Search Results (59)

Objective: This article presents a step-by-step digital technique for fabricating a 3D-printed surgical guide to assist in alveoloplasty for immediate denture placement. Methods: The workflow integrates intraoral scanning, virtual tooth extraction, digital soft tissue modeling, and additive manufacturing to produce a customized guide with an occlusal window and buccal slot, along with a verification stent. Results: This method ensures precise ridge recontouring and verification, enhancing surgical predictability and prosthetic fit. Conclusions: Unlike traditional surgical guides based on conventional casts or manual fabrication, this fully digital approach offers a practical and replicable protocol that bridges digital planning and clinical execution. By improving surgical precision, reducing operative time, and ensuring optimal denture fit, this technique represents a significant advancement in guided pre-prosthetic surgery. Full article

Grass-covered levees commonly protect river and estuarine areas against flooding. Climate-induced water level changes may increasingly expose these levees to overflow events. This study investigates whether grass-covered levees can withstand such events, and under what conditions failure may occur. Between 2020 and 2022, full-scale overflow tests were conducted at the Living Lab Hedwige-Prosperpolder along the Dutch–Belgian Scheldt Estuary to assess erosion resistance under varying hydraulic conditions and vegetation states. A custom-built overflow generator was used, with instrumentation capturing flow velocity, water levels, and erosion progression. The results show that well-maintained levees with intact grass cover endured overflow durations up to 30 h despite high terminal flow velocities (4.9–7.7 m/s), without structural damage. In contrast, levee sections with pre-existing surface anomalies, such as animal burrows, slope irregularities, surface damage, or reed-covered soft soils, failed rapidly, often within one to two hours. Animal burrows facilitated subsurface flow and internal erosion, initiating fast, retrograde failure. These findings highlight the importance of preventive maintenance, particularly the timely detection and repair of anomalies. Once slope failure begins, the process unfolds rapidly, leaving no practical window for intervention. Full article

This paper proposes a novel navigation approach for Unmanned Tracked Vehicles (UTVs) using prior-based ensemble deep reinforcement learning, which fuses the policy of the ensemble Deep Reinforcement Learning (DRL) and Dynamic Window Approach (DWA) to enhance both exploration efficiency and deployment safety in unstructured off-road environments. First, by integrating kinematic analysis, we introduce a novel state and an action space that account for rugged terrain features and track–ground interactions. Local elevation information and vehicle pose changes over consecutive time steps are used as inputs to the DRL model, enabling the UTVs to implicitly learn policies for safe navigation in complex terrains while minimizing the impact of slipping disturbances. Then, we introduce an ensemble Soft Actor–Critic (SAC) learning framework, which introduces the DWA as a behavioral prior, referred to as the SAC-based Hybrid Policy (SAC-HP). Ensemble SAC uses multiple policy networks to effectively reduce the variance of DRL outputs. We combine the DRL actions with the DWA method by reconstructing the hybrid Gaussian distribution of both. Experimental results indicate that the proposed SAC-HP converges faster than traditional SAC models, which enables efficient large-scale navigation tasks. Additionally, a penalty term in the reward function about energy optimization is proposed to reduce velocity oscillations, ensuring fast convergence and smooth robot movement. Scenarios with obstacles and rugged terrain have been considered to prove the SAC-HP’s efficiency, robustness, and smoothness when compared with the state of the art. Full article

This study investigates a green intermodal routing problem considering carbon tax regulation and a mixed (combined soft and hard) time window to improve cost- and time-effectiveness and promote carbon emission reduction in intermodal transportation. To enhance the feasibility of problem optimization, we model the uncertainty of both the carbon tax rate and the intermodal network capacity in the routing problem. By using interval fuzzy numbers to formulate the twofold uncertainty, an interval fuzzy linear optimization model is established to address the problem optimization, in which the optimization objective of the model is to minimize the total costs (consisting of transportation, time, and carbon emission costs). Furthermore, we conduct crisp processing of the proposed model to make the problem solvable, in which the optimization level, a parameter whose value is determined by the receiver before solving the problem, is introduced to represent the receiver’s attitude towards the reliability of transportation. We present a numerical experiment to verify the feasibility of the optimization model. The sensitivity analysis shows that the economics and environmental sustainability of the intermodal routing optimization conflict with its reliability. Improving the reliability of transportation increases both the total costs and the carbon emissions of the intermodal route. Furthermore, through comparison with deterministic modeling, the numerical experiment shows that modeling the twofold uncertainty can cover the different decision-making attitudes of the receiver, provide intermodal routes that are sensitive to the optimization level, enable flexible route decision-making, and avoid unreliable transportation. Through comparison with hard and soft time windows, the numerical experiment proves that the mixed time window is more applicable for problem optimization, since it can obtain the intermodal route that yields improved economics and environmental sustainability and simultaneously satisfies the receiver’s requirement for timeliness. Through comparison with the green intermodal route aiming at minimum carbon emissions, the numerical experiment indicates that carbon tax regulation under an interval fuzzy carbon tax rate is not feasible in all decision-making scenarios where the receivers have different attitudes regarding the reliability of transportation. When carbon tax regulation is infeasible, bi-objective optimization can provide Pareto solutions to balance the objectives of reduced costs and lowered carbon emissions. Finally, the numerical experiment reveals the influence of the release time of the transportation order at the origin and the stability of the interval fuzzy capacity on the routing optimization in the scenario in which the receiver prefers highly reliable transportation. Full article

This study explores sustainable foamed polyester materials derived from natural or bio-based building blocks, including succinic, glutaric, and adipic acids, combined with trimethylolpropane and pentaerythritol. By precisely tuning the ratio of functional groups, the resulting polymers contain minimal free functionalities, leading to lower hygroscopicity and enhanced stability. The reaction is monitored by tracking the mass loss associated with water formation, the primary condensation by-product, which reveals a first-order kinetic behaviour. Infrared spectroscopy indicates that foaming occurs in a narrow time window, while esterification begins earlier and continues afterwards. Thermogravimetric analysis confirms thermal stability up to ~400 °C, with complete decomposition at 500 °C and no residue. Scanning electron microscopy images of test specimens with varying densities reveal dense, microporosity-free cell walls in both materials, indicating a homogeneous polymer matrix that contributes to the overall stabilisation of the foam structure. In flammability tests, the foams resist ignition during two 10 s methane flame exposures and, under prolonged flame, burn 40 times more slowly than conventional foams. These results demonstrate a modular system for creating bio-based foams with tunable properties—from soft and elastic to rigid—suitable for diverse applications. The materials offer a sustainable alternative to petrochemical foams while retaining excellent mechanical and thermal properties. Full article

Container intermodal scheduling is critical for advancing low-carbon logistics within inland port systems. However, the scheduling process faces several challenges, including the complexity of coordinating transport modes and complying with carbon emission policies. To address these issues, this study proposes a multi-objective optimization model that simultaneously considers transportation cost, carbon emissions, and time efficiency under soft time window constraints. The model is solved using an improved grey wolf–Harris hawks hybrid algorithm (IGWOHHO). This algorithm enhances population diversity through Tent chaotic mapping, balances global exploration and local exploitation with adaptive weight adjustment, and improves solution quality by incorporating an elite retention strategy. Benchmark tests show that IGWOHHO outperforms several well-established metaheuristic algorithms in terms of convergence accuracy and robustness. A case study based on an intermodal transport network further demonstrates that adjusting the objective weights flexibly provides decision support under various scenarios, achieving a dynamic balance between cost, efficiency, and environmental impact. Additionally, the analysis reveals that appropriate carbon tax pricing can encourage the adoption of greener transport modes, promoting the sustainable development of multimodal logistics systems. Full article

The task allocation of autonomous underwater vehicles (AUVs) is a crucial aspect of ocean exploration and mission execution tasks. In a mine countermeasures (MCM) combat scenario, when a new suspicious mission point is detected in the mission area, the heterogeneous multi-AUV system requires reallocation in real time. To address this, a soft time windows consensus-based bundle algorithm with partial reallocation (SWCBBA-PR) is designed. Based on the consensus-based bundle algorithm (CBBA), this algorithm comprehensively considers the underwater communication limitations and introduces the soft time window mechanism and partial reallocation mechanism. Its aim is to solve the partial reallocation problem that arises when new task points appear under the temporal-coupling constraints of complex underwater tasks. The SWCBBA-PR algorithm has been validated through simulation, demonstrating its ability to generate an optimal allocation scheme in the scenario of MCM mission emergence, and it exhibits good convergence performance. Full article

Emergency logistics is essential for rapid and efficient disaster response, ensuring the timely availability and deployment of resources to affected areas. In the process of rescue work, the dynamic changes in rescue point information greatly increase the difficulty of rescue. This paper establishes a combined neural network model considering soft time-window penalty and applies deep reinforcement learning (DRL) to address the dynamic routing problem in emergency logistics. This method utilizes the actor–critic framework, combined with attention mechanisms, pointer networks, and long short-term memory neural networks, to determine effective disaster relief path, and it compares the obtained scheduling scheme with the results obtained from the DRL algorithm based on the single-network model and ant colony optimization (ACO) algorithm. Simulation experiments show that the proposed method reduces the solution accuracy by nearly 10% compared to the ACO algorithm, but it saves nearly 80% in solution time. Additionally, it slightly increases solution times but improves accuracy by nearly 20% over traditional DRL approaches, demonstrating a promising balance between performance efficiency and computational resource utilization in emergency logistics. Full article

Biomass monitoring of mushroom liquid strains during the fermentation process demands real-time analysis with minimal manual intervention, highlighting the urgent need for intelligent surveillance. This study introduced a soft sensor method based on edge computing machine vision, termed Edge CV, for in situ non-invasive estimation of biomass. In our experiment, the hardware of the Edge CV system includes the Jetson Nano with 4 GB RAM, 64 GB ROM, and a 128-core Maxwell GPU for executing intelligent machine vision tasks, along with embedded cameras for image data acquisition. Furthermore, a cascaded machine vision model was developed to enable biomass evaluation on the Edge CV system. The cascaded machine vision model mainly consists of three steps: first, the object detection task to locate the observation window, achieving a mean Average Precision (mAP50:95) of 82.3% with 78.7 GFLOPs; then, the segmentation task to extract liquid strain data within the observation window, yielding a mean intersection over union (MIoU) of 85.9% with 110.4 GFLOPs; and finally, calculating mycelium biomass indices via the morphological image processing task. The correlation between Edge CV inference and manual measurement showed an R² of 0.963 and an RMSE of 0.027 for normalized biomass indices, demonstrating a robust and consistent trend. Therefore, this study illustrates the practical application of edge computing-based machine vision for biomass soft sensing during the fermentation process. Full article

In the distribution field, distribution costs and customer service satisfaction are extremely important issues for enterprises. However, both the Vehicle Routing Problem with Soft Time Windows (VRPSTW) and the Vehicle Routing Problem with Fuzzy Time Windows (VRPFTW) have certain deficiencies in describing real-world scenarios. Therefore, this paper considers both soft time windows and fuzzy time windows, improving upon the traditional VRPSTW and VRPFTW models to establish a more comprehensive and realistic model called the Vehicle Routing Problem with Soft Time Windows and Fuzzy Time Windows (VRPSFTW). Secondly, to solve the relevant problems, this paper proposes a Directed Mutation Genetic Algorithm integrated with Large Neighborhood Search (LDGA), which fully utilizes the advantages of the Genetic Algorithm (GA) in the early stages and appropriately adopts removal and re-insertion operators from the Large Neighborhood Search (LNS). This approach not only makes efficient use of computational resources but also compensates for the weaknesses of crossover and mutation operators in the later stages of the genetic algorithm. Thereby, it improves the overall efficiency and accuracy of the algorithm and achieves better solution results. In addition, in order to solve multi-objective problems, this paper employs a two-stage solution approach and designs two sets of algorithms based on the principles of “cost priority” and “service-level priority”. Simulation experiments demonstrated that the algorithms designed in this study achieved a more competitive solving performance. Full article

As the logistics industry modernizes, living standards improve, and consumption patterns shift, the demand for fresh food continues to grow, making cold chain logistics for perishable goods a critical component in ensuring food quality and safety. However, the presence of both soft and hard time windows among demand nodes can complicate the single-network distribution of perishable goods. In response to these challenges, this paper proposes an optimization model for multi-distribution center perishable goods delivery, considering both one-echelon and two-echelon network joint distributions. The model aims to minimize total costs, including transportation, fixed, refrigeration, goods damage, and penalty costs, while measuring customer satisfaction by the start time of service at each demand node. A two-stage heuristic algorithm is designed to solve the model. In the first stage, an initial solution is constructed using a greedy approach based on the principles of the k-medoids clustering algorithm, which considers both spatial and temporal distances. In the second stage, the initial routing solution is optimized using a linear programming approach from the Ortools solver combined with an Improved Adaptive Large Neighborhood Search (IALNS) algorithm. The effectiveness of the proposed model and algorithm is validated through a case study analysis. The results demonstrate that the initial solutions obtained through the k-medoids clustering algorithm based on spatio-temporal distance improved the overall cost optimization by 1.85% and 4.74% compared to the other two algorithms. Among the three two-stage heuristic algorithms, the Ortools-IALNS proposed here showed enhancements in the overall cost optimization over the IALNS, with improvements of 3.24%, 1.12%, and 0.41%, respectively. The two-stage heuristic algorithm designed in this study also converged faster than the other two heuristic algorithms, with overall optimization improvements of 1.55% and 1.28%, further validating the superior performance of the proposed heuristic algorithm. Full article

In recent years, parcel volumes have reached record highs, prompting the logistics industry to explore innovative solutions to meet growing demand. In densely populated areas, delivery robots offer a promising alternative to traditional truck-based delivery systems. These autonomous electric robots operate on sidewalks and deliver time-sensitive goods, such as express parcels, medicine and meals. However, their limited cargo capacity and battery life require a return to a depot after each delivery. This challenge can be modeled as an electric vehicle-routing problem with soft time windows and single-unit capacity constraints. The objective is to serve all customers while minimizing the quadratic sum of delivery delays and ensuring each vehicle operates within its battery limitations. To address this problem, we propose a mixed-integer quadratic programming model and introduce an enhanced formulation using a layered graph structure. For this layered graph, we present two solution approaches based on relaxations that reduce the number of nodes and arcs compared to the expanded formulation. The first approach, Iterative Refinement, solves the current relaxation to optimality and refines the graph when the solution is infeasible for the expanded formulation. This process continues until a proven optimal solution is obtained. The second approach, Branch and Refine, integrates graph refinement into a branch-and-bound framework, eliminating the need for restarts. Computational experiments on modified Solomon instances demonstrate the effectiveness of our solution approaches, with Branch and Refine consistently outperforming Iterative Refinement across all tested parameter configurations. Full article

The rapid proliferation of Internet of Things (IoT) ground devices (GDs) has created an unprecedented demand for computing resources and real-time data-processing capabilities. Integrating unmanned aerial vehicles (UAVs) into Mobile Edge Computing (MEC) emerges as a promising solution to bring computation and storage closer to the data sources. However, UAV heterogeneity and the time window constraints for task execution pose a significant challenge. This paper addresses the multiple heterogeneity UAV routing problem in MEC environments, modeling it as a multi-traveling salesman problem (MTSP) with soft time constraints. We propose a two-stage heuristic algorithm, heterogeneous multiple UAV routing (HMUR). The approach first identifies task areas (TAs) and optimal hovering positions for the UAVs and defines an effective fitness measurement to handle UAV heterogeneity. A novel scoring function further refines the path determination, prioritizing real-time task compliance to enhance Quality of Service (QoS). The simulation results demonstrate that our proposed HMUR method surpasses the existing baseline algorithms on multiple metrics, validating its effectiveness in optimizing resource scheduling in MEC environments. Full article

In this study, we extend the research on the multimodal routing problem by considering flexible time window and multi-uncertainty environment. A multi-uncertainty environment includes uncertainty regarding the demand for goods, the travel speed of the transportation mode, and the transfer time between different transportation modes. This environment further results in uncertainty regarding the delivery time of goods at their destination and the earliness and lateness caused by time window violations. This study adopts triangular fuzzy numbers to model the uncertain parameters and the resulting uncertain variables. Then, a fuzzy mixed integer nonlinear programming model is established to formulate the specific problem, including both fuzzy parameters and fuzzy variables. To make the problem easily solvable, this study employs chance-constrained programming and linearization to process the proposed model to obtain an equivalent credibilistic chance-constrained linear programming reformulation with an attainable global optimum solution. A numerical case study based on a commonly used multimodal network structure is presented to demonstrate the feasibility of the proposed method. Compared to hard and soft time windows, the numerical case analysis reveals the advantages of the flexible time window in reducing the total costs, avoiding low reliability regarding timeliness, and providing confidence level-sensitive route schemes to achieve flexible routing decision-making under uncertainty. Furthermore, the numerical case analysis verifies that it is necessary to model the multi-uncertainty environment to satisfy the improved customer requirements for timeliness and enhance the flexibility of the routing, and multimodal transportation is better than unimodal transportation when routing goods in an uncertain environment. The sensitivity analysis in the numerical case study shows the conflicting relationship between the economic objective and the reliability regarding the timeliness of the routing, and the result provides a reference for the customer to find a balance between them. Full article

Environmental concerns have called for several measures to be taken within the logistics and transportation fields. Among these measures is the adoption of electric vehicles instead of diesel-operated vehicles for personal and commercial-delivery use. The optimized routing of electric vehicles for the commercial delivery of products is the focus of this paper. We study the effect of several practical challenges that are faced when routing electric vehicles. Electric vehicle routing faces the additional challenge of the potential need for recharging while en route, leading to more travel time, and hence cost. Therefore, in this work, we address the issue of electric vehicle routing problem, allowing for partial recharging while en route. In addition, the practical mandate of the time windows set by customers is also considered, where electric vehicle routing problems with soft time windows are studied. Real-life experience shows that the delivery of customers’ demands might be uncertain. In addition, real-time traffic conditions are usually uncertain due to congestion. Therefore, in this work, uncertainties in customers’ demands and traffic conditions are modeled and solved using fuzzy methods. The problems of fuzzy real-time, fuzzy demand, and electric vehicle routing problems with soft time windows are addressed. A mixed-integer programming mathematical model to represent the problem is developed. A novel two-phase solution approach is proposed to solve the problem. In phase I, the classical genetic algorithm (GA) is utilized to obtain an optimum/near-optimum solution for the fuzzy demand electric vehicle routing problem with soft time windows (FD-EVRPSTW). In phase II, a novel fuzzy real-time-adaptive optimizer (FRTAO) is developed to overcome the challenges of recharging and real-time traffic conditions facing FD-EVRPSTW. The proposed solution approach is tested on several modified benchmark instances, and the results show the significance of recharging and congestion challenges for routing costs. In addition, the results show the efficiency of the proposed two-phase approach in overcoming the challenges and reducing the total costs. Full article