Search Results (1,978)

This study addresses a real-world logistics problem in forestry operations: the distribution of plants from cultivation centers to planting sites under strict delivery time windows and limited depot resources. We introduce the Coordinated Truck Loading and Routing Problem (CTLRP), an extension of the classical Vehicle Routing Problem with Time Windows (VRPTW) that integrates routing decisions with truck loading schedules at a single depot with constrained capacity. To solve this NP-hard problem, we develop a metaheuristic algorithm based on Ant Colony Optimization (ACO), enhanced with a global memory system and a novel stochastic return rule that allows trucks to return to the depot when additional deliveries are suboptimal. Parameter calibration experiments are conducted to determine optimal values for the return probability and ant population size. The algorithm is tested on a real forestry dispatch scenario over six working days. The results show that an Ant Colony System (ACS–CTLRP) algorithm reduces total distance traveled by 23%, travel time by 22%, and the number of trucks used by 13 units, while increasing fleet utilization from 54% to 83%. These findings demonstrate that the proposed method significantly outperforms current company planning and offers a transferable framework for depot-constrained routing problems in time-sensitive distribution environments. Full article

To address challenges in automated charging operations of bulk explosive trucks in open-pit mines—specifically difficulties in borehole identification, positioning inaccuracies, and low operational efficiency—this study proposes an intelligent collaborative charging system integrating three modular components: (1) an explosive transport vehicle (with onboard terminal, explosive compartment, and mobility system enabling optimal routing and quantitative dispensing), (2) a charging robot (equipped with borehole detection, loading mechanisms, and mobility system for optimized search path planning and precision positioning), and (3) interconnection systems (coupling devices and interfaces facilitating auxiliary explosive transfer). This approach resolves three critical limitations of conventional systems: (i) mechanical arm-based borehole detection difficulties, (ii) blast hole positioning inaccuracies, and (iii) complex transport routing. The experimental results demonstrate that the intelligent cooperative charging method for open-pit mines achieves an 18% improvement in operational efficiency through intelligent collaboration among its modular components, while simultaneously realizing automated and intelligent charging operations. This advancement has significant implications for promoting intelligent development in open-pit mining operations. Full article

The rapid growth of e-commerce and advances in information and communication technologies have placed increasing pressure on last-mile delivery companies to enhance operational productivity. As investments in logistics infrastructure require long-term planning, maximizing the efficiency of existing terminal operations has become a critical priority. This study proposes a mathematical model for inbound truck scheduling that simultaneously minimizes truck waiting times and balances workload across temporary inventory storage located at outbound chutes in cross-docking terminals. The model incorporates a dynamic rescheduling strategy that updates the assignment of inbound trucks in real time, based on the latest terminal conditions. Numerical experiments, based on real operational data, demonstrate that the proposed approach significantly outperforms conventional strategies such as First-In First-Out (FIFO) and Random assignment in terms of both load balancing and truck turnaround efficiency. In particular, the proposed model improves workload balance by approximately 10% and 12% compared to the FIFO and Random strategies, respectively, and it reduces average truck waiting time by 17% and 18%, thereby contributing to more efficient workflow and alleviating bottlenecks. The findings highlight the practical potential of the proposed strategy for improving the responsiveness and efficiency of parcel distribution centers operating under fixed infrastructure constraints. Future research may extend the proposed approach by incorporating realistic operational factors, such as cargo heterogeneity, uncertain arrivals, and terminal shutdowns due to limited chute storage. Full article

To enhance the electromagnetic performance of flat-wire permanent magnet synchronous motors, three different groove structures were designed for the rotor, and a multi-objective optimization algorithm combining a genetic algorithm (GA) with the TOPSIS method was proposed. Firstly, an 8-pole 48-slot flat-wire motor model was established, and the cogging torque was analytically calculated to compare the motor’s performance under different groove schemes. Secondly, global multi-objective optimization of the rotor groove dimensions was performed using a combined simulation approach involving Maxwell, Workbench, and Optislang, and the optimal rotor groove size structure was selected using the TOPSIS method. Finally, a comparative analysis of the motor’s performance under both rated-load and no-load conditions was conducted for the pre- and post-optimization designs, followed by verification of the mechanical strength of the optimized rotor structure. The research results demonstrate that the combined optimization approach utilizing the genetic algorithm and the TOPSIS method significantly enhances the torque characteristics of the motor. The computational results indicate that the average torque is increased to 165.32 N·m, with the torque ripple reduced from 28.37% to 13.32% and the cogging torque decreased from 896.88 mN·m to 187.9 mN·m. Moreover, the total distortion rates of the air-gap magnetic flux density and the no-load back EMF are significantly suppressed, confirming the rationality of the proposed motor design. Full article

The swift advancements in autonomous vehicle systems have facilitated their implementation across various industries, including agriculture. However, studies primarily focus on passenger vehicles, with fewer examining autonomous trucks. Therefore, this study reviews autonomous truck systems implementation in North Dakota’s agricultural industry to develop comprehensive technology readiness frameworks and strategic deployment approaches. The review integrates systematic literature review and event history analysis of 52 studies, categorized using Social–Ecological–Technological Systems framework across six dimensions: technological, economic, social change, legal, environmental, and implementation challenges. The Technology Readiness Level (TRL) analysis reveals 39.5% of technologies achieving commercial readiness (TRL 8–9), including GPS/RTK positioning and V2V communication demonstrated through Minn-Dak Farmers Cooperative deployments, while gaps exist in TRL 4–6 technologies, particularly cold-weather operations. Nonetheless, challenges remain, including legislative fragmentation, inadequate rural infrastructure, and barriers to public acceptance. The study provides evidence-based recommendations that support a strategic three-phase deployment approach for the adoption of autonomous trucks in agriculture. Full article

To ensure stability and transparency in the European logistics sector, in May 2017, the European Commission presented several proposals to change the regulation of the market—in particular, market access, driving and rest periods, and business trips. In the development of this package, several unfavourable decisions were made that go against Lithuanian transport companies, which will have a significant impact on the companies’ finances, as the frequent return of trucks will lead to additional fuel costs and is also in contradiction with the concept of green logistics. Thus, it is essential to study the Mobility Package’s pros and cons and compare researchers’ views. Accordingly, the subject of this article is the impact of the Mobility Package on Lithuanian logistics companies. This article employs various methods, including an analysis of the scientific literature and legislation, statistical data analysis, PEST analysis, and qualitative research based on expert interviews. The results allow us to identify that the content of the Mobility Package is driven by the goal of ensuring equivalent working conditions throughout the EU, which in this case is the most important object of the legal changes. Also, based on the results obtained, it can be stated that Lithuanian logistics companies that want to remain in the market have several solutions they can employ to achieve that goal, and to support their efforts, a competitiveness improvement model for Lithuanian logistics companies has been developed. Full article

The article describes examples of reliability models of a renewable technical object. The proposed models are mathematical models that, according to the author, are best suited to presenting problems resulting from the operation of the analyzed technical objects. These objects are special vehicles, in this case garbage trucks with plate compaction and rear loading of waste containers. The author described two models: one where a model was analyzed and the replacement of a worn part with a brand new part was assumed, and a model where the worn element was repaired (renewed), so that after the repair, the element showed features as if it were a brand new element. Each of the examples was considered based on operational data from city cleaning companies. Data obtained from service books was used for calculations. The analyzed examples are concluded with short conclusions. In turn, the entire article ends with a summary in the form of conclusions resulting from the use of these specific models. The author draws attention to the reasonableness of their use in the scope analyzed by him, and the benefits that result from the use of these models. Full article

The rapid development of the logistics industry has underscored the urgent need for efficient and sustainable urban freight systems. As a core component of freight systems, heavy-duty trucks (HDT) have been researched regarding surface-level descriptive statistics of their heterogeneities, such as trip volume, frequency, etc., but there is a lack of in-depth analyses of the spatial interaction between freight travel and freight functional clustering, which restricts a systematic understanding of freight systems. Against this backdrop, this study develops a data-driven framework to analyze HDT behavioral heterogeneity and its spatial interactions with a freight functional zone in Shanghai. Leveraging the high-frequency trajectory data of nearly 160,000 HDTs across seven types, we construct a set of regional indicators and employ hierarchical clustering, dividing the city into six freight functional zones. Combined with the HDTs’ application scenarios, functional characteristics, and trip distributions, we further analyze the spatial interaction between the HDTs and clustered zones. The results show that HDT travel patterns are not merely responses to freight demand but complex reflections of urban industrial structures, infrastructure networks, and policy environments. By embedding vehicle behaviors within their spatial and functional contexts, this study reveals a layered freight system in which each HDT type plays a distinct role in supporting economic activities. This research provides a new perspective for deeply understanding the formation mechanisms of HDT trip distributions and offers critical evidence for promoting targeted freight management strategies. Full article

Empty container depot (ECD) design significantly impacts maritime terminal efficiency, yet traditional evaluation approaches assess limited operational factors, constraining comprehensive performance optimization. This study develops an integrated discrete event simulation (DES) framework that simultaneously evaluates lifting equipment utilization, truck turnaround times, and potential collisions to support terminal decision-making. This study combines operational efficiency metrics with safety analytics for non-automated ECDs using Top Lifters and Reach Stackers. Additionally, a regression analysis examines efficiency metrics’ effect on safety risk. A case study at a Chilean multipurpose terminal reveals performance trade-offs between indicators under different operational scenarios, identifying substantial efficiency disparities between dry and refrigerated container operations. An analysis of four distinct collision zones with varying historical risk profiles showed the gate area had the highest potential collisions and a strong regression correlation with efficiency metrics. Similar models showed a poor fit in other conflict zones, evidencing the necessity for dedicated safety indicators complementing traditional measures. This integrated approach quantifies interdependencies between safety and efficiency metrics, helping terminal managers optimize layouts, expose traditional metric limitations, and reduce safety risks in space-constrained maritime terminals. Full article

In this study, the behavior of a three-hinged buried precast arch structure under the impact of the design truck was studied and evaluated based on the finite element method. A three-dimensional finite element analysis model of the buried precast arch structure has been meticulously established, considering arch segments’ joining and surface contact and interaction between surrounding soil and concrete structures. The behavior of the arch structure was examined and compared with the influence of pavement types, number of lanes, and axle spacings. The crucial findings indicate that arch structure behavior differs depending on design truck layouts and pavement stiffness and less on multi-lane vehicle loading effects. Furthermore, the extent of pressure propagation under the wheel depends not only on the magnitude of the axle load but also on the stiffness of the pavement structures. Cement concrete pavement (CCP) allows better dispersion of wheel track pressure on the embankment than asphalt concrete pavement (ACP). Therefore, the degree of increase in arch displacement with ACP is higher than that of CCP. To enhance the coverage of the vehicle influence zone, an extension of the backfill material width should be considered from the bottom of the arch and with the prism plane created at a 45-degree transverse angle. Full article

This study addresses collision detection in the unmanned loading of ore from load-haul-dump (LHD) machines into mining trucks in underground metal mines. Such environments present challenges like heavy dust, confined spaces, sensor occlusions, and poor lighting. This work identifies two primary collision risks and proposes corresponding detection strategies. First, for collisions between the bucket and tunnel walls, LiDAR is used to collect 3D point cloud data. The point cloud is processed through filtering, downsampling, clustering, and segmentation to isolate the bucket and tunnel wall. A KD-tree algorithm is then used to compute distances to assess collision risk. Second, for collisions between the bucket and the mining truck, a kinematic model of the LHD’s working device is established using the Denavit–Hartenberg (DH) method. Combined with inclination sensor data and geometric parameters, a formula is derived to calculate the pose of the bucket’s tip. Key points from the bucket and truck are then extracted to perform collision detection using the oriented bounding box (OBB) and the separating axis theorem (SAT). Simulation results confirm that the derived pose estimation formula yields a maximum error of 0.0252 m, and both collision detection algorithms demonstrate robust performance. Full article

The European Union is committed to reducing greenhouse gas emissions across all sectors, including the transportation sector. It is possible to assume that road freight transport will need to undergo technological changes, leading to greater use of alternative powertrains. This article builds on previous research on the energy consumption of battery electric trucks (BETs) and assesses the economic efficiency of electric vehicles in freight transport through a cost calculation. The primary objective was to determine the conditions under which a BET becomes cost-effective for a transport operator. These findings are practically relevant for freight carriers. Unlike other studies, this article does not focus on total cost of ownership (TCO) but rather compares the variable and fixed costs of BETs and conventional internal combustion engine trucks (ICETs). In this article, the operating costs of BETs were calculated and modeled based on real-world measurements of a tested vehicle. The research findings indicate that BETs are economically efficient, primarily when state subsidies are provided, compensating for the significant difference in purchase costs between BETs and conventional diesel trucks. This study found that optimizing operational conditions (daily routes) enables BETs to reach a break-even point at approximately 110,000 km per year, even without subsidies. Another significant finding is that battery capacity degradation leads to a projected annual operating cost increase of approximately 4%. Full article

In airport operations management, accurately estimating the service durations of ground support equipment such as Potable Water Trucks (PWTs) is essential for improving resource allocation efficiency and ensuring timely aircraft turnaround. Traditional estimation methods often use fixed averages or assume normal distributions, failing to capture real-world variability and extreme scenarios effectively. To address these limitations, this study performs a comprehensive statistical analysis of PWT service durations using operational data from Beijing Daxing International Airport (ZBAD) and Shanghai Pudong International Airport (ZSPD). Employing chi-square goodness-of-fit tests, twenty probability distributions—including several heavy-tailed candidates—were rigorously evaluated under segmented scenarios, such as peak versus non-peak periods, varying temperature conditions, and different aircraft sizes. Results reveal that heavy-tailed distributions offer context-dependent advantages: the stable distribution exhibits superior modeling performance during peak operational periods, whereas the Burr distribution excels under non-peak conditions. Interestingly, contrary to existing operational assumptions, service durations at extremely high and low temperatures showed no significant statistical differences, prompting a reconsideration of temperature-dependent planning practices. Additionally, analysis by aircraft category showed that the Burr distribution best described service durations for large aircraft, while stable and log-logistic distributions were optimal for medium-sized aircraft. Numerical simulations confirmed these findings, demonstrating that the proposed heavy-tailed probabilistic models significantly improved resource prediction accuracy, reducing estimation errors by 13% to 25% compared to conventional methods. This research uniquely demonstrates the practical effectiveness of employing context-sensitive heavy-tailed distributions, substantially enhancing resource efficiency and operational reliability in airport ground handling management. Full article

In this article, we present a case study of the waste collection problem at the Hellenic Military Academy. The waste is sorted by type and collected by a garbage truck. To minimize the travel cost of the waste collection vehicle, we apply the Markov Decision Process methodology. This approach enables the development of more efficient algorithms. Full article

The amount of protein and fat in raw milk determines its quality, value in the marketplace, and related payment to suppliers. Technicians use expensive specialized laboratory equipment to measure milk quality in specialized laboratories. The continuous quality monitoring of the milk supply in the supplier’s tanks enables the production of higher quality products, better milk supply chain optimization, and reduced milk waste. This paper presents an inexpensive AI-powered IoT sensor that continuously measures the protein and fat in the raw milk in the tanks of dairy farms, pickup trucks, and intermediate storage depots across any milk supply chain. The proposed sensor consists of an in-tank IoT device and related software components that run on any IoT platform. The in-tank IoT device quality incorporates a low-cost spectrometer and a microcontroller that can send milk supply measurements to any IoT platform via NB-IoT. The in-tank IoT device of the milk quality sensor is housed in a food-safe polypropylene container that allows its deployment in any milk tank. The IoT software component of the milk quality sensors uses a specialized machine learning (ML) algorithm to translate the spectrometry measurements into milk fat and protein measurements. The paper presents the design of an in-tank IoT sensor and the corresponding IoT software translation of the spectrometry measurements to protein and fat measurements. Moreover, it includes an experimental milk quality sensor evaluation that shows that sensor accuracy is ±0.14% for fat and ±0.07% for protein. Full article