Search Results (64)

This study creates and refines a risk–effectiveness–integrated dynamic simulation framework that brings together risk and effectiveness factors affecting qualified workforce allocation in multi-project contexts, specifically in the construction of industrial production facilities. Based on a case study of three overlapping projects in West Java, Indonesia, this study examines the requirements for an expert workforce across the Engineering, Procurement, and Construction (EPC) phases. Conventional mitigation measures generally assume that a qualified expert workforce is immediately available. However, hiring the right personnel with specific qualifications for a project takes time. To fill this gap, this paper presents a system dynamics-based model that explicitly integrates quantified project risks and execution effectiveness to determine expert workforce requirements at the multi-project level. This aspect is often addressed implicitly in the existing workforce planning approaches. This mixed-methods strategy includes a literature review, variable validation, simulation modeling, and case analysis. The results show that workforce planning based on integrated risk and effectiveness factors significantly improves project delivery by anticipating expert workforce shortages and reducing the need for reactive solutions. Model validation using real project data demonstrates that the simulated expert workforce demand reproduces both the average behavior and variability observed in real-world practice, satisfying quantitative behavioral validation criteria across projects and the EPC phases. The model contributes to sustainability by enhancing long-term workforce resilience, reducing resource waste, and supporting more efficient industrial project delivery. Full article

This study maps the maturity of construction safety culture in Indonesia across the design and construction phases and identifies priorities for improving the safety management system in construction. Building on a literature-derived framework of categories and subcategories, we conducted a two-round questionnaire-based expert elicitation (pilot and final rounds) and focus group discussions (FGDs) with a purposive panel of 12 experts representing key stakeholders (government/owners, contractors, consultants, and academia). Expert validation was used to assess alignment with field conditions and refine recommendations. The results show average maturity scores of 3.11 in the design phase and 3.36 in the construction phase, indicating a position between the compliant and proactive levels. Sub-category analysis indicates comparatively stronger performance in regulatory mechanisms and operational controls but persistent weaknesses in early-stage planning competence, time and resource allocation for safety, digitalization of safety management, and hazardous waste management. A cross-phase gap is evident: safety is more institutionalized during execution than it is embedded in upstream design decisions. The findings suggest that advancing beyond compliance requires an integrated approach that links national regulations with international project management guidance and construction-specific practices. We conclude by outlining how these frameworks’ integration can support a transition toward more proactive and ultimately resilient safety culture maturity in Indonesia’s construction sector. Full article

Northern japonica rice holds a significant position in China’s food security. However, the traditional nitrogen fertilizer management model (nitrogen application rate > 225 kg/ha, base fertilizer proportion > 50%) has led to serious sustainability problems: the nitrogen utilization rate is only 25–30%, resulting in a large amount of fertilizer waste and economic losses. At the same time, it causes a decline in rice quality, manifested as a 15–20% increase in chalkiness and an 8–12% decrease in palatability value. It has also brought about environmental problems such as soil acidification and eutrophication of water bodies. As an important japonica rice production area, the Liaohe Plain has significant differences in the response of semi-upright and curved panicle varieties to nitrogen fertilizer. However, the agronomic physiological mechanism for the coordinated improvement of yield and quality of japonica rice with different panicle types is still unclear at present, which limits the sustainable development of rice production in this region. For this purpose, in this study, the typical semi-upright spike variety Shendao 47 and the curved spike variety Shendao 11 from the Liaohe Plain were used as materials, and five nitrogen fertilizer treatments were set up: N1, no nitrogen application; N2–N4, conventional nitrogen application rate of 165–225 kg/ha; and N5, and optimized nitrogen application rate of 195 kg/ha allocated in the proportion of 40% base fertilizer, 15% tillering fertilizer, 25% tillering fertilizer, 15% panicle fertilizer, and 5% grain fertilizer. The synergistic regulatory effect of nitrogen fertilizer management on yield and rice quality was systematically explored, and the key agronomic physiological mechanisms were analyzed. The research results show that: (1) The optimized nitrogen fertilizer treatment (N5) achieved a significant increase in yield while reducing the input of nitrogen fertilizer. The yields of Shendao 47 and Shendao 11 reached 10.71–11.82 t/ha and 9.50–10.62 t/ha, respectively, increasing by more than 35% compared with the treatment without nitrogen. (2) The N5 treatment simultaneously improved the processing quality (the whole polished rice rate increased by 4.11%) and the appearance quality (the chalkiness decreased by 63.8% to 77%). (3) The dry matter accumulation during the tillering stage (≥3.2 t/ha) and the net assimilation rate during the scion development stage (≥12 g/m²/d) were identified as key agronomic physiological indicators for regulating the yield-quality synergy. Optimizing nitrogen fertilizer management ensures an adequate supply of photosynthetic products through the high photosynthetic rate of flag-holding leaves and the extended lifespan of functional leaves. The phased nitrogen application strategy of “40% base fertilizer + 25% tillering fertilizer + 15% panicle fertilizer + 5% grain fertilizer” proposed in this study provides a theoretical and practical basis for the sustainable development of japonica rice production in the Liaohe Plain. This plan has achieved the coordinated realization of multiple goals including resource conservation (reducing nitrogen by 13%), environmental protection (lowering the risk of nitrogen loss), food security guarantee (stable increase in yield), and quality improvement (enhancement of rice quality), effectively promoting the development of the northern japonica rice industry towards a green, efficient and sustainable direction. Develop in the right direction. Full article

The composition elements of the earthwork allocation system (excavation project, filling project, transfer yard, waste disposal yard, and material yard) and the relationship between material flow were analyzed. Based on the construction of calculation models for soil and water conservation fees, landscape restoration fees, and road transportation intensity, a joint optimization model was constructed with the objectives of minimizing the total allocation cost and minimizing the peak transportation intensity of the road. By dynamically adjusting the volatility, setting penalty factors, and vectorizing NumPy arrays, the ant colony algorithm is improved and the optimization model is solved. Case analysis shows that considering the intensity of road transportation, the peak transportation intensity significantly decreases, and the proportion of directly filled earthwork increases to over 88% without exceeding the capacity of the intermediate transfer site. The total cost only increases by 0.91%, and the allocation plan is more in line with actual construction needs. Full article

To overcome the limited accuracy of existing street vitality assessments under dense occlusion and their lack of dynamic, multi-source data fusion, this study proposes an integrated dynamic model that couples an enhanced YOLOv11 with heterogeneous spatiotemporal datasets. The network introduces a two-backbone architecture for stronger multi-scale fusion, Spatial Pyramid Depth Convolution (SPDConv) for richer urban scene features, and Dynamic Sparse Sampling (DySample) for robust occlusion handling. Validated in Yibin, the model achieves 90.4% precision, 67.3% recall, and 77.2% mAP@50 gains of 6.5%, 5.3%, and 5.1% over the baseline. By fusing Baidu heatmaps, street-view imagery, road networks, and POI data, a spatial coupling framework quantifies the interplay between commercial facilities and street vitality, enabling dynamic assessment of urban dynamics based on multi-source data fusion, offering insights for targeted retail regulation and adaptive traffic management. By enabling continuous monitoring of urban space use, the model enhances the allocation of public resources and cuts energy waste from idle traffic, thereby advancing urban sustainability via improved commercial planning and responsive traffic control. The work provides a methodological foundation for shifting urban resource allocation from static planning to dynamic, responsive systems. Full article

Accurate forecasting of waste is essential for effective management and allocation of resources. As urban populations grow, the demand for municipal waste systems increases, creating the need for reliable forecasting methods to support planning and decision making. This study compares statistical models Error Trend Seasonality (ETS) and Auto Regressive Integrated Moving Average (ARIMA) with advanced machine learning approaches, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory (LSTM) networks. Five waste categories were analyzed: dead animal, building, commercial, domestic, and liquid waste. Historical datasets were used for model training and validation, with accuracy assessed through mean absolute error and root mean squared error. Results indicate that ARIMA generally outperforms ETS in forecasting building, commercial, and domestic waste streams, especially in capturing long-term domestic waste patterns. Both statistical models, however, show limitations in predicting liquid waste due to its irregular and highly variable nature, where even baseline models sometimes perform competitively. In contrast, machine learning methods consistently achieve the lowest forecasting errors across all categories. Their capacity to capture nonlinear relationships and adapt to complex datasets highlights their reliability for real-world waste management. The findings underline the importance of selecting forecasting techniques tailored to the characteristics of each waste type rather than applying a uniform method. By improving forecasting accuracy, municipalities and policymakers can design more effective waste management strategies that align with Sustainable Development Goal 11 on sustainable cities and communities, Sustainable Development Goal 12 on responsible consumption and production, and Sustainable Development Goal 13 on climate action. Full article

To align with the adoption of electric vehicles in the transportation sector, this paper proposes the use of electric heavy-duty trucks for the logistics and distribution of large prefabricated building components. This approach aims to address the problems of high total costs and significant energy waste in prefabricated component transportation. Focusing on the multi-to-multi distribution mode, a two-level optimization model is constructed. The upper-level model is responsible for the reasonable allocation of demand points. The lower-level model optimizes the selection of road network nodes and charging stations along the delivery routes. It also dynamically adjusts charging timing and volume according to the real-time power situation. To enhance solution performance, a two-level multi-objective evolutionary algorithm based on Pareto theory is designed. This algorithm simultaneously optimizes distribution costs while coordinating path planning and charging strategies. Comparative experiments across different cases show that, compared with traditional single-level and multi-stage models, the proposed algorithm improves both solution accuracy and quality. Additionally, when compared with the scheduling scheme based on the full-charge capacity strategy, the dynamic charging strategy proposed in this paper reduces the total distribution cost by approximately 15.83%. These findings demonstrate that the constructed model and algorithm can effectively optimize the logistics and distribution of prefabricated components. They also provide a feasible solution for the practical application of electric vehicles in engineering logistics. Full article

Traditional pumped storage capacity configuration uses static, year-targeted approaches, leading under-capacity in the early planning stages—wasting renewable energy—and over-capacity in later stages, thus wasting resources. In order to solve the above problems, this article innovatively proposes a dynamic, time-sequenced construction timeline and annual capacity configuration strategy, synchronized with new energy and load development, enhancing sustainability through optimized investment allocation and efficient resource utilization. It presents a two-layer model that considers multiple scenario operational dispatch. The upper layer aims to minimize the curtailment of wind and solar energy, providing a planning scheme to the lower layer, which focuses on multi-scenario economic dispatch, taking into account the peak-valley difference indicators. The models co-iterate: lower-layer operational outcomes feed back to refine the upper-layer’s capacity plan. This process continues until the predicted curtailment calculated by the upper layer aligns closely with that observed in the lower-layer operational simulations, or until capacity changes stabilize, ultimately determining the optimal time-phased capacity configuration. Simulations on a provincial power grid during three typical scenarios in winter, transitional seasons, and summer, as well as extreme weather scenarios, confirm that timely, dynamic configuration strategy significantly enhances renewable absorption, proving the model’s effectiveness. Full article

As the global manufacturing industry’s transformation accelerates toward being intelligent, “unmanned”, and low-carbon, manufacturing workshops face conflicts between production schedules and transportation tasks, leading to low efficiency and resource waste. This paper presents a multi-agent collaborative scheduling optimization method based on a hybrid game–genetic framework to address issues like high AGV (Automated Guided Vehicle) idle rates, excessive energy consumption, and uncoordinated equipment scheduling. The method establishes a trinity system integrating distributed decision-making, dynamic coordination, and environment awareness. In this system, the multi-agent decision-making and collaboration process exhibits significant symmetry characteristics. All agents (machine agents, mobile agents, etc.) follow unified optimization criteria and interaction rules, forming a dynamically balanced symmetric scheduling framework in resource competition and collaboration, which ensures fairness and consistency among different agents in task allocation, path planning, and other links. An improved best-response dynamic algorithm is employed in the decision-making layer to solve the multi-agent Nash equilibrium, while the genetic optimization layer enhances the global search capability by encoding scheduling schemes and adjusting crossover/mutation probabilities using dynamic competition factors. The coordination pivot layer updates constraints in real time based on environmental sensing, forming a closed-loop optimization mechanism. Experimental results show that, compared with the traditional genetic algorithm (TGA) and particle swarm optimization (PSO), the proposed method reduces the maximum completion time by 54.5% and 44.4% in simple scenarios and 57.1% in complex scenarios, the AGV idling rate by 68.3% in simple scenarios and 67.5%/77.6% in complex scenarios, and total energy consumption by 15.7%/10.9% in simple scenarios and 25%/18.2% in complex scenarios. This validates the method’s effectiveness in improving resource utilization and energy efficiency, providing a new technical path for intelligent scheduling in manufacturing workshops. Meanwhile, its symmetric multi-agent collaborative framework also offers a reference for the application of symmetry in complex manufacturing system optimization. Full article

With the popularization of electric vehicles and the continuous expansion of the electric vehicle market, the construction and management of charging facilities for electric vehicles have become important issues in research and practice. In some remote areas, the charging stations are idle due to low traffic flow, resulting in a waste of resources. Areas with high traffic flow may have fewer charging stations, resulting in long queues and road congestion. The purpose of this study is to optimize the location of charging stations and the number of charging piles in the stations based on the distribution of traffic flow, and to construct a bi-level programming model by analyzing the distribution of traffic flow. The upper-level planning model is the user-balanced flow allocation model, which is solved to obtain the optimal traffic flow allocation of the road network, and the output of the upper-level planning model is used as the input of the lower-layer model. The lower-level planning model is a generalized minimum cost model with driving time, charging waiting time, charging time, and the cost of electricity consumed to reach the destination of the trip as objective functions. In this study, an empirical simulation is conducted on the road network of Hefei City, Anhui Province, utilizing three algorithms—GA, GWO, and PSO—for optimization and sensitivity analysis. The optimized results are compared with the existing charging station deployment scheme in the road network to demonstrate the effectiveness of the proposed methodology. Full article

Life cycle assessment (LCA) is an excellent tool for developing energy saving and emission reduction strategies. However, inconsistencies in the summary calculation methods in LCA can significantly affect the reliability of LCA reports, such as the allocation of environmental loads related to solid waste. Essentially, allocation methods are used to allocate responsibility for environmental loads in situations where boundaries are unclear, and therefore, they are susceptible to regional, industry, and regulatory influences. For a long time, there has been controversy over the selection of allocation methods. This study is based on actual production data from a typical cement plant in South China. Multiple parallel LCA cases were carried out using different allocation methods, and different allocation methods were analyzed. Concepts such as driving force, active/passive environmental load, Valorized Solid Waste (VSW), and Non-Valorized Solid Waste (NVSW) were introduced. Analysis shows that the choice of allocation method directly affects the effectiveness of energy saving and emission reduction plans in the LCA report. For VSW, the economic allocation method has been proven to have high universality, effectively capturing the driving forces of economic factors. For NVSW, based on the “polluter pays principle” and active/passive environmental load, we introduced the Collaborative Disposal Allocation Method (CD method). In this study, the environmental benefits of domestic waste collaborative disposal were calculated using the CD method, resulting in a 2.25% reduction in global warming potential (GWP) and a 45.39% reduction in respiratory inorganics (RIs). Full article

Mountainous cities are especially vulnerable to flooding and water quality degradation due to surrounding steep terrain, variable precipitation, and fragile ecosystems. Existing studies often rely on small-scale scenario simulations or computationally intensive optimization algorithms, limiting their practical application. This study proposes a spatial layout strategy for stormwater management tailored to mountainous environments, using the Xining sponge city pilot area as a case study. Based on the “source–sink” theory, flood risk was assessed at the district scale, and the Storm Water Management Model (SWMM) was applied to evaluate four Low-Impact Development (LID) deployment schemes. A novel indicator—the source–sink coupling optimization degree (SSCOD)—was introduced to quantify LID spatial coordination between source and sink zones and identify optimal configuration thresholds. Results show that the four LID allocations significantly reduce runoff and improve water quality compared to the no-LID baseline. Analyses also reveal diminishing returns: optimal LID performance occurs when SSCOD ranges from 0.345 to 0.423, with 24.24–24.41% of LID facilities placed in high-risk zones. Beyond this range, effectiveness plateaus or declines, leading to potential resource waste. The proposed framework provides a technical basis and practical strategy for guiding stormwater infrastructure planning in mountainous cities, balancing effectiveness with resource efficiency. Full article

The construction of high-speed railway (HSR) station areas serves as a crucial catalyst for urban spatial evolution. However, the absence of targeted urban management theories has led to widespread spatial resource waste and post-construction abandonment phenomena in these areas. Existing research predominantly focuses on development strategies for individual construction elements of HSR stations yet lacks comprehensive strategy formulation through coordinated multi-level elements from a sustainable perspective. This study establishes a national database comprising 1018 HSR station area samples across China in 2020, integrating built environment characteristics, HSR network topology, ecological considerations, and socioeconomic indicators. Guided by the land equilibrium utilization theory, we employ the random forest Boruta algorithm to identify critical features, using land supply capacity and development intensity as target variables. Subsequently, K-means++ clustering analysis based on these key variables categorizes the samples into nine distinct clusters. Through normal distribution tests, we establish reference ranges for cluster-specific indicators and propose tailored development strategies across multiple dimensions. This research develops a multimodal feature extraction and evaluation framework specifically designed for the large-scale analysis of HSR station areas. The nine-category strategic recommendations with defined quantitative threshold intervals provide decision-makers with visually intuitive, operationally implementable, and practically significant guidance for spatial planning and resource allocation. Full article

With the acceleration of urbanization, urban regeneration has become a critical strategy for megacities to address spatial fragmentation and inefficient resource allocation. However, the mismatch between transportation nodes and land development potential remains a key barrier to sustainable urban renewal. This research takes the urban renewal areas in Changsha as a typical case. Based on the “Node–Place–Value” (NPV) model, a multi-dimensional evaluation system was constructed. Through multiple empirical analysis methods such as spatial data analysis, field research, and economic indicator evaluation, this study deeply explores how this evaluation system provides a theoretical and data basis for detailed planning and further provides guidance for meeting the needs of urban renewal. Through the empirical analysis of the urban renewal areas in Changsha, this study quantifies the matching relationship among transportation nodes, land use, and economic value and reveals the current imbalance issues of these elements in the areas. For example, there is a common mismatch between the functions of transportation nodes and the potential of land development. Specifically, the land use in transportation hub areas fails to fully utilize their transportation advantages, resulting in the waste of transportation resources and low economic benefits. The results reveal significant imbalances in the following areas: Transportation–Land Mismatch: High-accessibility areas (e.g., Martyrs’ Park and Railway Station ) exhibit underdeveloped land use and low economic conversion efficiency. Peripheral Lag: Remote areas (e.g., Wang Xin and Sunshine 100 ) lack both transportation infrastructure and land development potential, leading to resource waste. Value Dimension Impact: The added “value” dimension highlights thatareas with cultural assets (e.g., Martyrs’ Park) achieve higher comprehensive scores despite spatial constraints. The findings of this study not only provide a scientific basis for urban renewal in Changsha but also offer crucial theoretical support and practical references for other megacities in China to address similar issues and achieve sustainable development. Full article

A two-stage cold supply chain manages the transportation, storage, and distribution of temperature-sensitive products like frozen food, fresh/green products, and pharmaceuticals, which makes it costly. It consists of three key elements: a supplier, a warehouse, and multiple customers. Procurement planning can be conducted for various products, and this study assumes the transport of a fresh/green product with gradually decreasing quality due to its perishable nature. In a two-stage cold supply chain, multiple objective functions can be defined, including cost minimization, product quality optimization, and transportation/storage condition optimization. We developed a mathematical model to optimize these objectives, incorporating two specific functions, cost minimization and product age reduction, to ensure efficient supply chain performance. Traditional solution methods often struggle with multi-objective mathematical models due to their complexity. Therefore, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), a Genetic Algorithm-based approach, was applied to solve the model efficiently. NSGA-II optimized planning for a 7-day period under specific demand conditions, ensuring better resource allocation. The results showed that NSGA-II was better than traditional methods at making decisions and routing efficiently in the two-stage cold supply chain. This led to much better outcomes, with lower costs, less waste, and better product quality throughout the process. Full article