the model’s delay classification decisions include shipping costs, order weights, geopolitical risks, scheduled lead-time days, transportation mode, and base lead-time days. Full article

The digital transformation of shrimp aquaculture in Ecuador has accelerated the adoption of traceability technologies to improve transparency, regulatory compliance, and supply chain coordination. However, persistent structural limitations—such as technological fragmentation, low interoperability, and inconsistent data quality—continue to constrain their effectiveness. This study develops an approach to managing informational uncertainty in traceability systems, grounded in information theory and operationalized through the Technological Management Model for Shrimp Production (TMMT-SP). Methodologically, the research follows an abductive systemic modeling approach, integrating a systematic literature review, structural analysis of the production system, and ontological modeling to identify and classify interdomain gaps. The findings show that informational uncertainty emerges as a structural property of the system, resulting from misalignments across informational, technological, and governance dimensions. These misalignments limit the coherence, reliability, and integration of traceability processes. In response, the study proposes a structural entropy-based framework (understood as an operational representation of systemic informational dispersion) to diagnose, prioritize, and address these gaps, shifting the focus from isolated technological adoption toward systemic coherence. This approach provides a conceptual and methodological basis for designing technology management strategies to reduce uncertainty in complex production systems. Full article

Background: Global food supply chains have become increasingly complex, sourcing ingredients from multiple countries and intermediaries, creating opportunities for fraud, adulteration, and mislabeling that may compromise consumer safety and market confidence. Digital traceability technologies have been suggested as potential countermeasures, but there is little concrete evidence of their impact in practice. This research presents an assessment of the maturity and effectiveness of these technologies, identifies implementation barriers and security/privacy concerns, and maps research gaps/future directions. Methods: A scoping review of 64 studies from 2023 to 2026 with data extracted from the Scopus and IEEE databases was carried out according to the PRISMA-ScR guidelines and a structured pre-specified data extraction framework. Results: The field is empirically immature, with none of the reviewed solutions offering a provably correct, adversarially tested solution to the oracle problem. There is a lack of alignment between on-chain immutability and GDPR right to erasure and an unequal burden of implementation costs imposed on smallholder producers. Conclusions: A gradual implementation of traceability regulations, along with cost-of-ownership models and harmonized certification measures that do not disadvantage smaller producers are proposed. Finally, field trials, adversarial testing and reporting results in a standardized format, capturing detection performance and implementation costs, are essential. Full article

A maritime chokepoint shock can spread beyond the countries directly connected to the affected route. We develop a monthly resilience screening model for a hypothetical Strait of Hormuz closure that separates direct disruption, scarcity-driven trade squeeze, household access pressure and delayed production risk. Hormuz severity variants and related Red Sea, Black Sea and fertilizer node scenarios test the stability of the modeled mechanism ordering. A three-event historical panel provides a limited external consistency check, not full model validation. In the baseline Hormuz stress test, mean modeled import availability pressure ranges from 0.0023 to 0.0076 on the normalized scale during March–August 2026. Trade squeeze accounts for 92.1–94.5% of that pressure, compared with 5.5–7.9% for direct disruption. Of 161 countries entering model-Alert, 39 later enter model-Crisis or a higher internal state. Crossings concentrate where reallocation pressure coincides with weak household access and delayed production risk. Historical estimates are directionally consistent for some events, although the Suez window exhibits non-flat pre-event coefficients and is not interpreted causally. The state labels are internal thresholds, not humanitarian classifications. The model identifies which propagation layer is binding and how quickly the associated intervention window closes. Full article

Sustainability certification schemes (SCSs) such as LEED, BREEAM, etc., are widely accepted as an effective tool for the promotion of sustainable design principles and lifecycle carbon reduction in building projects. Greece, despite having a relatively small real estate market, presents solid activity in this field, but the related research remains extremely limited. Following a comprehensive quantitative data analysis regarding the use of SCSs in Greece and Europe, this paper examines adoption determinants, challenges, and prospects in the Greek construction sector, drawing on semi-structured interviews. The findings show that LEED dominates the Greek market, and although the SCS benefits are well documented in the literature, the overall market demand is largely confined to office and commercial developments and constrained by affordability concerns, low market awareness, supply chain constraints, administrative complexity, and project coordination challenges. Nevertheless, as future prospects are positive overall, the research provides recommendations towards targeted actions for policy-makers, industry professionals, and other stakeholders. This can encourage supply chain development and accelerate certification uptake, thereby supporting national sustainability objectives and the implementation of European climate and energy policies. Full article

Supply chains increasingly face complex disruptions and rapid technological change, making the relationship between digital transformation (DT) and supply chain resilience (SCR) an important issue in supply chain management. Yet existing evidence remains fragmented across forms of DT, dimensions of SCR, contextual conditions, and performance outcomes. This study conducts a systematic review and meta-analysis of 200 studies, including 124 examining the DT-SCR relationship and 104 examining the SCR–performance relationship. The results show that DT is positively associated with SCR, with stronger associations for digital capabilities than digital technologies and for reactive than proactive SCR. The DT-SCR association is also stronger among SMEs and mature firms, in manufacturing sectors and developing countries, and in contexts characterized by high power distance, low individualism, and stronger long-term orientation, while uncertainty avoidance shows no significant moderating effect. SCR is positively associated with firm performance, with stronger associations for operational and market performance than financial performance. By integrating evidence on the antecedents, boundary conditions, and performance consequences of SCR, this study provides a more differentiated and context-sensitive understanding of the role of DT in supply chain resilience. Full article

Digital Twins are increasingly viewed as key enablers of real-time decision-making in supply chain management and logistics, yet the literature remains fragmented across application domains, decision problems, methodological approaches, and technological implementations. This systematic review examines how Digital Twins support real-time decision-making, understood not as a fixed response time threshold but as the temporal alignment between data refresh, decision generation, and system evolution. Following the PRISMA 2020 framework, a Scopus search conducted on 16 March 2026 identified studies in which Digital Twins were a central component, incorporated dynamically updated data, supported real-time or near-real-time decision-making, and addressed supply chain management or logistics problems. A total of 57 peer-reviewed studies were retained and synthesized using descriptive analysis, cross-tabulation, and thematic coding across decision problems, application contexts, solution methods, enabling technologies, implementation challenges, and future research directions. The findings show that real-time Digital Twin applications are concentrated mainly in production scheduling and planning, followed by routing and dispatching, resource allocation, disruption management, and inventory management. Methodologically, most studies adopt hybrid approaches combining simulation, optimization, and/or machine learning, reflecting the complexity of real-time operational decision-making. Enabling technologies were grouped into six functional layers, with data acquisition technologies receiving the greatest attention, while higher-level integration and enterprise system layers remain less developed. Reported challenges cluster around data, methodological, and systemic issues. The review concludes that real-time Digital Twins are emerging as integrated decision environments, but further research is needed on computational efficiency, interoperability, validation, and application in underexplored logistics domains. Full article