Search Results (227)

The four-way shuttle storage and retrieval system (FWSS/RS) is an advanced automated warehousing solution for achieving green and intelligent logistics, and task allocation is crucial to its logistics efficiency. However, current research on task allocation in three-dimensional storage environments is mostly conducted in the single-operation mode that handles inbound or outbound tasks individually, with limited attention paid to the more prevalent composite operation mode where inbound and outbound tasks coexist. To bridge this gap, this study investigates the task allocation problem in an FWSS/RS under the composite operation mode, and deep reinforcement learning (DRL) is introduced to solve it. Initially, the FWSS/RS operational workflows and equipment motion characteristics are analyzed, and a task allocation model with the total task completion time as the optimization objective is established. Furthermore, the task allocation problem is transformed into a partially observable Markov decision process corresponding to reinforcement learning. Each shuttle is regarded as an independent agent that receives localized observations, including shuttle position information and task completion status, as inputs, and a deep neural network is employed to fit value functions to output action selections. Correspondingly, all agents are trained within an independent deep Q-network (IDQN) framework that facilitates collaborative learning through experience sharing while maintaining decentralized decision-making based on individual observations. Moreover, to validate the efficiency and effectiveness of the proposed model and method, experiments were conducted across various problem scales and transport resource configurations. The experimental results demonstrate that the DRL-based approach outperforms conventional task allocation methods, including the auction algorithm and the genetic algorithm. Specifically, the proposed IDQN-based method reduces the task completion time by up to 12.88% compared to the auction algorithm, and up to 8.64% compared to the genetic algorithm across multiple scenarios. Moreover, task-related factors are found to have a more significant impact on the optimization objectives of task allocation than transport resource-related factors. Full article

Security checks (SCs) at metro stations are regarded as an effective measure to address the heightened security risks associated with high ridership. Introducing SCs without exacerbating congestion requires a thorough understanding of their impact on passenger flow. Most existing studies were conducted where SCs are mandatory and fixed at certain locations. This study presents a method for advising the scale and placement for SCs under a more relaxed security setting. Using agent-based simulation with heterogeneous profiles for both inbound and outbound passenger flow, existing bottlenecks are first identified. By varying different percentages of passengers for SCs and locations to deploy SCs, we observe the influence on existing bottlenecks and suggest a suitable configuration. In our experiments, key bottlenecks are identified before tap-in fare gantries. When deploying SCs near tap-in fare gantries as seen in current practices, a screening percentage of beyond 10% could exacerbate existing bottlenecks and also create new bottlenecks at SC waiting areas. Relocating the SC to a point beyond the fare gantries helps alleviate congestion. This method provides a reference for station managers and transport authorities for balancing security and congestion. Full article

Cross-docking operations in Eastern and Central European markets face increasing complexity amid persistent uncertainty and inflationary pressures. This study provides the first comprehensive comparative analysis integrating economic efficiency with sustainability indicators across strategic locations. Using mixed-methods analysis of 40 bibliographical sources and quantitative modeling of cross-docking scenarios in Bratislava, Prague, and Budapest, we integrate environmental, social, and governance frameworks with activity-based costing and artificial intelligence analysis. Optimized cross-docking achieves statistically significant cost reductions of 10.61% for Eastern and Central European inbound logistics and 3.84% for Western European outbound logistics when utilizing Budapest location (p < 0.01). Activity-based costing reveals labor (35–40%), equipment utilization (25–30%), and facility operations (20–25%) as primary cost drivers. Budapest demonstrates superior integrated performance index incorporating operational efficiency (94.2% loading efficiency), economic impact (EUR 925,000 annual savings), and environmental performance (486 tons CO₂ reduction annually). This is the first empirically validated framework integrating activity-based costing–corporate social responsibility methodologies for an emerging market cross-docking, multi-dimensional performance assessment model transcending operational-sustainability dichotomy and location-specific contingency identification for emerging market implementation. Findings support targeted infrastructure investments, harmonized regulatory frameworks, and public–private partnerships for sustainable logistics development in emerging European markets, providing actionable roadmap for EUR 142,000–EUR 187,000 artificial intelligence implementation investments achieving a 14.6-month return on investment. Full article

Warehouse 4.0 adopts automation, IoT, and big data technologies to establish an intelligent warehousing system for efficient, real-time management of storage, handling, and picking. Addressing challenges like unreasonable storage allocation and inefficient order fulfillment, this paper presents an integrated framework that utilizes swarm intelligence algorithms and collaborative scheduling strategies to optimize inbound/outbound operations. First, for inbound processes, an algorithm-driven storage allocation model is proposed to solve stacker crane scheduling problems. Then, for outbound operations, a “1+N+M” mathematical model is developed, optimized through a three-stage algorithm addressing order picking and distribution scheduling. Finally, a case study of an industrial warehouse validates the proposed methods. The improved mayfly algorithm demonstrates excellent performance, achieving 64.5–74.5% faster convergence and 20.1–24.7% lower fitness values compared to traditional algorithms. The three-stage approach reduces order fulfillment time by 12% and average processing time by 1.8% versus conventional methods. These results confirm the framework’s effectiveness in enhancing warehouse operational efficiency through intelligent automation and optimized resource scheduling. Full article

The COVID-19 pandemic severely impacted Japan’s inbound tourism, but recent recovery trends highlight the growing importance of Chinese tourists. Understanding their motivations is crucial for revitalizing the industry. Building on our previous framework, this study applies Transformer-based natural language processing (NLP) models and principal component analysis (PCA) to analyze large-scale user-generated content (UGC) and identify key motivational factors influencing Chinese tourists’ visits to Hokkaido. Traditional survey-based approaches to tourism motivation research often suffer from response biases and small sample sizes. In contrast, we leverage a pre-trained Transformer model, RoBERTa, to score motivational factors like self-expansion, excitement, and cultural observation. PCA is subsequently used to extract the most significant factors across different destinations. Findings indicate that Chinese tourists are primarily drawn to Hokkaido’s natural scenery and cultural experiences, and the differences in these factors by season. While the model effectively aligns with manual scoring, it shows limitations in capturing more abstract motivations such as excitement and self-expansion. This research advances tourism analytics by applying AI-driven methodologies, offering practical insights for destination marketing and management. Future work can extend this approach to other regions and cross-cultural contexts, further enhancing AI’s role in understanding evolving traveler preferences. Full article

Accurate traffic flow prediction is essential for highway operations, especially during holidays when surging traffic poses significant challenges. This study focuses on holiday traffic and introduces a spatiotemporal cross-attention network (ST-Cross-Attn) that combines a bidirectional convolutional LSTM (Bi-ConvLSTM) with a cross-attention module to jointly predict toll station inbound flow and outbound flow. Under the multi-task learning framework, the model shares spatial–temporal features between inbound flow and outbound flow, enhancing their representations and improving multi-step prediction accuracy. Using three years of highway traffic flow data during Labor Day from Shandong, China, ST-Cross-Attn outperformed eight state-of-the-art benchmarks, achieving an average improvement of 4.34% in inbound flow prediction and 2.3% in outbound flow prediction. Extensive ablation studies further confirmed the effectiveness of the model’s components and multi-task learning framework, demonstrating its potential for reliable holiday traffic forecasting. Full article

This study aims to investigate the influence of open innovation on both resilience and sustainability, with the mediating effect of adaptability. This investigation is conducted through the lens of dynamic capabilities theory. Although many researchers regard OI as a crucial detector for performance enhancement, the mediating effect of OA in shaping the pathways in which it plays a mediating role in both OR and SP remains unexplored. While the majority of previous studies approached open innovation through inbound and outbound innovation positioned as a mediator or by investigating its direct impact either on OR or overall performance, few have concurrently approached it from the breadth and depth dimensions with respect to either performance or resilience. This study offers a comprehensive approach through its unique elements in which all underexplored factors are combined in one theoretical framework. To the best of our knowledge, this study is a pioneer in the UAE business market, providing insights from multisector employee-level data that differs from previous management-focused research. The data was analyzed using the SmartPLS 4 SEM approach. The findings indicate that OID directly influences OR, underscoring the significance of deep and sustained external collaboration. Meanwhile, OIB indirectly contributes to OR through OA, highlighting the significance of the mediating impact of OA. Moreover, both OIB and OID influence SP, positioning OI as a strategic lever for long-term, sustainable performance. This study contributes to the existing body of research by offering nuanced insights into and details on how organizations can benefit from various OI strategies that enhance resilience and sustainability in today’s dynamic business environments. Full article

Efficient material handling is crucial in the logistics operations of modern salt warehouses, where Rail Guided Vehicles (RGVs) and Air Sorting Robots (ASRs) are often deployed to manage inbound and outbound tasks. However, as the number of tasks increases within a given period, conflicts and deadlocks between simultaneously operating RGVs and ASRs become more frequent, reducing efficiency and increasing energy consumption during transportation. To address this, the research frames the inbound and outbound problem as a task allocation issue for the RGV/ASR system with a finite buffer, and proposes a collision avoidance strategy and a zero-wait strategy for loaded machines to reallocate tasks. To improve computational efficiency, we introduce an adaptive multi-neighborhood hybrid search (AMHS) algorithm, which integrates a dual-sequence coding scheme and an elite solution initialization strategy. A dedicated global search operator is designed to expand the search landscape, while an adaptive local search operator, inspired by biological hormone regulation mechanisms, along with a perturbation strategy, is used to refine the local search. In a case study on packaged salt storage, the proposed AMHS algorithm reduced the total makespan by 30.1% compared to the original task queue. Additionally, in 15 randomized test scenarios, AMHS demonstrated superior performance over three benchmark algorithms—Genetic Algorithm (GA), Discrete Imperialist Competitive Algorithm (DICA), and Improved Whale Optimization Algorithm (IWOA)—achieving an average makespan reduction of 12.6% relative to GA. Full article

This article examines the land-use politics of recreation development in rural China. Extending the lens of spatial informality, it analyzes how the appropriation and acquisition of space by small suburban leisure enterprises have constituted a de facto vehicle for rural spatial reconfiguration amidst land-use constraints. Drawing on ethnographic fieldwork and case studies, we illuminate emerging scenarios in which inbound businesses burgeoned through the production of informal spaces, which were subsequently formalized or tolerated by local governments geared towards social economic growth. More so, we reveal the potential and limitations of such an informal-to-formal approach for rural spatial reconfiguration by showing how its sustainability and survival depend upon the enterprises’ ability to enter into a tacit alliance of interests with local authorities. This article casts new light on emerging bottom-up processes of spatial reconfiguration, alongside its repercussions for local suburbs, in the development of rural tourism and suburban leisure. It further suggests that, as an analytical approach, a nuanced understanding of rural restructuring under the recent national rural revitalization strategy can benefit from moving beyond the sole emphasis on formal institutions to analyze the role played by ordinary market actors and their spatial practices that shape rural territories and spatial relationships. Full article

Scarce research on inbound tourism has focused on local residents’ attitudes toward inbound tourism, especially since the COVID-19 pandemic. This study combines social identity theory and emotional solidarity theory to explore Chinese residents’ attitudes toward inbound tourism. In particular, we explore two types of social identities (cultural and environmental identities, termed “humanistic environmental identity” in this study) and three factors of local residents’ emotional solidarity (welcoming nature, emotional closeness, and sympathetic understanding toward inbound tourists). Based on a survey of 310 local residents in Yangzhou, China, this study finds that local residents’ humanistic environmental identity significantly affects their emotional solidarity with inbound tourists, which significantly influences their acceptance of inbound tourism; this, in turn, increases their support for inbound tourism. Meanwhile, local residents’ humanistic environmental identity has an indirect effect on their support for inbound tourism through their welcoming nature, emotional closeness, sympathetic understanding, and acceptance of inbound tourism. In addition, local residents’ xenophobia significantly moderates the relationships between humanistic environmental identity and emotional closeness, between humanistic environmental identity and sympathetic understanding, and between emotional closeness and local residents’ acceptance of inbound tourism. This study extends research on factors affecting inbound tourism from the perspectives of local residents. Full article

In the four-way shuttle system, the efficiency of path planning directly affects the overall effectiveness of logistics and warehousing operations. Traditional path planning methods for multiple four-way shuttles do not take into account the fact that the map status will change as the inbound and outbound tasks are completed. To address this issue, a path planning algorithm for dynamic environments based on an improved Conflict-Based Search (CBS) mechanism is proposed. Firstly, by introducing turning constraints and a node expansion strategy, the A* algorithm is improved, reducing the number of turns and optimizing the node expansion process. Secondly, based on the improved A* algorithm, a path planning algorithm for dynamic environments based on CBS is designed. This algorithm adopts the inbound/outbound task priority strategy and the nearby-task priority strategy to resolve conflicts. It effectively manages the changes in the map status by establishing and maintaining a “ChangeList” and revises the path set of the four-way shuttles. Based on the layout of the intelligent vertical warehouse with four-way shuttles of a certain enterprise, simulation experiments were carried out using a rasterized map. The algorithm was compared with the DCBS-PFM and RRT-A algorithms, verifying the effectiveness and superiority of the algorithm. Full article

This study aims to understand community empowerment by utilizing open innovation through Village-Owned Enterprises (VOE) to enhance sustainable public welfare. To achieve both economic and social missions, VOE must develop the ability to engage in open innovation by leveraging external knowledge sources, both inbound and outbound. This research employs a literature review method, analyzing previous studies indexed in the Scopus Database and processed using the VOSviewer software. The findings indicate that open innovation, which utilizes inbound external knowledge sources such as markets, knowledge, open resources, and cooperative networks, has already been adopted. However, the utilization of knowledge has not yet been fully optimized as a foundation for producing goods and services due to limitations in human resources. Similarly, outbound open innovation derived from technology has been implemented, although the utilization of patents remains suboptimal. This study recommends that village-owned business entities evaluate their operations, particularly in the utilization of knowledge and commercialization to ensure sustainability. Furthermore, this research contributes to the discussion on open innovation by emphasizing that leveraging market-driven knowledge, openness, cooperation, and technology should be a major focus for VOE in the context of business activities, where increased public participation plays a crucial role in sustainable economic development. Full article

With the acceleration of urbanisation and the diversification of residents’ travel needs, rail transit plays a critical role in mitigating traffic congestion. However, existing studies predominantly rely on linear models, neglecting the nonlinear effects and spatial heterogeneity of built environment factors on ridership. To address this gap, this study integrates the Multiscale Geographically Weighted Regression (MGWR) model and the Gradient Boosting Decision Tree (GBDT) model to analyse the impact of built environment factors on total, inbound, and outbound ridership in Shenzhen. Utilising Automatic Fare Collection (AFC) data and multiple built environment variables, we identify six key factors (office type, accessibility, road network density, floor area ratio (FAR), public services, and residential type) through SHapley Additive exPlanations (SHAP) value and partial dependency plot (PDP) analysis. Notably, this study constructs a three-dimensional PDP to explore the linkage effects of building volume ratio and accessibility, revealing their joint influence on ridership. The results demonstrate that the GBDT model outperforms MGWR in handling high-dimensional nonlinear data. This paper provides policy recommendations for transport authorities, highlighting the synergies between optimising the planning of the built environment and the development of rail transport to improve the efficiency of short-distance commuting while supporting long-distance cross-city travel. Full article

In recent years, Serbia has become increasingly popular as a tourism destination, attracting travelers from the surrounding region, as well as Europe and even distant locations. The environmental impact linked with tourism activities, specifically their carbon footprints, has gained growing attention as sustainability becomes an important factor when discussing the future of tourism. This research, which is based on the DEFRA and ADEME methodologies using the Greentripper tool, examines the significance of carbon footprint estimations for incoming tourism in Serbia from a scientific standpoint. By considering the emissions produced from transportation, accommodation, and on-site activities, the results of 1,431,394,511 kg CO2e offer valuable information about the extent of carbon emissions linked to tourism movements. The primary driver of this carbon footprint is transportation (80.2%), on-site activities (15.2%), and housing (4.6%). The per capita tourism carbon footprint is 670 kg CO2e, which is lower compared to the values derived using general data and carbon emissions on a worldwide basis, which amount to 759 kg CO2e. These findings are essential for comprehending the environmental sustainability of tourism operations. Furthermore, carbon footprint assessments play a crucial role as a tool for making informed decisions and implementing initiatives to reduce carbon emissions in the tourism business. This could involve selecting environmentally friendly modes of travel, advocating for sustainable hotel choices, or integrating carbon offsetting activities into vacation packages. In addition, carbon footprint assessments promote transparency and responsibility in the tourism industry. Full article

This study examines how Japanese Generation Z, “digital natives” currently in higher education, engage in cross-cultural learning and develop global skills. In the modern digital era, encountering and studying international topics, cultures, and languages is no longer limited to the traditional physical movement of people to acquire new experiences. We seek to investigate a modernized educational model for intercultural exchange, learning, and internationalization that emphasizes the technological information, platforms, and tools that the digital native generation uses daily. We use survey data from 123 Japanese higher-education students to investigate this subject and help reveal how they can operate and learn global skills in an increasingly digital landscape. Our findings indicate a strong desire to gain intercultural competence through digital sources, remote communications, and interactions with inbound international students. Digital information provides a significant opportunity for students to gain foundational international knowledge and competencies without the level of investment and limited accessibility of traditional study-abroad programs. We consider the pros and cons of integrating digital information into future academic endeavors. Full article