Layout Optimization Model Research Articles

FLOWERS AEP: An Analytical Model for Wind Farm Layout Optimization

ABSTRACTAnnual energy production (AEP) is commonly used in objective functions for wind farm layout optimization. AEP is proportional to wind farm power production integrated over an annual distribution of free‐stream wind conditions. Physics‐based estimates of wind farm power production typically rely on low‐fidelity engineering wake models that approximate the steady‐state wind farm flow field. AEP estimates are then obtained by performing independent simulations for discrete wind conditions and using rectangular quadrature to account for each condition's expected frequency of occurrence. Depending on the number of simulated discrete wind conditions, this numerical integral could be hampered by poor accuracy or high computational costs. The FLOWERS AEP model instead poses an analytical integral of the engineering wake model over the variable wind conditions, yielding a closed‐form, analytical function for wind farm AEP. This paper derives the analytical functions for FLOWERS AEP and its derivatives with respect to turbine position, which are useful for gradient‐based wind farm layout optimization, in nondimensional form. We then analyze the benefits of the FLOWERS AEP model over conventional reference models, focusing on its low cost, adequate wake loss predictions, and smooth design space. Although the FLOWERS approach is found to predict the exact value of AEP with some error relative to the reference model (within 14% on average), it dramatically reduces computation time by an order of magnitude, produces a qualitatively similar design space at relatively low resolution, and yields comparable optimal layouts. This significant speed improvement is critical in layout optimization applications, where determining an optimal layout in an efficient manner is more important than precise AEP prediction.

Spatial layout optimization model integrating layered attention mechanism in the development of smart tourism management

Tourism demand projection is paramount for both corporate operations and destination management, facilitating tourists in crafting bespoke, multifaceted itineraries and enriching their vacation experiences. This study proposes a multi-layer self attention mechanism recommendation algorithm based on dynamic spatial perception, with the aim of refining the analysis of tourists’ emotional inclinations and providing precise estimates of tourism demand. Initially, the model is constructed upon a foundation of multi-layer attention modules, enabling the semantic discovery of proximate entities to the focal scenic locale and employing attention layers to consolidate akin positions, epitomizing them through contiguous vectors. Subsequently, leveraging tourist preferences, the model forecasts the likelihood of analogous attractions as a cornerstone for the recommendation system. Furthermore, an attention mechanism is employed to refine the spatial layout, utilizing the forecasted passenger flow grid to infer tourism demand across multiple scenic locales in forthcoming periods. Ultimately, through scrutiny of data pertaining to renowned tourist destinations in Beijing, the model exhibits an average MAPE of 8.11%, markedly surpassing benchmarks set by alternative deep learning models, thereby underscoring its precision and efficacy. The spatial layout optimization methodology predicated on a multi-layer attention mechanism propounded herein confers substantive benefits to tourism demand prognostication and recommendation systems, promising to elevate the operational standards and customer contentment within the tourism sector.

Production Sequencing and Layout Optimization of Precast Concrete Components under Mold Resource Constraints

Precast concrete components have attracted a lot of attention due to their efficient production on off-site production lines. However, in the precast component production process, unreasonable production sequence and mold layout will reduce production efficiency and affect the workload balance between each process. Due to the multi-species and small-lot production characteristics of precast concrete components, the number of molds corresponding to each precast concrete component is generally limited. In this paper, a production sequence and layout optimization model for assembling precast concrete components under a limited number of molds is proposed, aiming to improve the comprehensive utilization efficiency of the mold tables and balance the workload between each production process of precast components. In order to obtain a better production sequence and a richer combination of mold layout schemes, a multi-objective teaching-learning-based optimization algorithm based on the Pareto dominance relation is developed, and an enhancement mechanism is embedded in the proposed algorithm. To verify the superior performance of the enhanced teaching-learning-based optimization algorithm in improving the comprehensive utilization efficiency of the mold tables and balancing the workload between various processes, three different sizes of precast concrete component production cases are designed. The research results show that the proposed model and optimization algorithm can help production managers to efficiently formulate more reasonable precast component production sequence and layout schemes, especially for those enterprises that are struggling to improve the efficiency of precast concrete component production.

Optimization of industrial layout in airport economic zone through government-enterprise interaction

To avoid the issue of industrial layout plans in Airport Economic Zones (AEZs) being hard to implement due to mismatched the land supply and demand, it is necessary to clarify industrial layout factors that the government (land supplier) and locating enterprises (land demanders) focus on. This study constructs a AEZ's industrial layout optimization model from the government's perspective and enterprises’ locating utility. Based on the Gale-Shapley algorithm, aiming at eliminating blocking pairs, we establish government-enterprise interacting rules and coordinating mechanisms, corresponding to two types of government-enterprise interactions, namely public consultation and investment promotion joint meeting. According to government transparency and enterprise participation, the optimization model and Agent-Based Model are adopted in the combination to simulate the government-enterprise interactions in the decision-making of industrial layout in AEZ, under two interaction rules of “enterprises provide feedback in one go - the government makes revisions in one go” and “enterprises provide feedback one by one - the government makes revisions one by one”, respectively. By analyzing the changing curves of their objective functions, the equilibrium point where both parties can reach an agreement is identified, and the industrial layout scheme in AEZ is determined that will be mutually accepted. It is found that positive interaction with high government transparency and high enterprise participation will help improve the rationality of the industrial layout in AEZ, maximizing the efficiency of spatial allocation of elements and resources.

Layout optimization of pore microstructure in fluid-saturated porous media using Galerkin decoupling technology and independent continuous mapping method (GDT-ICM)

A layout optimal design method combining Galerkin decoupling technology and independent continuous mapping method (GDT-ICM) is proposed to rearrange the layout of pore microstructure. Firstly, the Galerkin decoupling technology (GDT) is employed to solve the Brinkman equation for fluid flow in fluid-saturated porous media, which integrates the Stokes equation and Darcy's law. Within this framework a self-programmable element balance equation is developed. Secondly, a permeability interpolation function is defined for each element, incorporating the permeability of the fluid and pore microstructure. This function distinguishes strictly between the fluid domain and the pore domain. Pore microstructures with arbitrary shape including permeability information are established in an Euler mesh by introducing the Carman-Kozeny equation, shape functions, the Joukowski mapping and filter functions (the modified arctan mapping function and the ‘inpolygon’ function) based on the ICM method. Thirdly, a layout optimization model aiming at minimizing energy dissipation is established to accomplish the optimal distribution of pore microstructures. A sensitivity analysis is calculated with respect to design variables and the optimal model is solved by a genetic algorithm. Numerical results demonstrate that the proposed method is effective and feasible in 2D-space. The maximum velocity within the fluid field is reduced by 45%, and the issue of localized high pressure occurring around the pore is solved. This paper provides guidance for solving the Brinkman coupled equation and the layout optimization of the microstructure in porous media.

Chemical plant optimization layout based on the domino hazard index considering the fixed hazard unit outside the available area

The layout of chemical plants is a significant step to promote the safe and efficient operation of a chemical factory. In the actual production process, the chemical process plant layout may be affected by immovable hazards outside the available layout area. In this paper, the external hazard unit is arranged as one of the chemical process plants. The domino effect potential of all chemical process plants is quantified by using the classical domino hazard index (DHI). A layout optimization model considering the effects of external hazard units has been proposed to obtain the optimal plant layout from economic and safety aspects. The feasible layout solutions are obtained under the orientation, boundary and non-overlapping constraints by the non-dominated sorting genetic algorithm II (NSGA-Ⅱ). The proposed model is further analyzed through a hypothetical case of three storage tanks outside the acrylic acid (AA) production plant layout area. The results show that the total cost range for the optimal solution is 650348–1365443 $, with corresponding DHI values ranging from 29 to 129. The plant with larger DHI value will be arranged to far away from the external hazard units to obtain a safer layout. This work can provide rational layout solutions for the layout designers to make different decisions according to the actual situation.

Exploration of the Indoor Layout Optimization Model in Computer-Aided Visual Analysis

Exploration of the Indoor Layout Optimization Model in Computer-Aided Visual Analysis

A novel optimization model for tackling capacity challenges in natural gas gathering systems

In the layout optimization of natural gas gathering pipeline network, a lot of work has been done, but few scholars focus on the capacity optimization of gas gathering station (short for station). The structural relationship parameters of dendritic pipeline network and the flow parameters of pipeline network are complicated, which makes it difficult to establish the model, and at the same time, the existence of nonlinear term causes the difficulty of solving the problem. In the current two stage star-dendritic natural gas gathering pipeline network, pipeline sizes and station capacity are not well taken into account for the optimization of pipeline network layout at the same time. In this paper, a consideration station capacity and pipeline sizes: Two-stage star-dendritic network layout optimization model (SCPSTSDLO-Model) is constructed by integrating the factors of site discrete characteristics, station capacity and pipeline sizes, and considering the hydraulic pressure drop conditions of the network, and a solution framework based on the nonlinear processing is proposed, which can obtain the optimization results of optimal affiliation, station type, station site and pipeline sizes at the same time. Five cases are used to verify the validity and accuracy of the model and algorithm, compare the sensitivity analysis and solution algorithm, compare the hydraulic pressure drop conditions, and analyze the application in different forms of pipeline networks. The results show that the SCPSTSDLO-Model and the solution method established in this paper can obtain the optimal solutions under different topologies according to the application requirements, taking into account the capacity of stations and pipeline sizes.

Research on Multi-objective Layout Optimization Model of Rural Industry based on Improved Ant Colony Algorithm Under the Background of Digital Economy

Research on Multi-objective Layout Optimization Model of Rural Industry based on Improved Ant Colony Algorithm Under the Background of Digital Economy

Optimal hybrid layout of point and open‐path gas detectors in process facilities of natural gas stations

AbstractBecause open‐path gas detectors offer superior technical performance, leakage monitoring systems tend to use a hybrid layout of point and open‐path gas detectors. However, most research on the optimized layout of leakage monitoring has focused only on point gas detectors. In this paper, an optimization model for a hybrid layout of point and open‐path gas detectors was proposed considering the cost–benefit ratio and detection time. As the measured value of an open‐path gas detector is an integral concentration, a monitor line in the simulation was calculated via the approximate rectangle method. The results showed that the use of a multiobjective gas detector layout considering the cost–benefit ratio and an improved non‐dominated sorting genetic algorithm (NSGA‐II) could optimize the layout of hybrid detectors. The hybrid layout was analyzed in a case study of process facilities at a natural gas station. As the number of detectors increased (safety investment), the proportion of open‐path gas detectors increased, improving both leakage monitoring performance and the cost–benefit ratio. Additionally, the layout of the point gas detectors was analyzed. A comparison of objective function values for detection time showed the superiority of the hybrid layout in our research.

A new optimization model for carbon capture utilization and storage (CCUS) layout based on high-resolution geological variability

Carbon capture utilization and storage (CCUS) is expected to play a pivotal role in achieving carbon-neutral pledge. However, the neglect of geological volatility in existing models will lead to obvious bias in the CCUS layout optimization results. Therefore, a new optimization model for CCUS layout is developed based on high-resolution geological variability. Finally, the model is validated by solving the optimal CCUS layout of coal-fired power plants (CFPPs) in a case region till 2060. The obtained results show that (i) To accomplish the same emission reduction targets, the total length of the pipeline network by 2060 in the scenario considering high-resolution geological variability (HGV) will decrease by 12.5% compared with the scenario without geological variability (NGV); (ii) The total crude oil production can be enhanced by about 19.0% in HGV scenario compared with NGV due to the optimized CO2 source-sink matching; (iii) Comparing with the NGV, the CCUS layout under the HGV can decrease 26.1 and 32.7 CNY/tCO2 in capital costs and O&M costs respectively, and ultimately improve the total profit by 149.4 CNY/tCO2. In summary, the proposed new model can solve the optimal CCUS layout and operation more practically and accurately.

Optimization model of battery electric vehicle charging facility layout towards embedded system and data mining algorithm

Abstract Battery electric vehicles have been developing rapidly in recent years due to their advantages of energy saving and emission reduction. However, the development of battery electric vehicles is still in its infancy due to the relatively immature charging technology and the lack of infrastructure development. In many researches, there have been many solutions to adopt remote monitoring systems in battery electric vehicles to monitor and collect them in real time for statistical analysis and fault diagnosis. However, the current monitoring systems are carried out statistically and it is difficult to extract much valuable information from the data. In this paper, after analyzing the construction mode of battery electric vehicle charging facilities, the battery electric vehicle charging facility system platform using embedded system was analyzed. Several functions were simulated to meet the problems encountered by users in using it, studying the battery electric vehicle charging facility layout optimization model based on data mining algorithm. Focusing on the coverage problem in the siting problem, the State-of-Charge (SOC) of the battery charge state was analyzed. Taking residential areas as an example and 4 weeks as a cycle, it was found that during the first to fourth weeks, the negative power at 1:00, 20:00 and 24:00 was higher. At the same type of time point, different charging thresholds had a significant impact on the charging load demand of users. The larger the charging threshold of battery electric vehicles appeared, the earlier the peak moment and the higher the peak load. The battery state of the battery electric vehicle in the work area is 60 % at 1:00, and it reaches a relatively full state of 90 % at 8:00. In different regions, the allocation of charging thresholds for battery electric vehicles was different, and the Markov theory was derived from the analysis of the coverage problem to enable fast and effective siting of charging piles while satisfying charging convenience and high utilization of charging facilities to complement the research on the optimization model of battery electric vehicle charging facility layout.

An optimization model for the impervious surface spatial layout considering differences in hydrological unit conditions for urban waterlogging prevention in urban renewal

The expansion of impervious surfaces during urbanization, as well as the irrationality of their spatial layout, is one of the main causes of urban waterlogging. Optimizing the spatial layout of impervious surfaces through urban renewal is important for urban waterlogging prevention. The differences in hydrological conditions among different hydrological units affect the optimization efficiency of the spatial layout of impervious surfaces in different hydrological units. Therefore, this study proposes an effective optimization model for impervious surface spatial layout, employing a hybrid approach that couples the Soil Conservation Service Curve Number (SCS-CN) model, nondominated sorting genetic algorithm 2 (NSGA2), and multiple linear programming (MLP) algorithm. This approach is applied in a case study of Guangzhou, China. The results show that (1) compared to the MLP-SCS model, the enhanced model not only provides reduced runoff coefficients but also achieves a better spatial balance of the retention water volume. (2) Compared to the conditions before optimization, medium- and high-density impervious surfaces significantly change after optimization, and the connectivity between patches is reduced. The optimized layout alters the original gradient connection method, preventing the aggregation of runoff from high-density impervious surfaces and thereby reducing the risk of urban waterlogging during heavy rain. This study can provide a reference for optimizing the spatial layout of impervious surfaces aimed at urban waterlogging prevention in urban renewal planning.

Research on data-driven multi-sensory design path based on Chinese gardens under the perspective of AI big data

Abstract In order to promote the development of the Chinese gardening field, this paper proposes a data-driven, multi-sensory design for Chinese gardens. The multisensory design of Chinese gardens, which combines data-driven technology and VR technology, is explored from two perspectives: the visual and auditory senses. It is found that the design easily leads to the loss of the natural beauty of the garden. For this reason, the layout optimization model is constructed using the improved PSO algorithm, and the corresponding analysis method is applied to analyze the effect of the multi-sensory design of the garden. The results show that the model in this paper is better than the other two control models in terms of weighted mean (4.078) and convergence time (0–4 ms). It was also found that the basis for visitors to make experience visual satisfaction evaluations was basically whether the floral landscape was beautiful (39.18%) and whether the field of view was open (23.27%), and the auditory experience design evaluation scores were higher for acacia forests (5.436), red-leafed ridges (4.091), snapdragon forests (3.872), and metasequoia forests (3.657) in the gardens. The research in this paper reveals the focus of multi-sensory design in Chinese gardens, which can provide theoretical references for research in the field of Chinese garden design.

Layout optimization of heliostat field based on genetic algorithm

In order to improve the annual average optical efficiency of a tower solar power plant, a simulated heliostat field layout optimization model is constructed using ray tracing method to calculate the shadow shading efficiency. is constructed using ray tracing method to calculate the shadow shading efficiency. Genetic algorithms are used to encode the variables such as solar pitch angle, solar azimuth angle, and coordinates of heliostat mirrors in chromosomal individuals in binary regions, and to find the near-optimal solutions in iterative populations through the use of ray tracing method. Simulation results show that the model can significantly optimize the layout of the tower solar power system. Simulation results show that the model can significantly optimize the layout of the tower solar power plant and improve the annual average optical efficiency. neighboring heliostats is at 0.398 times the sum of the length and width, the optical efficiency of the Khi Solar One power plant reaches 61.83%, which is an improvement of 15.74% compared to that of the Khi Solar One power plant. improvement of 15.74% compared to this engineering example.

A study on the application of AI in modern interior design and its impact on design efficiency

Abstract This paper focuses on the functional characteristics of BIM technology, emphasizing the need to develop a combination of BIM technology tailored to the user’s interior design process. We utilize the hardware and software to establish the BIM technology environment, construct the interior design model using the Autodesk Revit software platform, import it into Lumion for color rendering, and utilize Navisworks Simulate software to gather project data and monitor the interior design project’s progress. Autodesk Ecotect software was used to find out how lighting affected the interior design using BIM technology. Next, an interior layout optimization model based on the improved whale algorithm was made to solve the interior layout problem, and the impact of using AI was confirmed. The data show that the error range for the daylighting coefficient using BIM technology and the old method is between 0.04 and 0.91. The old method takes 131 minutes longer than BIM technology. This means that the BIM-based lighting simulation model pre-processing process is still much more efficient than the old method. Furthermore, this paper maintains all the model’s index values above 0.68, demonstrating the model’s ability to achieve a reasonable interior space layout while simultaneously enhancing both the layout speed and the layout effect.

A spatial design layout optimization model based on deep learning in the context of rural revitalization

In recent years, with the transformation of China's economy and social development, solving rural problems and achieving rural revitalization has become an urgent task. The development of information technology, specifically the advancements in deep learning technology, has provided new solutions for rural revitalization. This article aims to optimize the spatial structure of rural residential areas and investigate models for optimizing spatial design layouts within the context of rural revitalization utilizing deep learning techniques, specifically convolutional neural networks (DeepLab v3+). This article conducts in-depth research on the DeepLab v3+model and finds that it has good feature extraction ability in processing spatial information such as rural residential houses and ancillary land. By introducing an attention mechanism in the encoding region, we improved the feature extraction accuracy of the model. On the ImageNet and CoCo2017 datasets, the accuracy indicators PA, IoU, and F1 of the DeepLab v3+model performed well, reaching 98.25 %, 75.61 %, and 86.49 %, as well as 98.31 %, 74.91 %, and 85.49 %, respectively. Furthermore, it was discovered that as the training frequency approached 20, both ResNet50 and DeepLab v3-SE exhibited a generalization accuracy of 86.83 % and 89.26 %, correspondingly. This shows the effectiveness of our model in acquiring and utilizing knowledge during the training. The study further investigated the performance of the model in processing images with noise. When the noise standard deviation is 25, the mean SSIM of the O-DeepLab v3+network reaches 0.943, which is 0.53 % and 1.95 % higher than the DeepLab v3-SE network and traditional DeepLab v3+network, respectively. When the noise standard deviation is 50, the SSIM mean of the O-DeepLab v3+network reaches 0.82, which is 0.61 % and 1.49 % higher than the DeepLab v3-SE network and traditional DeepLab v3+network, respectively. This suggests that our model exhibits robustness when processing noisy images. In summary, the spatial design layout optimization model based on deep learning in the context of rural revitalization studied in this article has high accuracy and stability in processing spatial information such as rural residential houses and ancillary land. This is of great significance for optimizing rural land use, promoting urban-rural land allocation, and achieving rural revitalization. Therefore, this study has certain application value.

Commodity layout in supermarkets: using the integration of the comprehensive related value method and genetic algorithm

The existing shelf layout methods do not explicitly consider the attention and relevancy of the commodity systematically and thus have failed to capture the invalid associations, resulting in poor sales impact and customer satisfaction. For such shortcomings, in this paper, we propose a mathematical programming approach for shelf layout problems based on comprehensive related value. First, we introduce the concepts of related value considering both attention and relevancy; second, we give the concept of adjacent utility value and the freedom of placement, and further analyze the impact of the same commodity on surrounding commodities due to different placement positions; third, we establish a new comprehensive related value-based commodity layout optimization model (CRV-CL) and provide the solution steps integrating with a genetic algorithm. Finally, we analyze the characteristics of CRV-CL through a specific case. The simulation results indicate the overall relevancy after applying the CRV-CL model.

Grid-scale crop dynamic layout optimization model considering stakeholders' cropping preferences and practice behaviours

Rapid population growth has led to an increasing demand for food, accompanied by an increase in the resources invested in producing food, and the extensive food production practices have placed a huge burden on the environment. There is an urgent desire of need to find a sustainable model of food production. Therefore, this study attempted to develop a dynamic regional grid-scale crop layout model (ABCAMODP-Crop) combining agent-based model, multi-objective programming, dynamic programming and cellular automata model to promote more sustainable food production from a supply-side perspective. The model can: (1) take into account the planting preferences and practical behaviours of government and farmer as stakeholders in crop layout; (2) further weigh the conflicts among the various objectives that stakeholders are concerned with from resource, environmental, nutritional and economic dimensions; (3) consider the impact of the previous year's crop layout on the next year's crop placement; and (4) obtain grid-scale accurate crop layout solutions by considering the spatial variability of optimization parameters and the neighborhood effects of crop layout cells. The model was applied to the Shiyang River Basin in China to verify its usefulness. Over the entire planning period, crop layout schemes optimized with the objective of integrating irrigation water productivity, environmental impact, nutrient availability and economic benefits have higher comprehensive benefits than the original scheme, with an increase in average irrigation water productivity per unit area of 14.55%, an increase in total yield of 14.45%, an increase in total carbohydrate availability of 7.39%, an increase in total calcium availability of 18.74%, an increase in total net carbon sequestration of 8.05%, an increase in total net benefit of 20.08%. The model developed allows for a holistic view of crop layout and guides decision-makers in a rational distribution of regional crops, thus promoting more sustainable food production. It can equally be extended to other regions as a generic modelling framework.

Cascading failure model and resilience enhancement scheme of space information networks

Space information networks provide global users with high-speed Internet services. The failure of a small number of satellites can trigger cascading failures that can bring down the network. Therefore, enhancing the network’s resilience under cascading failures is crucial. In this paper, we establish a cascading failure model for space information networks for the first time. The gravity model-based load calculation method, entity failure mechanisms, and new survivability measures are presented. The load unbalances of satellites are the leading cause of cascading failures, and the layout of the gateways determines the final load distribution. Therefore, a load-oriented gateway layout optimization model is developed. In order to obtain better optimization performance, a gateway layout solution algorithm based on the cuckoo search is given. Exhaustive simulations are carried out in a case satellite constellation, and the results show that the proposed approach is reasonable and able to enhance the resilience of the network under cascading failures.