Search Results (410)

Wood supply chains are complex, involving many stakeholders, intricate processes, and logistical challenges to ensure the timely and accurate delivery of wood products to customers. Weather-related variations in forest road accessibility further complicate operations. This paper explores the challenges faced by forest managers in targeting many delivery requirements—four or more. To address this, simulation-based optimization, using NSGA-III, a many-objective optimization algorithm, is proposed to simultaneously optimize often conflicting objectives primarily by minimizing delivery lead time, delivery deviations in backlogs, and delivery variation. NSGA-III enables the exploration of a diverse set of Pareto-optimal solutions that show trade-offs across a flexible set of four, or more, delivery objectives. A Discrete Event Simulation model is integrated to evaluate objectives in a complex wood supply chain. The implementation of NSGA-III within the framework allows forestry decision-makers to navigate between different harvest schedules and evaluate how they target a set of preference-based delivery objectives. The simulation can also provide detailed insights into how a specific harvest schedule affects the supply chain when post-processing possible solutions, facilitating decision making. This study shows that NSGA-III could substitute NSGA-II to optimize the wood supply chain for more than three objective functions. Full article

Universal accessibility remains a critical challenge for effective public transport and urban equity. This study addresses the need for operational prioritisation tools by proposing a robust hybrid methodology to rank interventions at urban mobility nodes. The approach combines the Analytic Hierarchy Process (AHP) for integrating expert and participatory criteria weighting with four Multi-Criteria Decision-Making (MCDM) techniques (TOPSIS, VIKOR, COPRAS, and ARAS) to ensure solution reliability. Empirical validation, conducted on 30 bus stops in Segovia, Spain, confirmed the methodological soundness, evidenced by near-perfect correlations (ρ = 0.99) among the compromise and additive ratio models (TOPSIS–VIKOR and COPRAS–ARAS) and stability across over 85% of sensitivity simulations. The findings validate that the methodology effectively guides resource allocation towards interventions yielding maximum social impact and demonstrate its transferability to complex urban supply chain contexts, such as logistics microhubs. Ultimately, this replicable and adaptable model supports the transition towards more equitable, resilient urban systems, aligning directly with Sustainable Development Goal 11 (Sustainable Cities and Communities). Full article

Although conventional lectures can provide a wide range of information to a large group of people, maintaining attention and ensuring knowledge transfer can be a challenge. Therefore, it is important to look for new, engaging, and effective approaches. This pilot feasibility study explores the effectiveness of virtual reality (VR) in increasing student engagement and knowledge transfer during lectures in the field of supply chain logistics and inventory selection systems. An educational VR game was developed through the systematic design of application logic, the creation of 3D assets, the construction of virtual scenes, and the implementation of gameplay. The application simulates three inventory picking methods: conventional selection, Pick by Light, and Pick by Vision systems. A total of 22 master’s students participated in the pilot study. They tested three different versions of the VR game, compared the time they needed to complete it, and participated in a guided discussion and questionnaire. The preliminary student reports indicated that students felt more engaged in the learning process and reported a perceived higher engagement with inventory picking systems compared to the traditional lecture format. On the other hand, participants mentioned concerns about nausea and the unavailability of VR headsets. The pilot results indicate that VR shows potential as an educational tool for teaching industrial logistics because it transforms the typical classroom environment into a more active and playful one, leading to a more natural understanding of the subject. Full article

The single-cell spatial transcriptomics (ST) data with cell type and spatial location, i.e., $(C,x,y)$ with C as cell type and $(x,y)$ as its spatial location, produced by recent biotechnologies, such as CosMx and Xenium, contain a huge amount of information about cancer tissue samples, thus have great potential for cancer research via detection of ST-Community which is defined as a collection of cells with distinct cell-type composition and similar neighboring patterns based on nearby cell-percentages. But for huge CosMx single-cell ST data, the existing clustering methods do not work well for st-community detection, and the commonly used kNN compositional data method shows lack of informative neighboring cell patterns. In this article, we propose a novel and more informative disk compositional data (DCD) method for single-cell ST data, which identifies neighboring patterns of each cell via taking into account of ST data features from recent new technologies. After initial processing single-cell ST data into the DCD matrix, an innovative DCD-TMHC computation method for st-community detection is proposed here. Extensive simulation studies and the analysis of CosMx breast cancer data, which is an example of single-cell ST dataset, clearly show that our proposed DCD-TMHC computation method is superior to other existing methods. Based on the st-communities detected for CosMx breast cancer data, the logistic regression analysis results demonstrate that the proposed DCD-TMHC computation method produces better interpretable and superior outcomes, especially in terms of assessment for different cancer categories. These suggest that our proposed novel and informative DCD-TMHC computation method here will be helpful and have an impact on future cancer research based on single-cell ST data, which can improve cancer diagnosis and monitor cancer treatment progress. Full article

Hierarchical Bayesian models based on Gaussian processes are considered useful for describing complex nonlinear statistical dependencies among variables in real-world data. However, effective Monte Carlo algorithms for inference with these models have not yet been established, except for several simple cases. In this study, we show that, compared with the slow inference achieved with existing program libraries, the performance of Riemannian manifold Hamiltonian Monte Carlo (RMHMC) can be drastically improved by applying the chain rule for the differentiation of the Hamiltonian in the optimal order determined by the model structure, and by dynamically programming the eigendecomposition of the Riemannian metric with the recursive update of the eigenvectors at the previous move. This improvement cannot be achieved when using a naive automatic differentiator included in commonly used libraries. We numerically demonstrate that RMHMC effectively samples from the posterior, allowing the calculation of model evidence, in a Bayesian logistic regression on simulated data and in the estimation of propensity functions for the American national medical expenditure data using several Bayesian multiple-kernel models. These results lay a foundation for implementing effective Monte Carlo algorithms for analysing real-world data with Gaussian processes, and highlight the need to develop a customisable library set that allows users to incorporate dynamically programmed objects and to finely optimise the mode of automatic differentiation depending on the model structure. Full article

Background/Objectives: Endovascular aneurysm repair (EVAR) is the standard treatment for abdominal aortic aneurysms, but the risk of endoleak compromises its effectiveness. Type IA endoleak, stemming from an inadequate proximal seal, is the most critical complication associated with the highest risk of rupture. Current preoperative planning relies on static anatomical measurements from computed tomography angiography that fail to predict seal failure due to dynamic biomechanical forces. This study aimed to retrospectively validate the predictive accuracy of a novel physics-informed digital twin and artificial intelligence (AI) model for predicting type IA endoleak risk compared to conventional static planning methods. Methods: This was a retrospective, single-center proof-of-concept validation study involving 15 patients who underwent elective EVAR (5 with confirmed type IA endoleak and 10 without type IA endoleak). A patient-specific digital twin was created for each case to simulate stent-graft deployment and capture the dynamic biomechanical interaction with the aortic wall. A logistic regression AI model processed over 16,000 biomechanical measurements to generate a single, objective metric of the endoleak risk index (ERI). The predictive performance of the ERI (using a cutoff of 0.80) was assessed and compared against a 1:3 propensity score-matched conventional control group (n = 45) who received traditional anatomical-based planning. Results: The mean ERI was significantly higher in the endoleak-positive group (0.85 ± 0.10) compared to the endoleak-negative group (0.39 ± 0.11) (p = 0.011). The digital twin/AI model demonstrated superior predictive capability, achieving an overall accuracy of 80% (95% CI: 51.9–95.7) and an area under the curve (AUC) of 0.85 (95% CI: 0.58–0.99). Crucially, the model achieved a sensitivity of 100% and a negative predictive value (NPV) of 100%, correctly identifying all high-risk cases and ruling out endoleak in all low-risk cases. In stark contrast, the matched conventional planning group achieved an overall accuracy of only 51.1% and an AUC of 0.54. Conclusion: This physics-informed digital twin and AI framework successfully validated its capability to accurately and objectively predict the risk of type IA endoleak following EVAR. The derived ERI offers a significant quantitative advantage over traditional static anatomical measurements, establishing it as a highly reliable safety tool (100% NPV) for ruling out endoleak risk. This technology represents a critical advancement toward personalized EVAR planning, enabling surgeons to proactively identify high-risk anatomies and adjust treatment strategies to minimize post-procedural complications. Further large-scale, multicenter prospective trials are necessary to confirm these findings and support clinical adoption. Full article

As ports evolve to meet sustainability targets, seamless coordination between road and rail operations becomes fundamental to success. This study addresses the Train Loading Planning Problem (TLPP) which focuses on assigning outbound containers to train wagons under slot, weight, and pattern constraints aiming to examine its broader systemic implications. A compact mixed-integer programming formulation is developed and enhanced through a column-generation approach that efficiently prices feasible wagon plans. The optimization module is embedded within a discrete-event simulation of terminal processes including yard handling, gate operations, and train timetables. The study tests a TLPP-based rail planning algorithm within a DES of terminal and hinterland operations to quantify the impact under realistic variability. Using operational data from the Port of Valencia, realistic planning scenarios are evaluated across varying demand mixes and train frequencies. Results indicate that integrating rail capacity with optimized wagon loading reduces set-up time by 20%, delivery lead time by 54%, container dwell time by 80%, and greenhouse gas emissions by 54% compared with a trucking forwarding baseline, while maintaining throughput and alleviating congestion at terminal gates and yards. From a computational perspective, the column-generation approach achieves improved runtimes to the compact MIP and scales linearly to the number of variables. The proposed framework delivers ready to use load plans and practical insights for the deployment of additional rail capacity, supporting sustainable logistics in port environments. Full article

Conventional statistical process control (SPC) charting, an efficient monitoring and diagnosis scheme, is under development in several fields of healthcare monitoring. Investigation of clinical binary outcomes using risk-adjusted (RA) control charts is an important subject in this area. Different researchers have extended the monitoring of the binary outcomes of cardiac surgeries by fitting a logistic model for a patient’s death probability against the patient’s risk. As a result, different RA-based cumulative sum (CUSUM) charts have been proposed for monitoring a patient’s 30-day mortality in several studies. Here, a novel run rules method is introduced in conjunction with the RA CUSUM control chart. The suggested approach was tested and benchmarked through simulation studies based on the average run length (ARL) metric. The outcomes showed favourable results, and further analysis under beta-distributed conditions confirmed its robustness. A worked example was presented to illustrate its implementation. Full article

This study applies integrated LCA–LCC to 1 L of bottled beer at a representative small Japanese brewery using 2024 operational data. Following ISO 14040/44, the cradle-to-gate boundary covers raw materials (excluding agricultural cultivation while including transport and preprocessing), brewing, packaging, and thermal sterilization. The baseline global warming impact is 0.52 kg CO₂e/L and the cost is JPY 487/L, with single-use glass and labor identified as dominant hotspots. As beer is produced from malt, hops, yeast, and water, this study focuses on how alternative production strategies mitigate sustainability hotspots within this process. Three alternative production scenarios were evaluated within this integrated LCA–LCC model. Scenario 1 (local rice substitution) replaces 30% of the fermentable extract from imported malt with domestically grown rice, changing only ingredient transport and preprocessing within the truncated cradle-to-gate boundary (crop cultivation remains excluded), and yields 0.55 kg CO₂e/L and JPY 492/L, i.e., a slightly higher global warming impact and cost than the baseline. Scenario 2 (direct sales expansion) assumes that 50% of the beer is sold on site via draft, thereby reducing single-use glass bottles and fuel for pasteurization and achieving 0.29 kg CO₂e/L (−44%) and JPY 435/L (−11%) in the deterministic model, the best combined environmental and economic performance among the modeled options. Scenario 3 (joint logistics) models cooperative brewing and shared distribution, which improve labor efficiency and modestly reduce transport intensity, delivering 399 JPY/L in the deterministic model; however, Monte Carlo analysis yields a higher expected cost and indicates that these cost savings are not robust. One-way sensitivity analysis identified packaging and labor as the dominant drivers of both environmental and economic performance, while Monte Carlo simulation confirmed the relative insignificance of electricity-related parameters and reinforced the comparative robustness of Scenario 2. Together, these results highlight the most effective leverage points for a sustainable transition in Japan’s craft beer sector, offering the greatest leverage for a more sustainable transition in Japan’s craft brewing sector. Full article

In highly dynamic and information-intensive logistics environments, understanding how firms achieve modeling and simulation ambidextrous innovation (MSAI) through strategic alignment is crucial. Drawing on organizational information processing theory (OIPT), we develop an integrative framework that links strategic congruence with capability development and innovation outcomes. The study examines (1) whether buffering–bridging congruence (B–B congruence) exists and how it enhances MSAI through operational stability, financial flexibility, and knowledge management capability; (2) how these three capabilities shape the differentiated pathways toward exploitative and explorative simulation innovation; and (3) how firms may leverage a simulation-driven decision framework to achieve strategic–capability alignment in the highly dynamic maritime logistics environment. The framework is empirically tested using polynomial regression models based on survey data from Chinese maritime logistics firms, analyzed with SPSS 27.0 and STATA 15. Our empirical results indicate that, regardless of the level of buffering strategy or bridging strategy, the firm’s operational stability, financial flexibility, and knowledge management capabilities are always higher when buffering and bridging strategy are congruent. The results also show that the three capabilities influence MSAI differently. Specifically, knowledge management capability exerts positive effects on both exploitative and exploratory modeling innovation. Financial flexibility mainly promotes exploitative innovation, while its influence on exploratory innovation is not significant. In contrast, operational stability does not enhance exploitative innovation but unexpectedly shows a positive effect on exploratory innovation. The findings advance OIPT’s theoretical application in simulation-intensive settings and offer guidance for firms seeking to align capabilities and strategy in complex systems, providing both theoretical and practical insights. Full article

This paper investigates the motion planning problem for symmetric UAV cooperative lifting in confined spaces. A dynamic model of the symmetric UAV cooperative lifting system is established, and differential flatness analysis is employed to transform nonlinear dynamics into constraints on flat outputs, thereby simplifying the motion planning process. The planning framework consists of two levels: path planning and trajectory planning. For path planning, a reinforcement learning-based bidirectional RRT (RLDB-BiRRT) method is proposed, which integrates the random tree expansion mechanism with the DDPG algorithm to achieve adaptive directional bias. This approach effectively mitigates the issues of low search efficiency and excessive redundant nodes inherent in traditional RRT algorithms. For trajectory planning, an adaptive safe flight corridor (SFC) construction method is introduced, combining symmetric ellipsoids and convex polyhedra to generate high-quality linear constraints. Building upon the proposed motion planning method and leveraging differential flatness analysis, a unified planning framework is developed that seamlessly integrates the reinforcement learning-enhanced path planning with adaptive safe corridor construction and differential-flatness-based trajectory optimization, specifically designed for symmetric UAV cooperative lifting tasks in confined spaces. This integrated approach enhances corridor space utilization and ensures trajectory continuity. Simulation experiments validate the effectiveness of the proposed methods, demonstrating their capability to generate dynamically feasible, smooth, and safe transportation trajectories in confined environments, while effectively constraining load swing and UAV attitude angles. This study provides theoretical foundations and practical references for the application of symmetric UAV cooperative lifting in low-altitude logistics and emergency transportation scenarios. Full article

Background: Kazakhstan’s transport system plays a key role in Eurasian logistics due to its position along the Middle Corridor. However, multimodal freight transport remains under-optimized due to infrastructure bottlenecks, uneven cargo flows, and limited digital tools for forecasting and planning. Methods: This study presents the development of an agent-based simulation model for analyzing multimodal transportation in Kazakhstan. The model integrates railway, road, and maritime components, simulating cargo flows across export, import, and transit scenarios. Key agents include orders, transport vehicles, logistics hubs, and border checkpoints. The model is implemented in AnyLogic 8.9 and calibrated using a mix of official statistics, industry data, and field estimates. Results: The simulation replicates key logistics processes, identifies congestion points, and evaluates delivery performance under different scenarios. Experiments demonstrate how bottlenecks at terminals and border crossings affect delivery times, vehicle utilization, and hub load. The model allows testing infrastructure development options and scheduling policies. Conclusions: The approach enables a dynamic assessment of logistics efficiency under uncertainty and can support decision-making in transport planning. The novelty lies in the integrated simulation of multimodal freight flows with infrastructure constraints. The model serves as a foundation for digital twin applications and scenario-based planning. Full article

Background: Jute, recognized as the ‘golden fiber’ of Bangladesh, produces a substantial amount of stick left over (waste), a byproduct of the fiber. Usually, unused jute sticks (JS) are thrown away or burned, since they are treated as landfill or unusable waste. Noteworthy research gaps exist in the farming process, infrastructure, [supply chains], unfavorable policies, government interference, and insufficient farmers’ knowledge of the export market. This research examines the potential of jute stick charcoal (JSC) as a sustainable and value-added product within the circular economy framework. Methods: This study employs a system dynamics (SD) modeling approach to examine how various factors, including agricultural output, supply chain process efficiency, trade flows, and relevant variables, influence JSC supply chain performance. Considering technologies, logistics, and policy variables, this study constructed a simulation model with three scenarios: current, worst-case, and improved, using Vensim DSS to identify system behavior under changing conditions. Results: The simulation indicates that optimizing idle jute resources, enhancing supply chain processes, and expanding markets can increase economic returns, reduce waste, and create more rural jobs, particularly for women. Conclusions: Enhanced coordination, technologies, and logistics can reduce carbon emissions, benefit farmers, support rural industries, and contribute to SDGs 8, 12, and 13. Full article

Background: As a labor-, capital-, and technology-intensive sector, shipbuilding supports water transportation, international trade, and marine development, driving economic growth and employment. Yet rising raw material/labor costs now bottleneck enterprise performance, making cost reduction and efficiency improvement urgent for shipbuilding and repair firms. It is an effective way to improve logistics transportation efficiency for reducing the cost of shipbuilding and repair firms. However, there are still few methods specifically designed for logistics transportation scheduling in shipbuilding and repair firms. Methods: In this paper, a “dual-cycle” strategy is proposed to optimize material transportation and cut logistics vehicles’ empty-load rate in the shipbuilding and repair process. A mixed-integer programming model is built to minimize total empty travel time, considering task priorities and time windows. A genetic algorithm-based scheduling method is proposed to solve this complex scheduling model. Results: Simulation with real shipyard logistics data shows the proposed model and algorithm can effectively address the shipbuilding logistics vehicle scheduling problem. In addition, the proposed algorithm performs better than two other compared algorithms in handling the studied problem. Conclusions: This study aids shipbuilding and repair logistics managers in making scheduling plans and determining optimal vehicle numbers, supporting cost-efficiency improvement. Full article

To address the challenges of low efficiency, instability, and difficulties in meeting multiple constraints simultaneously in multi-AGV (Automated Guided Vehicle) task scheduling for intelligent manufacturing and logistics, this paper introduces a scheduling method based on multi-feature constraints and an improved deep reinforcement learning (DRL) approach (Improved Proximal Policy Optimization, IPPO). The method integrates multiple constraints, including minimizing task completion time, reducing penalty levels, and minimizing scheduling time deviation, into the scheduling optimization process. Building on the conventional PPO algorithm, several enhancements are introduced: a dynamic penalty mechanism is implemented to adaptively adjust constraint weights, a structured reward function is designed to boost learning efficiency, and sampling bias correction is combined with global state awareness to improve training stability and global coordination. Simulation experiments demonstrate that, after 10,000 iterations, the minimum task completion time drops from 98.2 s to 30 s, the penalty level decreases from 130 to 82, and scheduling time deviation reduces from 12 s to 0.5 s, representing improvements of 69.4%, 37%, and 95.8% in the same scenario, respectively. Compared to genetic algorithms (GAs) and rule-based scheduling methods, the IPPO approach demonstrates significant advantages in average task completion time, total system makespan, and overall throughput, along with faster convergence and better stability. These findings demonstrate that the proposed methodology enables effective multi-objective collaborative optimization and efficient task scheduling within complex dynamic environments, holding significant value for intelligent manufacturing and logistics systems. Full article