Search Results (74)

Construction and demolition waste (CDW) constitute a menace in Zimbabwe. The industry’s image is tainted by rampant disposal on roadsides, in watercourses, and in landfills. Concerted practical efforts to proffer solutions to the problems of CDW disposal have achieved little. Therefore, this study aimed to develop a lean-based framework that could help reduce the impacts of CDW. An in-depth review of the related literature was conducted to establish that lean construction approaches have been adopted to minimise CDW. The literature review led to the compilation of a semi-structured questionnaire used to expedite survey research, which received insights and perspectives from 260 construction personnel gathered through a purposive sampling technique. The top-ranked lean CDW minimisation framework embeds recycling, recovering, and reuse, Kaizen (continuous improvement), Last Planner System (LPS), Just-in-Time (JIT), and Andon (visualisation). The four-step framework shows potential for reducing CDW in Zimbabwe and similar regional contexts. Some of the findings show that the recycling technologies needed to recycle construction waste are not yet available in Zimbabwe. The available regulatory frameworks are not very clear on using recovered, salvaged, and recycled construction materials. Designers are not designing in a way that controls waste streams on sites. Full article

This paper studies the symmetries of the extension to three points of the Dubins problem, the Three-Point Dubins Problem (3PDP), which consists of finding the shortest curvature-constrained $C^1$ path passing through three waypoints, which are the first and last oriented. In the literature, the optimal solution is selected by enumerating 18 possible candidates: the best is elected as the global solution of the instance of the 3PDP. To reduce the need of this enumeration, we exploit the symmetries of the problem to improve the solution strategy by using a Machine Learning (ML) framework. We show how to map an arbitrary configuration into a canonical domain and significantly reduce the parameter space, without a loss of generality. Then, we use this method to construct a compact yet comprehensive dataset of over 17 million valid cases. The reduction in the input dimensionality leads to a faster and more robust learning approach; we investigate both regression and classification neural networks, where the regression model estimates the optimal intermediate angle, and the classification model predicts the path type. The classification network achieved a top-1 accuracy of 97.5% and 100% accuracy within the top-5 predictions (instead of testing all 18 cases), whereas the regression model attained a mean angular error of about $2^{°}$. A detailed case study illustrates how the proposed method can complement existing analytic approaches by providing accurate initial guesses, thus accelerating iterative solvers. Our results demonstrate that ML-based methods can serve as efficient and reliable alternatives for solving the 3PDP, with direct implications for other motion planners in robotics and autonomous systems. Full article

Urban air mobility (UAM) introduces electric vertical takeoff and landing (eVTOL) systems, creating new requirements for infrastructure planning. Vertiport siting is central, yet existing approaches such as multi-criteria decision analysis and optimization often rely on fixed criteria and seldom integrate ground transportation, which is critical for first- and last-mile access. Large language models (LLMs) show strong capabilities in reasoning and tool orchestration, but their role in siting tasks remains underexplored. This study proposes a strategy-aware LLM-based framework that connects heterogeneous spatial data with planning strategies expressed in natural language. A reflective loop connects the planner, executor, and validator for iterative refinement using two methods: multi-criteria decision analysis for interpretable mapping and a genetic algorithm for nonlinear optimization. Experiments in Los Angeles highlight both the potential and challenges of applying LLM agents to siting: outcome evaluation shows that strategies can be translated into distinct trade-offs, while process evaluation demonstrates the benefits of iterative refinement. The study suggests that LLM-based agents can formalize qualitative strategies into reproducible workflows, indicating their potential for UAM siting and promise for broader use in urban planning. Full article

On-demand urban delivery increasingly relies on electric delivery bicycles (EDBs), yet their limited battery capacity creates coupled challenges of routing efficiency and energy replenishment. We study a novel battery swapping cabinet location-routing problem (BSC-LRP) with multiple depots, which jointly optimizes routing and modular energy infrastructure deployment under time-window and battery constraints. To address the problem’s complexity, we design an improved branch-and-price algorithm enhanced with adaptive heuristic-exact labeling (IBP-HL) and a robust arc-based branching scheme. This hybrid framework accelerates column generation while preserving exactness, representing a methodological advancement over standard B&P approaches. Computational experiments on modified Solomon instances show that IBP-HL consistently outperforms Gurobi in both runtime and solution quality on small cases, and achieves substantial speedups and improved bounds over baseline B&P on medium and large cases. These results demonstrate not only the scalability of IBP-HL but also its practical relevance: the framework provides decision support for operators and planners in designing cost-efficient, reliable, and sustainable last-mile delivery systems with battery-swapping infrastructure. Full article

Lean construction is considered a new methodology for minimizing the causes of waste that hinder the achievement of green building (GB) goals. The main aim of this study is to develop a lean model using fuzzy logic technique to mitigate causes of waste effect in GB projects and to determine the most appropriate lean tools affecting these causes. The inputs of this model include GB waste and four lean tools, comprising Quality Function Deployment (QFD), Last Planner System (LPS), Value Stream Mapping (VSM), and 5S, while the outputs include four improvement level indices based on the lean tools. The model uses various logical rules to achieve several relations among the inputs and outputs, and it is applied and verified using data related to several causes of waste categorized under five groups. The strongest correlation is found between VSM and 5S indices, while an adverse relationship is observed between QFD and 5S indices. The results indicate that a cause of waste that refers to poor assessment of site conditions is considered the most substantial one due to its high improvement level indices across all lean tools. The most significant waste group is related to GB stakeholders, which contains 38% of key causes of waste. The improvement using QFD increases by 10% compared to VSM and 28.20% compared to 5S. QFD and LPS are measured as the most suitable lean tools to mitigate the causes of waste effects due to their high impact and high improvement level indices. Full article

Background: Cities worldwide face continuous challenges in sustainable development, particularly in transportation systems where decisions have long-lasting impacts on urban infrastructure and quality of life. The evaluation of sustainable transportation alternatives requires careful consideration of multiple indicators, making it a complex multi-criteria decision-making process. Existing multi-criteria decision-making (MCDM) frameworks often overlook the dual uncertainties introduced by both fuzzy expert judgments and probabilistic performance measures, hindering robust evaluation of transportation alternatives in developing countries. Methods: In response, this study introduces a novel hybrid methodology combining fuzzy set theory and Monte Carlo simulation to evaluate transportation alternatives through 14 comprehensive sustainability indicators. Addressing the critical need for sustainable public transportation assessment in rapidly urbanizing developing countries, where existing assessment frameworks frequently prove inadequate, we present a case study from Amman, Jordan. Results: The results reveal that a Bus Rapid Transit (BRT) system outperforms both conventional automobiles and small buses in 87.06% of simulation scenarios, underscoring its robust sustainability profile. The sensitivity analysis highlights that a BRT system is highly robust, with minimal sensitivity to changes in most criteria and strong responsiveness to critical factors such as land usage. Conclusions: This research provides decision-makers with a comprehensive, evidence-based tool for evaluating public transport investment under uncertainty. The methodology’s ability to account for multiple stakeholder perspectives while handling uncertainty makes it particularly valuable for urban planners and policymakers facing complex transportation infrastructure decisions in rapidly evolving urban environments. Full article

The construction industry, a cornerstone of global economic growth, continues to struggle with entrenched inefficiencies, including low productivity, cost overruns, and fragmented project delivery. Addressing these persistent challenges requires more than incremental improvements, it demands a strategic unification of Lean Construction, Building Information Modeling (BIM), and Emerging Technologies. This systematic review synthesizes evidence from 64 academic studies to identify the most influential tools, techniques, and methodologies across these domains, revealing both their individual strengths and untapped synergies. The analysis highlights widely adopted Lean practices such as the Last Planner System (LPS) and Just-In-Time (JIT); BIM capabilities across 3D, 4D, 5D, 6D, and 7D dimensions; and a spectrum of digital innovations including Digital Twins, AR/VR/MR, AI, IoT, robotics, and blockchain. Crucially, the review demonstrates that despite rapid advancements, integration remains sporadic and unstructured, representing a critical research and industry gap. By moving beyond descriptive mapping, this study establishes an essential foundation for the development of robust, adaptable integration frameworks capable of bridging theory and practice. Such frameworks are urgently needed to optimize efficiency, enhance sustainability, and enable innovation in large-scale and complex construction projects, positioning this work as both a scholarly contribution and a practical roadmap for future research and implementation. Full article

While the explosive growth in e-commerce stresses urban logistics systems, city planners lack of fine-grained data in order to anticipate and manage the resulting freight flows. Using a three-stage analytical approach combining descriptive zonal statistics, hotspot analysis and different regression modeling from univariate to geographically weighted regression, this study integrates one year of parcel deliveries from a leading courier with open spatial layers of land-use zoning, census population, mobile-signal activity and household income to model last-mile demand across different land use types. A baseline linear regression shows that residential population alone accounts for roughly 30% of the variance in annual parcel volumes (2.5–3.0 deliveries per resident) while adding daytime workforce and income increases the prediction accuracy to 39%. In a similar approach where coefficients vary geographically with Geographically Weighted Regression to capture the local heterogeneity achieves a significant raise of the overall R² to 0.54 and surpassing 0.70 in residential and institutional districts. Hot-spot analysis reveals a highly fragmented pattern where fewer than 5% of blocks generate more than 8.5% of all deliveries with no apparent correlation to the broaden land-use classes. Commercial and administrative areas exhibit the greatest intensity (1149 deliveries per ha) yet remain the hardest to explain (global R² = 0.21) underscoring the importance of additional variables such as retail mix, street-network design and tourism flows. Through this approach, the calibrated models can be used to predict city-wide last-mile demand using only public inputs and offers a transferable, privacy-preserving template for evidence-based freight planning. By pinpointing the location and the land uses where demand concentrates, it supports targeted interventions such as micro-depots, locker allocation and dynamic curb-space management towards more sustainable and resilient urban-logistics networks. Full article

This study aimed to investigate the future trajectories of last-mile delivery (LMD), and their implications for sustainable urban logistics and smart city planning. Through a Delphi-based scenario analysis targeting the year 2030, this research draws on inputs from a two-round Delphi study with 52 experts representing logistics, academia, and government. Four key thematic areas were explored: consumer demand and behavior, emerging delivery technologies, innovative delivery services, and regulatory frameworks. The projections were structured using fuzzy c-means clustering, and analyzed through the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT), supporting a systemic understanding of innovation adoption in urban logistics systems. The findings offer strategic insights for municipal planners, policymakers, logistics service providers, and e-commerce stakeholders, helping align infrastructure development and regulatory planning with the evolving needs of last-mile logistics. This approach contributes to advancing resilient, low-emission, and inclusive smart city ecosystems that align with global sustainability goals, particularly those outlined in the UN 2030 Agenda for Sustainable Development. Full article

This study explores the application potential of a lean–Agile hybrid method in complex construction project management. By integrating Scrum iterative development, the Last Planner System, and a BIM collaboration platform, a dual-engine model is established to optimize the dynamic priority mechanism (MoSCoW 2.0) and interface conflict entropy algorithm (ICE model). Through a combination of theoretical and practical approaches, the study elucidates the implementation pathway of this hybrid method and evaluates its benefits in enhancing project efficiency, reducing waste, and accelerating digital transformation. The study provides a replicable management framework for the construction industry and proposes a blockchain-based decentralized knowledge management framework based on blockchain technology. Full article

Island communities that depend on mainland grid connections face substantial risks when natural disasters sever undersea or overhead cables, often resulting in long-lasting outages. This paper presents a comprehensive and novel two-part methodological framework for enhancing the resilience of these communities through networked microgrids that interconnect local renewable energy resources and battery storage. The framework integrates techno-economic capacity optimization using HOMER Pro with agent-based simulation in AnyLogic to determine cost-effective solar and storage capacities and to model dynamic real-time dispatch under varying conditions. Six island communities across three Indonesian provinces serve as illustrative case studies, tested under best-case and worst-case disruption scenarios that reflect seasonal extremes of solar availability. Simulation results reveal that isolated expansions of PV and battery storage can ensure critical residential loads, though certain islands with limited resources continue to experience shortfalls. By contrast, networked microgrids enable surplus power transfers between islands, significantly reducing unmet demand and alleviating the need for large-scale, individual storage. These findings demonstrate the significant potential of clustered microgrid designs to improve reliability, lower operational costs, and facilitate secure energy supply even during prolonged cable outages. The proposed framework offers a scalable roadmap for deploying resilient microgrid clusters in remote regions, with direct policy implications for system planners and local stakeholders seeking to leverage renewable energy in high-risk environments. Full article

Integrating passenger and parcel transportation via transit (also known as transit co-modality) has been regarded as a potential solution to sustainable transportation, in which well-planned locations for parcel lockers are crucial for transferring parcels from transit to last-mile delivery vehicles. This paper proposes a data-driven optimization framework on parcel locker locations in a transit co-modal system, where last-mile delivery is realized via a ride-pooling service that pools passengers and parcels using the same fleet of vehicles. A p-median model is proposed to solve the problem of optimal parcel locker locations and matching between passengers and parcel lockers. We use the taxi trip data and the candidate parcel locker location data from Shenzhen, China, as inputs to the proposed p-median model. Given the size of the dataset, an optimization framework based on random sampling is then developed to determine the optimal parcel locker locations according to each candidate’s frequency of being selected in the sample. The numerical results are given to show the effectiveness of the proposed optimization framework, explore its properties, and perform sensitivity analyses on the key model parameters. Notably, we identify five types of optimal parcel location based on their ranking changes according to the maximum number of planned parcel locker locations, which suggests that planners should carefully determine the optimal number of candidate locations for parcel locker deployment. Moreover, the results of sensitivity analyses reveal that the average passenger detour distance is positively related to the density of passenger demand and is negatively impacted by the number of selected locations. We also identify the minimum distance between any pair of selected locations as an important factor in location planning, as it may significantly affect the candidates’ rankings. Full article

The design phase is critical in construction projects, as it directly impacts cost, quality, and execution efficiency. However, it suffers from structural deficiencies in communication, coordination, and early problem detection, leading to delays, cost overruns, and inefficiencies. While Lean Construction has been widely applied in execution phases, its adoption in design remains fragmented, lacking a clear framework for identifying and evaluating Lean tools in this context. This study aims to identify, classify, and evaluate Lean tools applicable to the building design phase, emphasizing their functionalities, benefits, and limitations. A systematic literature review and expert validation process led to the identification of 16 Lean tools and 26 design-related functionalities. Among these tools, Building Information Modeling (BIM), Last Planner System (LPS), and Agile Design Management (ADM) were identified as the most impactful, collectively addressing 88% of design functionalities. Expert insights revealed that ADM improves task control and decision-making clarity, LPS reduces uncertainty and enhances workflow reliability, and BIM strengthens coordination and early conflict detection. This study provides a structured perspective on Lean tool integration during design, highlighting their benefits and limitations and offering guidance for their implementation. The findings contribute to improving design efficiency, minimizing waste, and fostering collaboration in construction projects. Full article

The lack of access to centralized technologies and economic resources in rural communities makes wastewater management a critical challenge. Decentralized systems such as constructed wetlands offer sustainable solutions by leveraging natural processes for effluent treatment. However, their success and sustainability require active community participation. Currently, there is little evidence of community involvement in the implementation, maintenance, and management of constructed wetlands. Existing strategies for community collaboration in environmental and sanitation projects were analyzed through a literature review covering research conducted in the last 20 years. Only peer-reviewed research in English and Spanish was considered. Based on the findings, a triple helix model integrating academia, government, and society is proposed, compiling the most functional strategies from initial awareness raising to maintenance and dissemination. A case study of community participation is presented under this approach in the Salvador Díaz Mirón rural community, Veracruz, Mexico. The results of this study provide key information for effective strategies designed to manage constructed wetlands, emphasizing that their success depends on both the technology and the genuine commitment of communities to their operation and long-term sustainability. Furthermore, these findings can serve as a reference for decision-makers and project planners seeking to integrate participatory models into decentralized sanitation and water resource conservation. Full article

The increasing complexity of urban logistics in smart cities requires innovative solutions that leverage real-time data, predictive analytics, and adaptive learning to enhance efficiency. This study presents a predictive analytics framework integrating digital twin technology, IoT-enabled logistics data, and cybernetic feedback loops to improve last-mile delivery accuracy, congestion management, and sustainability in smart cities. Grounded in Systems Theory and Cybernetic Theory, the framework models urban logistics as an interconnected network, where real-time IoT data enable dynamic routing, demand forecasting, and self-regulating logistics operations. By incorporating machine learning-driven predictive analytics, the study demonstrates how AI-powered logistics optimization can enhance urban freight mobility. The cybernetic feedback mechanism further improves adaptive decision-making and operational resilience, allowing logistics networks to respond dynamically to changing urban conditions. The findings provide valuable insights for logistics managers, smart city policymakers, and urban planners, highlighting how AI-driven logistics strategies can reduce congestion, enhance sustainability, and optimize delivery performance. The study also contributes to logistics and smart city research by integrating digital twins with adaptive analytics, addressing gaps in dynamic, feedback-driven logistics models. Full article