Search Results (76)

Urban vitality is an important evaluation indicator for enhancing urban spatial efficiency and promoting sustainable development. However, few studies have systematically integrated steady-state and dynamic vitality perspectives. To address this gap, we integrate steady-state vitality and dynamic vitality indicators, and use geographically weighted regression (GWR) and geographically weighted logistic regression (GWLR) to quantify how the built environment, natural elements, and travel purposes influence urban vitality. The results reveal that: (1) From the steady-state perspective, urban vitality exhibits a distinct polycentric structure, with transportation POI and catering facilities serving as core driving factors; (2) From the dynamic perspective, areas where citizens are always highly concentrated are mainly influenced by floor area ratio and transportation POI. Green space coverage and building density significantly correspond to patterns of persistently low vitality, while periodic population fluctuations are associated with subway accessibility and proximity to waterfronts. This study provides a comprehensive analysis of the stable spatial distribution and dynamic changes in population aggregation, offering a theoretical and empirical basis for optimizing urban spatial layout and meeting citizens’ activity needs. Full article

This paper explores the café–gym symbiosis mode in Xi’an and its key influencing factors. Taking 63 sub-districts in the seven main urban districts of Xi’an as an example, based on the Dianping.com data of 753 cafés and 335 gyms and survey data from 492 questionnaires, this paper uses methods such as the symbiotic degree, symbiotic coefficient, and binary logistic regression model. On the basis of evaluating the symbiotic model between cafés and fitness centers, it explores the key factors influencing the symbiotic model of cafés and fitness centers. The results showed that cafés and gyms in Xi’an have a variety of characteristics, including agglomeration, correlation, complementarity, and combination, laying the foundation for a symbiosis between them. Among the subject symbiosis modes in Xi’an, point symbiosis was the main symbiotic organization mode. Simultaneously, the proportion of the point symbiosis mode was higher in the urban–rural transitional area than in other areas (traditional inner-city areas, mature built-up areas, emerging expansion areas). An asymmetric reciprocal symbiosis mode dominated the symbiotic behavior mode of entertainment industry objects in Xi’an. In terms of the total weekly entertainment consumer and the additional entertainment consumer dimensions, in the asymmetric reciprocal symbiosis mode, the proportion of cafés having a large impact on gyms was the highest: 60.00% and 62.86%, respectively. However, from the composite index dimension, in the asymmetric reciprocal symbiosis mode, the proportion of gyms having a large impact on cafés was the highest: 39.13%. From the symbiotic interface, the physical space within urban residential areas, office areas, commercial areas, and other main material spaces was the important basic support force for the symbiotic development of urban culture and the entertainment industry. The influence of the symbiosis mode of the culture and entertainment industry has stability. From the perspective of the symbiotic environment, cultural and creative elements, government policies, and consumer spending on entertainment foster the formation of an asymmetrical mutualistic symbiosis model between cafés and gyms. Conversely, factors such as marketization, globalization, and demographic factors inhibit its development. These findings offer valuable insights for urban planners and businesses, which help optimize the layout of the urban entertainment industry. Full article

With the rapid growth of e-commerce and rising demand for faster, reliable last mile delivery, optimizing the spatial layout of terminal logistics facilities is critical. This paper proposes a two-stage location framework for mini-depots and lockers considering spatiotemporal customer demand. In the first stage, Affinity Propagation (AP) clustering identifies candidate mini-depot locations and locker layouts based on temporal and spatial demand characteristics. In the second stage, an Adaptive Heuristic Electric Eel Foraging Optimization (AHEEFO) determines the optimal mini-depot location strategy to minimize total cost. A dataset of 1157 Beijing customer points, including latitude, longitude and demand information, is used for model validation. Results show that Scenario 2, with dispersed demand, outperforms Scenario 1 and traditional strategies in both total cost and customer satisfaction; dispersed demand can be effectively supported via crowdsourced delivery and locker layout, whereas concentrated demand requires more professional courier resources. Comparative experiments reveal AP clustering is more stable, reducing clustering-stage cost by 13.57% compared with K-means, and AHEEFO outperforms other algorithms in cost optimization, computational efficiency, and significance tests under random demand surges. Finally, the sensitivity analysis highlights the effects of different algorithmic and operational parameters, offering valuable insights for both managerial practice and academic research. Full article

In the context of rapid development within the logistics sector and widespread advocacy for sustainable development, this paper proposes enhancements to the task scheduling and path planning components of four-way shuttle systems. The focus lies on refining and innovating modeling approaches and algorithms to address issues in complex environments such as uneven task distribution, poor adaptability to dynamic conditions, and high rates of idle vehicle operation. These improvements aim to enhance system performance, reduce energy consumption, and achieve sustainable development. Therefore, this paper presents an energy-saving and efficiency-enhancing optimization study for a four-way shuttle-based high-density automated warehouse system, utilizing deep reinforcement learning. In terms of task scheduling, a collaborative scheduling algorithm based on an Improved Genetic Algorithm (IGA) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) has been designed. In terms of path planning, this paper provides the A*-DQN method, which integrates the A* algorithm(A*) with Deep Q-Networks (DQN). Through combining multiple layout scenarios and adjusting various parameters, simulation experiments verified that the system error is within 5% or less. Compared to existing methods, the total task duration, path planning length, and energy consumption per order decreased by approximately 12.84%, 9.05%, and 16.68%, respectively. The four-way shuttle vehicle can complete order tasks with virtually no conflicts. The conclusions of this paper have been validated through simulation experiments. Full article

Order-picking is one of the most labor- and cost-intensive operations in warehouses, especially under the pressures of e-commerce growth and supply chain disruptions. Globally, order-picking accounts for 50–75% of total warehouse operating costs and nearly 55% of labor time, making it a dominant factor in logistics performance. Improving picking efficiency is therefore essential not only for reducing operational costs but also for enhancing resilience and sustainability in logistics. This study investigates the combined impact of storage space allocation and picker routing strategies on performance in a real-world edible oil factory warehouse with a three-block U-shaped layout. Three allocation policies (dedicated, turnover-based class storage, and family-based class storage) and three routing methods (S-shape, return, and midpoint) were tested in nine combinations over a five-week period. Results show that storage allocation has a stronger influence on picking efficiency than routing decisions. The family-based (Class 2) allocation with return routing achieved the lowest weekly picking time, reducing retrieval effort by concentrating items in low-level storage locations. Beyond efficiency gains, the findings highlight how simple, low-cost adjustments to storage policies can reduce picker travel, lower energy use, and support sustainable warehouse operations. This case study provides practical guidance for managers of small and medium-sized warehouses and offers baseline insights for the development of digital twin models and smart warehousing solutions in Industry 4.0. Full article

With the surge in urban logistics demand, traditional surface transportation faces challenges, such as traffic congestion and environmental pollution. Leveraging metro systems in metropolitan areas for both passenger commuting and underground logistics presents a promising solution. The metro-based underground logistics system (M-ULS), characterized by extensive coverage and independent right-of-way, has emerged as a potential approach for optimizing urban freight transport. However, existing studies primarily focus on single-line scenarios, lacking in-depth analyses of multi-tier network coordination and dynamic demand responsiveness. This study proposes an optimization framework based on mixed-integer programming and an improved ICSA to address three key challenges in metro freight network planning: balancing passenger and freight demand, optimizing multi-tier node layout, and enhancing computational efficiency for large-scale problem solving. By integrating E-TOPSIS for demand assessment and an adaptive mutation mechanism based on a normal distribution, the solution space is reduced from five to three dimensions, significantly improving algorithm convergence and global search capability. Using the Nanjing metro network as a case study, this research compares the optimization performance of independent line and transshipment-enabled network scenarios. The results indicate that the networked scenario (daily cost: CNY 1.743 million) outperforms the independent line scenario (daily cost: CNY 1.960 million) in terms of freight volume (3.214 million parcels/day) and road traffic alleviation rate (89.19%). However, it also requires a more complex node configuration. This study provides both theoretical and empirical support for planning high-density urban underground logistics systems, demonstrating the potential of multimodal transport networks and intelligent optimization algorithms. Full article

This study tries to make some improvements in the order picking operations by offering a novel mathematical model and efficient solution algorithm. Accordingly, the order picking policies are examined to allow for picking more orders by reducing the collection time/distance of order pickers. Batching orders for the pick are included in the order picking process as it could enable the order picker to collect more orders. Since the most labor-intensive movement in the order picking function in a high-level shelf layout is the retrieval of products from upper shelves and placing them onto the collection vehicle in the picker-to-part system, the use of drones is preferred to eliminate this costly movement. Drones assist humans in the order picking process by retrieving products from upper levels, thus reducing the order picking time. Here, a Vehicle Routing Problem (VRP) is formulated to deal with drone routing which is then solved based on the Order Picking Problem (OPP) framework. Consequently, an integrated OPP involving both order pickers and drones is addressed and formulated using a Mixed-Integer Linear Programming (MILP) model. To cope with the complexity of the problem, an Adaptive Genetic Algorithm (AGA) is designed which is able to yield superior results compared to the classical Genetic Algorithm (GA). Finally, a sensitivity analysis is performed to assess the behavior of the model against real-world fluctuations. The main reason for this research is to speed up the order picking process in warehouses by taking advantage of the tools brought by the technology age. According to the research results, when the results of the drone-assisted order picking process are compared to the order picking process without drone support, an improvement of 29.68% is observed. The theoretical contribution of this work is that it initially mathematically defines the drone-aided OPP in the literature and proposes a solution with the help of the AGA. As a practical contribution, it provides a solution with the capacity to reduce operational costs by accelerating the order picking operation in warehouses and a practical optimization framework for logistics managers. In addition, warehouse managers, senior company managers, and researchers working on order picking processes can benefit from this study. Full article

As a key component of new infrastructure, smart logistics is becoming an essential driver for reducing foreign trade costs and risks and promoting the sustainable development of foreign trade. Using panel data from 286 prefecture level and above cities from 2014 to 2023, this article attempts to refine the measurement of smart logistics level from provincial to municipal levels, construct a two-way fixed effect model and a mediation effect model, and deeply explore the inherent relationship between smart logistics, industrial structure upgrading, and sustainable development of foreign trade. The results reveal that: (1) smart logistics significantly promotes the sustainable development of foreign trade. (2) Rationalization and advancement of industrial structure play an intermediary role between the two. (3) Market integration has a positive moderating effect on the path of “smart logistics—industrial structure rationalization”, but the moderating effect is not significant in other paths. It has been confirmed that there is a “siphon effect” in the advantageous regions. (4) Heterogeneity analysis shows that the effect of smart logistics on foreign trade promotion is more significant in the central and inland regions. This study provides a theoretical basis and practical inspiration for optimizing regional smart logistics layout and deepening industrial structure adjustment. Full article

Under the advancing urban–rural integration strategy, last-mile logistics, and their spatial accessibility, have become key indicators for measuring regional coordination. Focusing on Guangzhou as the case study area, this study constructs an urban–rural spatial accessibility assessment model integrating multimodal convolutional neural networks and Graph Neural Networks (GNN) to systematically examine the evolving accessibility patterns in last-mile logistics distribution across urban and rural spaces. The study finds that Guangzhou’s urban space continues to expand while rural space gradually decreases during this period, showing an overall development trend from centralized single-core to multi-polar networked patterns. The spatial accessibility of last-mile logistics in Guangzhou exhibits higher levels in urban core areas and lower levels in peripheral rural areas, but the overall accessibility is progressively expanding and improving in outlying regions. These accessibility changes not only reflect the optimization path of logistics infrastructure but also reveal the practical progress of urban–rural integration development. Through spatial distribution analysis and dynamic simulation of logistics networks, this study establishes a novel explanatory framework for understanding the spatial mechanisms of urban–rural integration. The findings provide decision-making support for optimizing last-mile logistics network layouts while offering both theoretical foundations and practical approaches for promoting co-construction and sharing of urban–rural infrastructure and achieving integrated regional spatial governance. Full article

The integration of robotic agents into complex indoor construction environments is increasing, particularly through human–robot collaboration (HRC) and multi-robot collaboration (MRC). These collaborative frameworks hold great potential to enhance productivity and safety. However, indoor construction environments present unique challenges, such as dynamic layouts, constrained spaces, and variable lighting conditions, which complicate the safe and effective deployment of collaborative robot teams. Existing studies have primarily addressed various HRC and MRC challenges in manufacturing, logistics, and outdoor construction, with limited attention given to indoor construction settings. To this end, this review presents a comprehensive analysis of human–robot and multi-robot collaboration methods within various indoor domains and critically evaluates the potential of adopting these methods for indoor construction. This review presents three key contributions: (1) it provides a structured evaluation of current human–robot interaction techniques and safety-enhancing methods; (2) it presents a summary of state-of-the-art multi-robot collaboration frameworks, including task allocation, mapping, and coordination; and (3) it identifies major limitations in current systems and provides research directions for enabling scalable, robust, and context-aware collaboration in indoor construction. By bridging the gap between current robotic collaboration methods and the needs of indoor construction, this review lays the foundation for the development of adaptive and optimized collaborative robot deployment frameworks for indoor built environments. Full article

In regions where transportation and the economy are closely integrated, optimizing network structure and enhancing synergy are vital for regional integration. This paper constructs a dual-factor linkage network using enterprise investment and liner shipping data to analyze linkage strength and synergy effects among cities in the Greater Bay Area. The findings reveal that (1) a core-periphery structure exists, with core cities dominating resource flows while secondary cities remain weak. The logistics network is led by Hong Kong and Shenzhen, while the capital flow network showcases the dominance of Hong Kong, Shenzhen, and Guangzhou. (2) From 2016 to 2021, interactions between transportation and the economy deepened, showing strong correlations in logistics and capital flows among core cities and between core and edge cities, but weaker correlations with sub-core and edge cities. Core cities stabilize regional transportation and economy, fostering agglomeration, while sub-core cities are more reliant on them, indicating a need for better resource balance. (3) The spatio-temporal coupling analysis reveals significant heterogeneity in flows among cities, with many exhibiting antagonistic couplings outside core areas. This study enhances understanding of synergy mechanisms in transportation and economic networks, offering insights for optimizing layouts and improving capital flow efficiency. Full article

Background: The integration of mining and urban spaces in coal-resource-based cities holds significant implications for urban transformation and sustainable development. However, existing research lacks an in-depth analysis of its characteristics and driving factors. Methods: This study takes the central urban area of Huaibei City as a case, utilizing historical documents, POI data, and spatial analysis methods to explore the evolution patterns and influencing factors of mining–urban spatial integration. Standard deviation ellipse analysis was employed to examine historical spatial changes, while a binary logistic regression model and principal component analysis were constructed based on 300 m × 300 m grid units to assess the roles of 11 factors, including location, transportation, commerce, and natural environment. Results: The results indicate that mining–urban spatial integration exhibits characteristics of lag, clustering, transportation dominance, and continuity. Commercial activity density, particularly leisure, dining, and shopping facilities, serves as a core driving factor. Road network density, along with the areas of educational and residential zones, positively promotes integration, whereas water surface areas (such as subsidence zones) significantly inhibit it. Among high-integration areas, Xiangshan District stands as the most economically prosperous city center; Lieshan–Yangzhuang mining area blends traditional and modern elements; and Zhuzhuang–Zhangzhuang mining area reflects the industrial landscape post-transformation. Conclusions: The study reveals diverse integration patterns under the synergistic effects of multiple factors, providing a scientific basis for optimizing spatial layouts and coordinating mining–urban development in coal-resource-based cities. Future research should continue to pay attention to the dynamic changes of spatial integration of mining cities, explore more effective integrated development models, and promote the rational and efficient use of urban space and the sustainable development of cities. Full article

The reduction of environmental pollutant emissions—including greenhouse gases, particulate matter, and other harmful substances—represents one of the foremost challenges in climate policy, economics, and industrial management today. Excessive emissions of CO₂, NO_X, and suspended particulates exert significant impacts on climate change as well as human health and welfare. Consequently, numerous studies and regulatory and technological initiatives are underway to mitigate these emissions. One critical area is intra-plant transport within manufacturing facilities, which, despite its localized scope, can substantially contribute to a company’s total emissions. This paper aims to assess the potential of computer simulation using FlexSim software as a decision-support tool for planning inter-operational transport, with a particular focus on environmental aspects. The study analyzes real operational data from a selected production plant (case study), concentrating on the optimization of the number of transport units, their routing, and the layout of workstations. It is hypothesized that reducing the number of trips, shortening transport routes, and efficiently utilizing transport resources can lead to lower emissions of carbon dioxide (CO₂) and nitrogen oxides (NO_X). The findings provide a basis for a broader adoption of digital tools in sustainable production planning, emphasizing the integration of environmental criteria into decision-making processes. Furthermore, the results offer a foundation for future analyses that consider the development of green transport technologies—such as electric and hydrogen-powered vehicles—in the context of their implementation in the internal logistics of manufacturing enterprises. Full article

This study shows a comprehensive simulation to assess and enhance the throughput capacity of unmanned air system vertiports, one of the most essential elements of urban air mobility ecosystems. The framework integrates dynamic grid-based spatial management, probabilistic mission duration algorithms, and EASA-compliant operational protocols to address the infrastructural and logistical demands of high-density UAS operations. It was focused on two use cases—high-frequency food delivery utilizing small UASs and extended-range package logistics with larger UASs—and the model incorporates adaptive vertiport zoning strategies, segregating operations into dedicated sectors for battery charging, swapping, and cargo handling to enable parallel processing and mitigate congestion. The simulation evaluates critical variables such as vertiport dimensions, UAS fleet composition, and mission duration ranges while emphasizing scalability, safety, and compliance with evolving regulatory standards. By examining the interplay between infrastructure design, operational workflows, and resource allocation, the research provides a versatile tool for urban planners and policymakers to optimize vertiport layouts and traffic management protocols. Its modular architecture supports future extensions. This work underscores the necessity of adaptive, data-driven planning to harmonize vertiport functionality with the dynamic demands of urban air mobility, ensuring interoperability, safety, and long-term scalability. Full article

With the rapid proliferation of electric vehicles (EVs), urban logistics faces increasing challenges in optimizing vehicle routing. This paper presents a new modeling framework for the Electric Vehicle Routing Problem (EVRP), where multiple electric trucks serve a set of customers within their capacity limits. The model incorporates critical EV-specific constraints, including limited battery range, charging demand, and dynamic urban traffic conditions, with the objective of minimizing total delivery cost. To efficiently solve this problem, a Dual Population Cooperative Genetic Algorithm (DPCGA) is proposed. The algorithm employs a dual-population mechanism for global exploration, effectively expanding the search space and accelerating convergence. It then introduces local refinement operators to improve solution quality and enhance population diversity. A large number of experimental results demonstrate that DPCGA significantly outperforms traditional algorithms in terms of performance, achieving an average 3% improvement in customer satisfaction and a 15% reduction in computation time. Furthermore, this algorithm shows superior solution quality and robustness compared to the AVNS and ESA-VRPO algorithms, particularly in complex scenarios such as adjustments in charging station layouts and fluctuations in vehicle range. Sensitivity analysis further verifies the stability and practicality of DPCGA in real-world urban delivery environments. Full article