Search Results (26)

Accurate dynamic flow statistics for trackless vehicles are critical for efficiently scheduling trackless transportation systems in underground mining. However, traditional discrete time-point methods suffer from “time membership discontinuity” due to RFID timestamp sparsity. This study proposes a fuzzy five-region membership (FZFM) model to address this issue by subdividing time intervals into five characteristic regions and constructing a composite Gaussian–quadratic membership function. The model dynamically assigns weights to adjacent segments based on temporal distances, ensuring smooth transitions between time intervals while preserving flow conservation. When validated on a 29-day RFID dataset from a large coal mine, FZFM eliminated conservation bias, reduced the boundary mutation index by 11.1% compared with traditional absolute segmentation, and maintained high computational efficiency, proving suitable for real-time systems. The method effectively mitigates abrupt flow jumps at segment boundaries, providing continuous and robust flow distributions for intelligent scheduling algorithms in complex underground logistics systems. Full article

As global trade and container transportation continue to grow, port collection and distribution systems face increasing challenges, including congestion, inefficiency, and environmental impact. Traditional ground-based transportation methods often exacerbate these issues, especially under uncertain demand conditions. This study aims to optimize freight flow allocation in port collection and distribution networks by integrating traditional and innovative transportation modes, including underground container logistics systems, under demand uncertainty. A stochastic optimization model is developed, incorporating transportation, environmental, carbon tax and subsidy, and congestion costs while satisfying various constraints, such as capacity limits, time constraints, and low-carbon transport requirements. The model is solved using a hybrid algorithm combining an improved Genetic Algorithm and Simulated Annealing (GA-SA) with Deep Q-Learning (DQN). Numerical experiments and case studies, particularly focusing on A Port, demonstrate that the proposed approach significantly reduces total operational costs, congestion, and environmental impacts while enhancing system robustness under uncertain demand conditions. The findings highlight the potential of underground logistics systems to improve port logistics efficiency, providing valuable insights for future port management strategies and the integration of sustainable transportation modes. Full article

Underground coal mines face increasing challenges in the scheduling of Electric Rubber-Tired Vehicles (ERTVs) due to confined spaces, dynamic production demands, and the need to coordinate multiple constraints such as complex roadway topologies, strict time windows, and limited charging resources in the context of clean energy transitions. This study presents a Constraint Programming (CP)-based optimization framework that integrates Virtual Charging Station Mapping (VCSM) and sensor fusion positioning to decouple spatiotemporal charging conflicts and applies a dynamic topology adjustment algorithm to enhance computational efficiency. A novel RFID–vision fusion positioning system, leveraging multi-source data to mitigate signal interference in underground environments, provides real-time, reliable spatiotemporal coordinates for the scheduling model. The proposed multi-objective model systematically incorporates hard time windows, load limits, battery endurance, and roadway regulations. Case studies conducted using real-world data from a large-scale Chinese coal mine demonstrate that the method achieves a 17.6% reduction in total transportation mileage, decreases charging events by 60%, and reduces vehicle usage by approximately 33%, all while completely eliminating time window violations. Furthermore, the computational efficiency is improved by 54.4% compared to Mixed-Integer Linear Programming (MILP). By balancing economic and operational objectives, this approach provides a robust and scalable solution for sustainable ERTV scheduling in confined underground environments, with broader applicability to industrial logistics and clean mining practices. Full article

The fast growth of global e-commerce has made cargo transportation and package delivery more important in cities. However, the limited resources for urban road traffic have made urban logistics distribution less efficient. The global movement toward green sustainability, energy conservation, and emission reduction has heightened awareness of the necessity to enhance urban mobility and transportation. This work further investigates the optimization of distribution hub locations based on subway systems, informed by research on urban distribution modes and the current state of underground logistics. This work presents two unique models: a metro-integrated evaluation model and a distribution hub location model, aimed at identifying the ideal subway logistics station and establishing the distribution center with minimal total logistics costs. A heuristic method, the jellyfish search algorithm (JS) in particular, is carefully explained in order to find a good answer for the model. From an empirical perspective, the district of Chaoyang in Beijing, China, was taken as a case to simulate the progress of identifying an ideal metro station as a city distribution hub, aimed at minimizing total logistical costs. The results indicate that the subway system can be used for city deliveries, and the proposed model and method are very useful for improving the location of delivery hubs in the city. Consequently, when subway facilities allow, we should fully utilize the extensive capacity of the subway transit system to enhance the efficient, environmentally friendly, and sustainable advancement of urban logistics. Full article

With the rapid acceleration of global urbanization and the advent of smart city initiatives, large metropolises confront the dual challenges of surging logistics demand and constrained surface transportation resources. Traditional surface logistics networks struggle to support sustainable urban development in high-density areas due to traffic congestion, high carbon emissions, and inefficient last-mile delivery. This paper addresses the layout optimization of a hub-and-spoke underground space logistics system (ULS) network for smart cities under stochastic scenarios by proposing an immune-inspired multi-objective particle swarm optimization (IS-MPSO) algorithm. By integrating a stochastic robust Capacity–Location–Allocation–Routing (CLAR) model, the approach concurrently minimizes construction costs, maximizes operational efficiency, and enhances underground corridor load rates while embedding probability density functions to capture multidimensional uncertainty parameters. Case studies in Beijing’s Fifth Ring area demonstrate that the IS-MPSO algorithm reduces the total objective function value from 9.8 million to 3.4 million within 500 iterations, achieving stable convergence in an average of 280 iterations. The optimized ULS network adopts a “ring–synapse” topology, elevating the underground corridor load rate to 59% and achieving a road freight alleviation rate (RFAR) of 98.1%, thereby shortening the last-mile delivery distance to 1.1 km. This research offers a decision-making paradigm that balances economic efficiency and robustness for the planning of underground logistics space in smart cities, contributing to the sustainable urban development of high-density regions and validating the algorithm’s effectiveness in large-scale combinatorial optimization problems. Full article

Cyperus esculentus, a drought-resistant Cyperaceae with ecological and economic value (stems/leaves as feed, tubers as oil source), stabilizes arid soils through its extensive root system. Understanding its biomass allocation strategies is crucial for comprehending carbon storage in arid environments. The results showed that allometric models best described leaf biomass, while Gompertz and logistic models provided superior accuracy (evaluated using R², p-value, AIC, RMSE, and RSS) for estimating root, tuber, and whole plant biomass. In our study, the equilibrium biomass showed that underground (74.29 g and 64.22 g) was superior to aboveground (63.63 g and 58.72 g); and the growth rate showed the same result, underground (0.112 and 0.055) surpassed aboveground (0.083 and 0.046). The initial inflection point (POI1 = 11) suggests that leaves are prioritized in acquiring limited resources to support growth. In conclusion, the tiller number is a reliable predictor for developing robust biomass models for C. esculentus. The Gompertz model is best for leaves, roots, and total biomass, while the logistic model is optimal for predicting tuber biomass in arid areas. The tiller number is a reliable predictor for developing robust biomass models for C. esculentus. The research findings have supplied useful insights into the growth modifications, production potential, and management experience gained from Cyperus esculentus plant agriculture. Full article

In the last two decades, South Korea has seen an increase in extreme rainfall coinciding with the proliferation of impermeable surfaces due to urban development. When underground drainage systems are overwhelmed, pluvial flooding can occur. Therefore, recognizing drainage systems as key flood-conditioning factors is vital for identifying flood-prone areas and developing predictive models in highly urbanized regions. This study evaluates and maps urban pluvial flood susceptibility in Seoul, South Korea using the machine learning techniques such as logistic regression (LR), random forest (RF), and support vector machines (SVM), and integrating traditional flood conditioning factors and drainage-related data. Together with known flooding points from 2010 to 2022, sixteen flood conditioning factors were selected, including the drainage-related parameters sewer pipe density (SPD) and distance to a storm drain (DSD). The RF model performed best (accuracy: 0.837, an area under the receiver operating characteristic curve (AUC): 0.902), and indicated that 32.65% of the study area has a high susceptibility to flooding. The accuracy and AUC were improved by 7.58% and 3.80%, respectively, after including the two drainage-related variables in the model. This research provides valuable insights for urban flood management, highlighting the primary causes of flooding in Seoul and identifying areas with heightened flood susceptibility, particularly relating to drainage infrastructure. Full article

The metro-based underground logistics system (M-ULS) has been identified as an effective solution to urban problems resulting from the expansion of urban freight traffic. However, there is a paucity of current research that examines the resilience of a M-ULS in the context of unexpected events during operations. Therefore, this paper presents a methodology for assessing the resilience of the M-ULS. The method considers the propagation paths of various failures in a multi-layered, interdependent network that includes topology, functionality, facilities, and information, as well as network performance indicators based on network freight flow and logistics timeliness. The effectiveness of the method is demonstrated using the case of the Nanjing Metro. The results show that the type of disruption, the duration, and the direction of train travel all have a significant impact on the resilience of the M-ULS. The method proposed in this paper provides a scientific basis for the assessment and optimization of M-ULS resilience and also offers new insights into the use of urban rail transit to promote the sustainable development of urban logistics. Full article

The logistics transportation system is critical to the United States economy. Underground Logistics Transportation (ULT) is a class of automated transportation systems in which vehicles carry freight through pipelines and tunnels between terminals. Being able to use a part of the underground space of existing highways will greatly facilitate the construction of such pipelines and tunnels and reduce their construction costs. Underground Logistics Transportation (ULT) could be the answer to make freight transport more sustainable and competitive. Texas highways and railroads are expected to increase by nearly 207% from 2003 to 2030. Truck tonnage will grow by 251%, while rail tonnage is forecasted to increase 118%. The number of trucks carrying NAFTA goods will increase by 263%, and the number of rail units will grow by 195%. This will have a profound impact on the highway and rail systems. The objective of this paper is to present requirements and operational components for three types of ULT lines: standard shipping containers, a standard crate size, and a standard pallet size. This study examines the use of ULT as a mode of underground transportation with the help of three case studies. This research shows that ULT is financially viable, feasible, greener, cost effective, and can become an important part of intermodal freight mobility. Full article

The increasing density of urban populations has spurred interest in utilizing underground space. Underground logistics systems (ULS) are gaining traction due to their effective utilization of this space to enhance urban spatial efficiency. However, research on technological advancements in related fields remains limited. To address this gap, we applied a data-driven approach using patent data related to the ULS to develop a technology roadmap for the field. We employed Latent Dirichlet Allocation (LDA), a machine learning-based topic modeling technique, to categorize and identify six specific technology areas within the ULS domain. Subsequently, we conducted portfolio analytics to pinpoint technology areas with high technological value and to identify the major patent applicants in these areas. Finally, we assessed the technology market potential by mapping the technology life cycle for the identified high-value areas. Among the six technology areas identified, Topic 1 (Underground Material Handling System) and Topic 4 (Underground Transportation System) showed significant patent activity from companies and research institutions in China, the United States, South Korea, and Germany compared to other countries. These areas have the top 10 patent applicants, accounting for 20.8% and 13.6% of all patent applications, respectively. Additionally, technology life cycle analytics revealed a growth trajectory for these identified areas, indicating their rapid expansion and high innovation potential. This study provides a data-driven methodology to develop a technology roadmap that offers valuable insights for researchers, engineers, and policymakers in the ULS industry and supports informed decision-making regarding the field’s future direction. Full article

In recent years, underground logistics systems have attracted more and more attention from scholars and are considered to be a promising new green and intelligent transportation mode. This paper proposes a yard design problem considering an underground container logistics system. The structure and workflow of the underground container logistics system are analyzed, and key features are recognized for the yard design problem, such as the container block layout direction, the lane configuration in the yard, and the number of container blocks. We formulate the problem into mathematical models under different scenarios of the key features with the comprehensive objective of maximizing the total throughput and minimizing the total operation cost simultaneously. An improved tabu search algorithm is designed to solve the problem. Experimental results show that the proposed algorithm can generate a satisfactory layout design solution for a real-size instance. Our research studies different container yard design options for introducing the underground logistics system into port terminals, which provides an important scientific foundation for promoting the application of underground container logistics systems. Full article

Sustainable urban development relies on forward-looking infrastructure development. As an emerging infrastructure system that incorporates green technologies, the Metro-based Underground Logistics System (M-ULS) enables sustainable transportation of passengers and freight within cities collaboratively by sharing rail transit network facilities. M-ULS can effectively save non-renewable energy and reduce pollution to the ecological environment, and the comprehensive benefits of the system make an outstanding contribution to sustainable urban development. The purpose of this study is to provide a systematic review of M-ULS based on different perspectives and to present the development of the M-ULS network integration concept. By employing bibliometric analysis, the four dimensions of M-ULS related literature are statistically analyzed to discover the knowledge structure and research trends. Through thematic discussions, a development path for developing the concept of M-ULS network integration was established. The main findings of this study are summarized as follows: (i) A comparative analysis shows that the metro system has a high potential for freight use; (ii) Improvements in metro freight technologies are conducive to urban economy, environment, and social sustainability; (iii) Network expansion is an inevitable trend for implementing underground logistics based on the metro; (iv) The interaction among public sectors, metro operators, logistics corporations, and users plays a critical role in promoting the development of M-ULS. (v) It is worth mentioning that the planning of green infrastructure should fully consider its comprehensive contribution to the sustainable development of the city. This study visualizes the current status and hotspots of M-ULS research. It also discloses frontier knowledge and novel insights for the integrated planning and operations management of metro and urban underground freight transportation. Full article

The surging demand for logistics systems brought about by the vigorous development of e-commerce makes urban traffic more and more congested. The need for a sustainable transition in terms of urban transportation infrastructure also encourages the further innovation of logistics systems. The urban underground logistics system (ULS) emerges as a promising alternative for realizing efficient large-scale freight distribution in megacities. However, there are relatively few studies that have explored the factors that determine the uptake of ULSs in practice. This paper thus aims to identify the critical success factors of ULSs throughout project life cycle stages. Firstly, a desktop study and a study using the Delphi method were conducted to extract the critical success factors (CSFs) of ULS projects. Secondly, a questionnaire survey was conducted to collect data on the perceived significance of the selected success factors from ULS professionals. Thirdly, the intergroup comparison of the significance of CSFs and exploratory factory analysis were used to ascertain the critical factors and latent determinants influencing the development of ULS projects. In total, 36 CSFs in the four life cycle stages of ULS projects were finalized. The identified factors represent the seven latent determinants in developing a ULS project, namely, overall feasibility and acceptance of the ULS, prototype system, and business model, competence and resources for ULS construction and operation, station layout and intermodal transportation, government policies and incentives, long-term planning of the underground space and logistics network, and market investigation and forecasting. The research findings of the paper help guide practitioners and policy makers on decisions made during ULS planning and construction and provide a reference performance evaluation framework for ULS projects. Full article

The main purpose of the study was to apply symmetry principles to general mathematical modeling based on a bi-level programming model in order to select the optimal nodes of the underground metro-based logistics system (M-ULS). The first step was to select the metro stations as alternative logistics distribution nodes based on the existing metro network. Secondly, given the requirements of suppliers and demanders, a bi-level programming model was built based on symmetry principles to minimize the total cost of logistics distribution nodes, including construction cost, transport cost, and fixed cost. The third objective was to use an efficient heuristic algorithm to solve the model to obtain the optimal location of the nodes of the logistics distribution. Lastly, Nanjing’s Metro Line 2 was used as an example to validate the efficacy of the proposed model. The results of the case indicate that it is possible to deliver goods from logistics distribution nodes to demanders using the excess capacity of the metro, and the proposed bi-level programming model for M-ULS can be used to select suitable metro stations as distribution nodes and achieve the lowest cost on both the supply and demand sides of logistics while still ensuring the green and efficient transport of logistics services. References and suggestions for planning and selecting the location of logistics distribution nodes based on the metro network in the future can be found in this article. Full article

Tunnelling is a vital geotechnical engineering feature of underground transportation systems that is potentially hazardous if not properly investigated, studied, planned, and executed. A reliability, availability, and maintainability (RAM) analysis is one of the main practical techniques in machinery-based projects to recognize the failure and repair rates of machines during or after their operations. RAM analysis of mechanized tunneling can help to manage the project safety and cost, and improve the availability and performance of the machine. There are several methods to obtain and predict the RAM of a system, including the Markov chain simulation and other statistical methods; however, the result of the analysis can be affected by the selected method. This paper presents the results of a critical investigation on the RAM of the Earth pressure balance machines (EPBMs) used in developing an urban metro project in Isfahan, Iran. The five kilometer length of the first line of the Isfahan metro project was excavated using EPBMs over four years. After overhauling the EPBMs and making some minor changes, excavation of the second line started, and to date, about 1.2 km has been excavated by the refurbished machines. In the present study, a RAM analysis has been applied to electrical, mechanical, and cutter head subsystems of the EPBMs in Lines 1 and 2 of the Isfahan metro project over an 18- and 7-month period of machine operation, respectively. The results show that the estimated availability, A(t), determined by the Markov method, is closer to reality but cannot be propagated to reliability R(t) and maintainability M(t) analysis. It was also revealed that by predicting the required maintenance and proper planning, the overall availability of the EPBM was improved from 45% in Line 1 to 61% in Line 2. The outcomes of this study can be used in the future planning of urban tunneling projects to estimate machine, staff, and logistic performance with the least possible error, and appropriately arrange the factors involved in the system. Full article