Search Results (99)

The single-row facility layout problem (SRFLP) orders unequal-length facilities on a line to minimize flow-weighted center distances. We present a time-adaptive multi-start simulated annealing (AMSA) framework that coordinates one spectral start, randomized restarts, incremental insertion and interchange moves, variable-neighborhood descent (VND), and exact fixed-exterior window optimization under a shared deadline. The primary experiment comprised 137 public instances with 8–1000 facilities and ten fixed seeds per instance, giving 1370 successful runs. Among 133 instances with traceable historical reference values, the best of ten runs reached or improved the study reference on 91 instances. The mean run-level relative gap was 0.00470%, and 57 instances produced the same objective for every seed. New controlled experiments compare six variants on 21 representative instances, nine parameter groups on six instances, serial and concurrent execution on nine instances, and four exclusive runtime stages on nine instances. Full AMSA had the best aggregate rank; only removal of adaptive time allocation differed significantly from the full method after Holm correction. All tested non-default parameter levels had paired Wilcoxon $p>0.05$. Profiling showed that annealing consumed 70.29%, 88.52%, and 96.69% of solver time in the small–medium, medium, and large groups, respectively. Two stored layouts below the archived reference snapshot were independently recomputed by two objective identities with zero discrepancy. These results support AMSA as a reproducible deadline-aware baseline; they do not establish superiority over recent methods evaluated on different platforms or budgets. Full article

Traditional location models for Park-and-Ride (P&R) facilities are constrained by the Independent and Identically Distributed (IID) assumption, failing to simultaneously capture inter-modal substitution elasticity and spatial path overlap, which leads to systematic demand forecasting biases. To address this gap, this study proposes an integrated Cross-Nested Logit (CNL) and Paired Combinatorial Logit (PCL) behavioral kernel within a bi-level programming framework, where the upper level minimizes total system generalized cost and the lower level simulates multi-modal Stochastic User Equilibrium (SUE). A hybrid GA-MSA solution strategy is developed. Experiments on the classic Sioux Falls benchmark network demonstrate that the proposed model identifies the optimal construction scale (N = 3) and the critical parking fee threshold (65 CNY) for mode shift. Compared to the un-nested MNL-PCL formulation, the integrated CNL-PCL framework provides an 11.75% downward behavioral correction in P&R market-share estimation, effectively counteracting the overestimation tendency inherent in conventional architectures. The optimal spatial layout (Nodes 4, 6, and 19) achieves a 54.40% share for “P&R + Public Transport” green modes and yields an annual net CO₂ mitigation of 957 tons. These findings confirm that synergistically characterizing mode correlation and path overlap provides a more prudent foundation for sustainable P&R planning. The proposed framework is also generalizable to other multi-modal facility location problems, such as transit-oriented hub sizing or electric vehicle charging network planning. Full article

Currently, the global manufacturing industry faces significant challenges due to increasingly competitive and constantly changing markets. Therefore, adapting to customer needs and improving efficiency and productivity are essential to compete internationally. Plant design and layout play a crucial role in production, material handling, time, and operational costs. The objective of this research was to implement the Systematic Layout Planning (SLP) methodology, supported by CAD and quality tools, to free up 280 m² for production processes in a horizontal blind manufacturing company. AutoCAD was used to model the facilities and visualize pre- and post-improvement scenarios, while ABC classification and root cause analysis supported problem identification in inventory areas. Results show a released expansion area of 340 m², corresponding to 21.5% above the initial space requirement, and a reduction in material travel distance from 317 m to 109 m, equivalent to 65.6%. These improvements enhanced workflow continuity and operational efficiency. The integration of SLP with CAD and quality tools provides a replicable framework for layout optimization in manufacturing environments, while future research should validate the approach under dynamic production conditions. Full article

Aiming at the problems of uneven radiation distribution and difficult regulation in double-film multi-span greenhouses in the Gobi Desert, a spatiotemporal simulation model of the radiation environment based on the coupling of Rhino–Grasshopper and Radiance was constructed in this study. Parametric simulation and multi-objective optimization were adopted to significantly improve the solar radiation capture and distribution uniformity inside the greenhouse, providing a scientific basis for greenhouse design in the Gobi area. The results show that the model has high accuracy (R² > 0.98), and the radiation inside the greenhouse presents a distribution pattern of “higher in the northeast, lower in the southwest, higher in the upper layer and lower in the lower layer”. The optimal orientation is 1° west of south, and the optimal configuration is 8 m span, 5 m eave height, and 30° roof slope. This study can provide quantitative support for the structural design, planting layout and energy-saving regulation of double-film multi-span greenhouses in arid desert areas, and has important practical value for promoting the efficient and sustainable development of facility agriculture in the Gobi Desert. Full article

Addressing the dual challenges of efficient water resource utilization and high construction costs in agricultural production, this study proposes a low-cost sprinkler irrigation system featuring a joint optimized design of water supply facilities and sprinkler layout. Initially, to mitigate water wastage at the field boundaries, an enhanced sprinkler layout is designed. This design strategically adjusts sprinkler spacing to position units along the irrigation area’s perimeter, leveraging their adjustable spray angles for semicircular coverage, thereby achieving superior water conservation compared to traditional honeycomb full coverage layouts. Subsequently, considering the non-linear relationship between pipeline cost and its length and flow rate, a supply network comprising five independent pipelines running perpendicular to the river is constructed. Furthermore, water storage tanks are strategically located at the head of each pipeline near the water source to reduce costs. Finally, constrained by the daily soil moisture levels required for crop survival, an inference-based dimension reduction algorithm is employed to jointly optimize the daily pipeline flow rate and storage tank capacity for each supply line. Specifically, by constructing the functional mapping between flow rate and tank capacity, the complex bivariate optimization problem is reduced to a single-variable extremum problem. Additionally, a calculation method for the feasible region of decision variables is proposed to ensure solution validity. The results demonstrate that the proposed scheme achieves a minimum total construction cost of CNY 2,611,404.00 with a total storage tank capacity of 114,892.40 L, and generates a detailed daily irrigation strategy. This study offers a significant model reference and a technical pathway for developing agricultural irrigation systems that are both economical and efficient. Full article

Plant layout significantly influences manufacturing performance by optimizing the placement of machines and resources to enhance output and minimize operational costs. We redesigned the layout for the steaming line of a food manufacturing facility to improve line efficiency and labor productivity without compromising product quality. We used the define-measure-analyze-design-verify six sigma methodology to identify problems and develop solutions. Layout modifications were validated using ProModel 2016. Results demonstrated reduced process bottlenecks and improved workflow. The results offered actionable insights into food manufacturing and similar industries, promoting the adoption of data-driven, technology-enabled approaches to enhance operational efficiency and productivity. Full article

Construction site layout planning (CSLP) is an optimization issue that has been studied for decades. However, risk factors are still open to exploration, and risk is often not addressed comprehensively in the state-of-the-art literature; moreover, only a few studies have investigated safety as an optimization component in construction sites. This research aims to obtain optimal layout solutions that minimize site risk. In this study, the components of the risk factor were defined as interaction flows between facilities, closeness factors, and the influence of tower cranes. The Dragonfly Algorithm (DA) was selected to solve the CSLP problem due to its strong exploration and exploitation capacity. A DA-based model was developed that integrates the relationships between facilities into a single objective function. This integration extends existing CSLP optimization frameworks by explicitly incorporating multiple risk factors, which constitutes the novelty of the proposed approach. The model was implemented in an actual construction site as a case study. Also, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were employed for comparison purposes. The acquired layout plans showed that the DA provided lower site risk values with feasible solutions for CSLP optimization problems. To validate the results, structured feedback was obtained from 10 experienced project managers and Occupational Health and Safety (OHS) experts, confirming the practical and safety relevance of the optimized layouts. Overall, the proposed DA-based model in this study not only provides feasible solutions for CSLP problems but also integrates comprehensive safety considerations that enable more efficient and safer construction site layouts. Full article

During fatigue tests of tidal turbine blades, digital image correlation (DIC) is used to collect vital information about the specimen. DIC provides high-resolution displacement and strain maps of selected blade sections; however, continuous operation is hindered by the need to acquire, transfer, and process large volumes of high-resolution images, precluding real-time use during long tests. We address this problem by optimising sparse sensing locations on the blade surface so that full-field maps can be accurately reconstructed from a small subset of pixel measurements. In contrast to most DIC improvements found in the literature, which focus on accelerating the processing stage, this approach circumvents the need to collect high-resolution data. We evaluate this approach in a case study at FastBlade, a dedicated testing facility for tidal turbine blades. With less than 1% of the original pixels measured, the mean relative error evaluated on the dataset is 0.4% and 16% for displacement and strain maps, respectively, with the larger strain error reflecting the higher spatial complexity of strain fields. The optimised layouts outperform random and grid-like arrangements. The framework enables real-time monitoring and, subject to relevant validation, might be applied to reconstruct high-resolution strain maps directly from strain-gauge readings, potentially extending to in-ocean blade monitoring. Given the high accuracy of deflection reconstructions, using them to derive strain fields is suggested as a direction for further study. Full article

The problem of the post-harvest loss of perishable products has been a loss facing food security, especially in areas that lack adequate cold chain facilities. This issue is directly connected with sustainability objectives because post-harvest losses are the major source of food wastage, unneeded energy use, and related greenhouse gas emissions. Cold storage with phase-change material (PCM) is a promising alternative, as it aims at stabilizing temperatures and enhancing energy consumption, but current analyses of performance have been conducted through experimental testing and computational fluid dynamic (CFD) simulations, which are precise but computationally expensive. To handle this drawback, the current work constructs a machine learning predictive model to predict the dynamics of charging and discharging temperature of PCM cold storage systems. Four regression models, namely Random Forest, Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and K-Nearest Neighbors (KNNs), were trained and tested on experimental datasets that were obtained for varying storage layouts. The various error and accuracy measures used to determine model performance comprised MSE, MAE, R², MAPE, and percentage accuracy. The findings suggest that Random Forest provides the best accuracy during both the charging and the discharging process, with the highest R² values of over 0.98 and with minimal mean absolute errors. The KNN model was competitive in the discharge process, especially in cases of consistent thermal recovery patterns, and XGBoost was consistent in layout accuracy. However, SVR had relatively lower robustness, particularly when using nonlinear charged dynamics. Among the evaluated models, the Random Forest algorithm demonstrated the highest predictive accuracy, achieving coefficients of determination (R²) exceeding 0.98 for both charging and discharging processes, with mean absolute errors below 0.6 °C during charging and 0.3 °C during discharging. This paper has proven that machine learning is an efficient surrogate to CFD and experimental-only methods and can be used to predict the thermal behavior of PCM quickly and precisely. The proposed framework will allow for developing cold storage systems based on energy efficiency, low costs, and sustainability, especially in the context of decentralized and resource-limited agricultural supply chains, with the help of quick and data-focused forecasting of PCM thermal behavior. Full article

The last kilometer connection problem of metro transit stations is the core factor to measure the connection efficiency and service quality. Establishing the spatiotemporal distribution pattern of the connection distance is conducive to clarifying the interaction mechanism between bike-sharing connections and urban space. This study focuses on the travel behavior of shared bicycle users accessing metro stations, aiming to reveal the access distance decay patterns and their relationship with influence factors. Finally, the random forest algorithm was used to explore the nonlinear relationship between the influencing factors and the connection decay distance, and to clarify the importance of the factors. Multiple linear regression was applied to examine the linear correlation between the distance decay coefficient and the factors influence. The geographically weighted regression was further employed to explore spatial variations in their effects. Finally, the random forest algorithm was used to rank the importance of the impact factors. The results indicate that proximity distance to metro stations, proximity distance to bus stops, and the number of bus routes serving the station area have significant negative correlations with the distance decay coefficient. Significant spatial heterogeneity was observed in the influence of each factor on the distance decay coefficient, based on the geographically weighted regression analysis. With a high goodness-of-fit (R² = 0.8032), the Random Forest regression model furthermore quantified the relative importance of each factor influencing the distance decay coefficient. The findings can be directly applied to optimize the layout of shared bicycle parking, metro access facilities planning, and multi-modal transportation system design. Full article

The topic of archaeological apothekes, i.e., storage areas not intended for display and not accessible to the public (depositi in Italian), has only recently received the attention it deserves, for reasons related to the history of research methodology. The archiving of archaeological material poses specific problems compared to other categories of material with which the process is generally associated, such as artistic artefacts. Excavation finds consist mainly (and increasingly) of a mass of anonymous, repetitive pottery fragments, not destined to be accessible to the public. The management of these storage facilities poses two sets of problems linked with its archiving: on one hand, its (digital) documentation; on the other hand, its physical arrangement. Both aspects have often been contemplated, but as separate entities by different specialists (archaeologists, conservators, etc.). An adequate approach requires however both aspects to be considered together, for archaeological material only achieves its full value when its context of origin is secure. Only proper management of digital and physical archives can ensure a full understanding of the historical significance of archaeological material. These challenges also apply to the Archaeological Mission of Phaistos, in Crete, where Italian have been active since 1900. The reorganisation of the warehouses in 2024–2025 provided an opportunity to adequately address both the digital archiving of the material and the layout of the warehouses, tackling at the same time the particularly pressing issue in this case of the reuse of ‘legacy data’, which poses problems of standardization. This led also to a new perspective, using old labels and boxes as metadata to reconstruct the methods of archaeological research. The main results however were the creation of a holistic approach to the management of archaeological material and its (written, graphic, photographic, and topographic) documentation through the adoption and implementation of PyArchInit (version 4.9.5), a plug-in of QGIS (version 3.40.7 Bratislava). Full article

The application of autonomous navigation in intelligent agriculture is becoming more and more extensive. Traditional navigation schemes in greenhouses, orchards, and other agricultural environments often have problems such as the inability to deal with an uneven illumination distribution, complex layout, highly repetitive and similar structures, and difficulty in receiving GNSS (Global Navigation Satellite System) signals. In order to solve this problem, this paper proposes a new tightly coupled LiDAR (Light Detection and Ranging) inertial odometry SLAM (LIO-SAM) framework named April-LIO-SAM. The framework innovatively uses Apriltag, a two-dimensional bar code widely used for precise positioning, pose estimation, and scene recognition of objects as a global positioning beacon to replace GNSS to provide absolute pose observation. The system uses three-dimensional LiDAR (VLP-16) and IMU (inertial measurement unit) to collect environmental data and uses Apriltag as absolute coordinates instead of GNSS to solve the problem of unreliable GNSS signal reception in greenhouses, orchards, and other agricultural environments. The SLAM trajectories and navigation performance were validated in a carefully built greenhouse and orchard environment. The experimental results show that the navigation map developed by the April-LIO-SAM yields a root mean square error of 0.057 m. The average positioning errors are 0.041 m, 0.049 m, 0.056 m, and 0.070 m, respectively, when the density of Apriltag is 3 m, 5 m, and 7 m. The navigation experimental results indicate that, at speeds of 0.4, 0.3, and 0.2 m/s, the average lateral deviation is less than 0.053 m, with a standard deviation below 0.034 m. The average heading deviation is less than 2.3°, with a standard deviation below 1.6°. The positioning stability experiments under interference conditions such as illumination and occlusion were carried out. It was verified that the system maintained a good stability under complex external conditions, and the positioning error fluctuation was within 3.0 mm. The results confirm that the robot positioning and navigation accuracy of mobile robots satisfy the continuity in the facility. Full article

In high-density urban areas where ecological protection constraints are increasingly stringent, transportation infrastructure layout must balance service efficiency and environmental preservation. From an ecological-prioritization perspective, this study proposes a three-stage multi-objective optimization strategy for siting underground smart parking facilities using the NSGA-III algorithm, with Haidian District, Beijing, as a case study. First, spatial identification and screening are conducted using GIS, integrating urban fringe-space extraction with POI, AOI, population, and transportation network data to determine candidate locations. Second, a multi-objective model is constructed to minimize green space occupation, walking distance, and construction cost while maximizing service coverage, and is solved with NSGA-III. Third, under the ecological-prioritization strategy, the solution with the lowest land occupation is selected, and marginal benefit analysis is applied to identify the optimal trade-off between ecological and economic objectives, forming a flexible decision-making framework. The findings show that several feasible schemes can achieve zero green-space occupation while maintaining high service coverage, and marginal benefit analysis identifies a cost-effective solution serving about 20,000 residents with an investment of 7 billion CNY. These results confirm that ecological protection and urban service efficiency can be reconciled through quantitative optimization, offering practical guidance for sustainable infrastructure planning. The proposed methodology integrates spatial analysis, multi-objective optimization, and post-Pareto analysis into a unified framework, addressing diverse infrastructure planning problems with conflicting objectives and ecological constraints. It offers both theoretical significance and practical applicability, supporting sustainable urban development under multiple scenarios. Full article

Industrial site selection holds strategic importance in the layout of industrial facilities. Scientific decision-making in site selection not only enhances the economic and technical feasibility of a project but also lays the foundation for sustainable development. However, industrial site selection is considered an NP-hard problem. The criteria used to evaluate site suitability, the methods proven effective under different conditions, big data sources introduced, and the key data gaps, methodological limitations, and research priorities to improve decision quality are important for researchers and engineers. Based on the Web of Science (WOS) core collection as the data source, this paper retrieved the literature related to the themes of “industrial site selection” and “facility location decision making”, and selected 149 highly relevant papers. It systematically categorizes three mainstream site selection methods: operations research-based methods; the application of geographic information systems in site selection; and the application of artificial intelligence in site selection. On this basis, this paper provides a systematic review of the overall industrial site selection process and methodologies, aiming to offer references for subsequent site selection analysis research and practical site selection work. An “MCDM–GIS–AI” technology convergence roadmap is also proposed for industrial site selection to identify remaining research gaps and offer a set of “good-practice guidelines” to inform both practical applications and future analytical studies. Full article

The rapid growth of the semiconductor industry has created a bottleneck in which traditional manual methods for designing fabrication plants (fabs) cannot keep pace with their high complexity and short technological lifecycles. This problem stems from the critical mismatch between a fab’s multiyear construction timeline and the rapidly shrinking lifecycle of the advanced chips it is built to produce. To address this challenge, the present study proposes a methodological framework that uses dynamic generative design within a Building Information Modelling (BIM) environment. This approach applies algorithms to generalized models to generate and evaluate numerous potential design solutions automatically. For facility layouts, the framework produces plans that balance spatial efficiency, material flow, and stringent cleanroom protocols. For complex utility systems, it moves beyond simple clash detection to proactively generate resource-efficient, clash-free routing paths that consider both constructability and long-term maintainability. The primary contribution of this study is a standardized, data-agnostic design process that enhances design quality without requiring sensitive project data, establishing a robust foundation for future Digital Twin integration. Full article