Search Results (10)

Cold chain logistics is crucial in ensuring food quality and safety in modern supply chains. The required temperature control systems increase operational costs and environmental impacts compared to conventional logistics. To reduce these costs while maintaining service quality in real-world distribution scenarios, efficient route planning is essential, particularly when products with different temperature requirements need to be delivered together using multi-compartment refrigerated vehicles. This substantially increases the complexity of the routing process. We propose a novel deep reinforcement learning approach that incorporates a vehicle state encoder for capturing fleet characteristics and a dynamic vehicle state update mechanism for enabling real-time vehicle state updates during route planning. Extensive experiments on a real-world road network show that our proposed method significantly outperforms four representative methods. Compared to a recent ant colony optimization algorithm, it achieves up to a 6.32% reduction in costs while being up to 1637 times faster in computation. Full article

Precise spatial localization of broadleaf species is crucial for efficient forest management and ecological studies. This study presents an advanced approach for segmenting and classifying broadleaf tree species, including Japanese oak (Quercus crispula), in mixed forests using multi-spectral imagery captured by unmanned aerial vehicles (UAVs) and deep learning. High-resolution UAV images, including RGB and NIR bands, were collected from two study sites in Hokkaido, Japan: Sub-compartment 97g in the eastern region and Sub-compartment 68E in the central region. A Mask Region-based Convolutional Neural Network (Mask R-CNN) framework was employed to recognize and classify single tree crowns based on annotated training data. The workflow incorporated UAV-derived imagery and crown annotations, supporting reliable model development and evaluation. Results showed that combining multi-spectral bands (RGB and NIR) with canopy height model (CHM) data significantly improved classification performance at both study sites. In Sub-compartment 97g, the RGB + NIR + CHM achieved a precision of 0.76, recall of 0.74, and F1-score of 0.75, compared to 0.73, 0.74, and 0.73 using RGB alone; 0.68, 0.70, and 0.66 with RGB + NIR; and 0.63, 0.67, and 0.63 with RGB + CHM. Similarly, at Sub-compartment 68E, the RGB + NIR + CHM attained a precision of 0.81, recall of 0.78, and F1-score of 0.80, outperforming RGB alone (0.79, 0.79, 0.78), RGB + NIR (0.75, 0.74, 0.72), and RGB + CHM (0.76, 0.75, 0.74). These consistent improvements across diverse forest conditions highlight the effectiveness of integrating spectral (RGB and NIR) and structural (CHM) data. These findings underscore the value of integrating UAV multi-spectral imagery with deep learning techniques for reliable, large-scale identification of tree species and forest monitoring. Full article

Over the years, the vehicle routing problem has been studied by several authors, creating several extensions, such as the multi-compartment vehicle routing problem. Several studies in the literature have addressed this problem, but few have solved it through exact approaches owing to model convolution. In this way, a mathematical model is proposed for the multi-compartment vehicle routing problem with time windows, in which three types of fuel products are distributed to a set of customers using a limited homogeneous fleet. The model explicitly considers time windows, as well as regulatory rest times for the drivers and time limits for each trip and for working schedules, addressing a real company’s decision support requirements, which is scarce in the literature. The optimal solution determines, for each vehicle, the distribution route and time to carry out the deliveries with the corresponding loading of products to compartments, complemented by the calculation of carbon emissions. The main objective is to minimize the total distance traveled, which corresponds to the sum of the distances traveled by each one of the allocated vehicles. The results allow the assessment of the solution optimization applied to a set of instances for a Portuguese company to evaluate the performance and compare decision support improvements with current baseline company procedures. Full article

The use of multi-compartment vehicles (MCVs) in urban logistics distribution is increasing. However, urban traffic congestion causes high carbon emissions in the logistics distribution, resulting in unsustainable development in urban transportation. In addition, the application of the mixed carbon policy has gradually become the first choice for energy conservation and emission reduction in some countries and regions. The transportation industry is a major carbon-emitting industry, which needs to be constrained by carbon emission reduction policies. In this context, the research on the multi-compartment vehicle routing problem (MCVRP) considering traffic congestion under the mixed carbon policy is carried out. Firstly, a mathematical model of MCVRP considering traffic congestion under the mixed carbon policy is constructed. Secondly, a two-stage variable neighborhood threshold acceptance algorithm (VNS-TA) is proposed to solve the above mathematical model. Thirdly, 14 adapted standard examples of the MCVRP are used to verify the effectiveness and optimization ability of the two-stage VNS-TA algorithm. A simulation example of the MCVRP considering traffic congestion under the mixed carbon policy is used to conduct sensitivity analyses for different scenarios. Finally, the following conclusions are drawn: (1) the two-stage VNS-TA algorithm is effective and has strong optimization ability in solving the basic MCVRP, and (2) the two-stage VNS-TA algorithm can solve and optimize the MCVRP considering traffic congestion under the mixed carbon policy, which has the effects of cost saving and energy conservation and emission reduction. Full article

The rapid increase in urbanization results in an increase in the volume of municipal solid waste produced every day, causing overflow of the garbage cans and thus distorting the city’s appearance; for this and environmental reasons, smart cities involve the use of modern technologies for intelligent and efficient waste management. Smart bins in urban environments contain sensors that measure the status of containers in real-time and trigger wireless alarms if the container reaches a predetermined threshold, and then communicate the information to the operations center, which then sends vehicles to collect the waste from the selected stations in order to collect a significant waste amount and reduce transportation costs. In this article, we will address the issue of the Dynamic Multi-Compartmental Vehicle Routing Problem (DM-CVRP) for selective and intelligent waste collection. This problem is summarized as a linear mathematical programming model to define optimal dynamic routes to minimize the total cost, which are the transportation costs and the penalty costs caused by exceeding the bin capacity. The hybridized genetic algorithm (GA) is proposed to solve this problem, and the effectiveness of the proposed approach is verified by extensive numerical experiments on instances given by Valorsul, with some modifications to adapt these data to our problem. Then we were able to ensure the effectiveness of our approach based on the results in the static and dynamic cases, which are very encouraging. Full article

Pulmonary surfactant (PS) has been proposed as an efficient drug delivery vehicle for inhaled therapies. Its ability to adsorb and spread interfacially and transport different drugs associated with it has been studied mainly by different surface balance designs, typically interconnecting various compartments by interfacial paper bridges, mimicking in vitro the respiratory air–liquid interface. It has been demonstrated that only a monomolecular surface layer of PS/drug is able to cross this bridge. However, surfactant films are typically organized as multi-layered structures associated with the interface. The aim of this work was to explore the contribution of surface-associated structures to the spreading of PS and the transport of drugs. We have designed a novel vehiculization balance in which donor and recipient compartments are connected by a whole three-dimensional layer of liquid and not only by an interfacial bridge. By combining different surfactant formulations and liposomes with a fluorescent lipid dye and a model hydrophobic drug, budesonide (BUD), we observed that the use of the bridge significantly reduced the transfer of lipids and drug through the air–liquid interface in comparison to what can be spread through a fully open interfacial liquid layer. We conclude that three-dimensional structures connected to the surfactant interfacial film can provide an important additional contribution to interfacial delivery, as they are able to transport significant amounts of lipids and drugs during surfactant spreading. Full article

The configurable and multi-articulated urban bus is a new type of urban vehicle with the advantages of road vehicles and urban rail trains. However, its articulated and long body structure will bring about difficulties in steering control and trajectory following. Moreover, the following carriages easily deviate from their expected path, leading to the fishtailing and folding of the compartment. In this paper, we propose a generic framework that allows the rapid building of kinematic models for the new train. By introducing the MPC theory, we design a trajectory tracking controller for a multi-articulated vehicle with an arbitrary number of carriages. To verify our models, we establish kinematic models and a trajectory tracking controller for a multi-articulated train with different number of compositions in MATLAB. Under the double-lane-change track and serpentine road conditions, the trajectory tracking of the train is simulated. The influence of the number of carriages, velocity, and length of carriage on the trajectory tracking are further analyzed. The experimental results show the feasibility of our method. Our findings thus provide significant guidance for the design, actual configuration, and trajectory tracking control of the new multi-articulated urban bus. Full article

Car-sharing systems, i.e., automatic, short-time car rentals, are among the solutions of the new mobility concept, which in recent years has gained popularity around the world. With the growing interest in services in society, their demands for the services offered to them have also increased. Since cars play a key role in car-sharing services, the fleet of vehicles should be properly adapted to the needs of customers using the systems. Due to the literature gap related to the procedure of proper selection of vehicles for car sharing and the market need for car-sharing service operators, this work has been devoted to the selection of car models for car sharing from the perspective of users constantly using the systems (regular users). This paper considered the case of the Polish who are constantly using car-sharing service systems. Vehicle selection was classified as a multi-faceted, complex problem, which is why one of the ELECTRE III multi-criteria decision support methods was used for this study. This study focused on the classification of vehicles from the user’s perspective. Twelve modern and most popular car models in 2021 with internal combustion, electric and hybrid engines were considered. The results indicate that the best choice from the point of view of regular customers is large cars (representing vehicle classes C and D), with a large luggage compartment capacity, the highest possible ratio of engine power to vehicle weight, and the ratio of engine power to energy consumption. Importantly, small urban vehicles, which ideologically should be associated with car-sharing services due to occupying as little urban space as possible, were classified as the worst in the ranking. The results support car-sharing operators during the process of completing or upgrading their vehicle fleets. Full article

The nonlinear model predictive control (NMPC) controller is designed for an engine cooling system and aims to control the pump speed and fan speed according to the thermal load, vehicle speed, and ambient temperature in real time with respect to the coolant temperature and comprehensive energy consumption of the system, which serve as the targets. The system control model is connected to the underhood computational fluid dynamics (CFD) model by the coupling thermal transmission equation. For the intricate thermal management process predictive control and system control performance analysis, a coupling multi-thermodynamic system nonlinear model for integrated vehicle thermal management was established. The concept of coupling factor was proposed to provide the boundary conditions considering the thermal transmission interaction of multiple heat exchangers for the radiator module. Using the coupling factor, the thermal flow influence of the structural characteristics in the engine compartment was described with the lumped parameter method, thereby simplifying the space geometric feature numerical calculation. In this way, the coupling between the multiple thermodynamic systems mathematical model and multidimensional nonlinear CFD model was realized, thereby achieving the simulation and analysis of the integrated thermal management multilevel cooperative control process based on the underhood structure design. The research results indicated an excellent capability of the method for integrated control analysis, which contributed to solving the design, analysis, and optimization problems for vehicle thermal management. Compared to the traditional engine cooling mode, the NMPC thermal management scheme clearly behaved the better temperature controlling effects and the lower system energy consumption. The controller could further improve efficiency with reasonable coordination of the convective thermal transfer intensity between the liquid and air sides. In addition, the thermal transfer structures in the engine compartment could also be optimized. Full article

Supply chains for goods that must be kept cool—cold chains—are of increasing importance in world trade. The goods must be kept within well-defined temperature limits to preserve their quality. One technique for reducing logistics costs is to load cold items into multiple compartment vehicles (MCVs), which have several spaces within that can be set for different temperature ranges. These vehicles allow better consolidation of loads. However, constructing the optimal load is a difficult problem, requiring heuristics for solution. In addition, the cost determined must be allocated to the different items being shipped, most often with different owners who need to pay, and this should be done in a stable manner so that firms will continue to combine loads. We outline the basic structure of the MCV loading problem, and offer the view that the optimization and cost allocation problems must be solved together. Doing so presents the opportunity to solve the problem inductively, reducing the size of the feasible set using constraints generated inductively from the inductive construction of minimal balanced collections of subsets. These limits may help the heuristics find a good result faster than optimizing first and allocating later. Full article