Integrated kitchen design and optimization based on the improved particle swarm intelligent algorithm

Ji, Xiaomin

doi:10.1111/coin.12301

Cited by 8 publications

(6 citation statements)

References 5 publications

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“…Previous studies into ML applications of facility, floor, or furniture layout problems were mostly focused on equally sized objects [33,38]. Some addressed 10-20 differently sized items [37], or the number of layout objects was usually less than 10 items [34,38,39,41]. In addition, the entire size of the space was small or hypothetical e.g., a 10 × 10 bounding box [38], 4.51 × 5.01 m [39], or 48.6 m 2 [41].…”

Section: Discussionmentioning

confidence: 99%

“…Some addressed 10-20 differently sized items [37], or the number of layout objects was usually less than 10 items [34,38,39,41]. In addition, the entire size of the space was small or hypothetical e.g., a 10 × 10 bounding box [38], 4.51 × 5.01 m [39], or 48.6 m 2 [41]. It seems unreasonable to generalize the layout problem with equal size objects for ELP in museums.…”

Section: Discussionmentioning

confidence: 99%

“…Regarding furniture layout, Sun and Ji applied the improved particle swarm algorithm to optimize the arrangement of eight objects in the kitchen, and the arranged area is 48.6 m 2 , which is included the kitchen area and living area. The optimized plan was the core area of the kitchen operation center integrated the water tank, multifunction table, and user's behavior line in the kitchen and living space, which was maximized to meet the needs of the user group [41]. Erculiani et al employed a perceptron learning algorithm to arrange tables in a room by choosing a recommendation to synthesize a novel configuration [38].…”

Section: Related Study Of Spatial Planning With An Algorithmmentioning

confidence: 99%

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Optimal Planning Method for Large-Scale Historical Exhibits in the Taiwan Railway Museum

Fan

Chu

2021

Applied Sciences

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The curation design of cultural heritage sites, such as museums, influence the level of visitor satisfaction and the possibility of revisitation; therefore, an efficient exhibit layout is critical. The difficulty of determining the behavior of visitors and the layout of galleries means that exhibition layout is a knowledge-intensive, time-consuming process. The progressive development of machine learning provides a low-cost and highly flexible workflow in the management of museums, compared to traditional curation design. For example, the facility’s optimal layout, floor, and furniture arrangement can be obtained through the repeated adjustment of artificial intelligence algorithms within a relatively short time. In particular, an optimal planning method is indispensable for the immense and heavy trains in the railway museum. In this study, we created an innovative strategy to integrate the domain knowledge of exhibit displaying, spatial planning, and machine learning to establish a customized recommendation scheme. Guided by an interactive experience model and the morphology of point–line–plane–stereo, we obtained three aspects (visitors, objects, and space), 12 dimensions (orientation, visiting time, visual distance, centrality, main path, district, capacity, etc.), 30 physical principles, 24 suggestions, and five main procedures to implement layout patterns and templates to create an exhibit layout guide for the National Railway Museum of Taiwan, which is currently being transferred from the railway workshop for the sake of preserving the rail culture heritage. Our results are suitable and extendible to different museums by adjusting the criteria used to establish a new recommendation scheme.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Related Study Of Spatial Planning With An Algorithmmentioning

confidence: 99%

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Optimal Planning Method for Large-Scale Historical Exhibits in the Taiwan Railway Museum

Fan

Chu

2021

Applied Sciences

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“…Depending on the age group, kitchen layout design affects cooking and dining experiences. Food behaviors are influenced by features including size, connectivity to other areas, and safety features, underscoring the need for more research [51]. These factors should be considered to enhance productivity and design effective incentives and rewards for improving performance [52].…”

Section: Summary On the Level Of Kitchen Layout Designmentioning

confidence: 99%

Kitchen Layout Design and Individual Work Performance of Eatery in Davao Del Norte, Philippines

Aparece,

Ayop,

Ruz

et al. 2024

S. Asian J. Soc. Stud. Econ.

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This study assessed the level of kitchen layout design and individual work performance in eateries in Davao del Norte, Philippines. A descriptive-correlational research design was employed in this study to determine the relationship between the two variables. The respondents of this study were 207 employees of eateries in Davao del Norte, selected through a simple random sampling technique. Two adapted questionnaires were used, and the statistical tools included mean, Pearson r, and standard deviation. The findings of this study revealed that the level of kitchen layout design in terms of lighting, space or arrangement of kitchen equipment, and type of flooring was very high. The investigation also found that the level of individual work performance in terms of task performance, contextual performance, and counterproductive work behavior was very high, indicating that these behaviors were always manifest. Additionally, the study found a significant relationship between kitchen layout design and individual work performance, with an r value of 0.781 and a p-value of 0.000. Therefore, it was recommended to select a suitable kitchen layout design in eateries to enhance individual performance and workplace productivity. Future researchers were encouraged to continue exploring the space or arrangement of kitchen equipment to better manage kitchens and develop creative technologies for improved comprehension in these areas. Enhancing the study of task and contextual performance was suggested to emphasize human factor considerations and a holistic approach to boost productivity outcomes.

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“…Therefore, it is only suitable for the optimization of components, but not for the optimization of large structural systems. Recently, heuristic algorithms like GA and simulated annealing algorithm (SA) have emerged in the eld of engineering structure optimization design [25]. GA has no requirement for continuous differentiability of the optimization model, and it can search the global optimum solution, so it has been widely used.…”

Section: Literature Reviewmentioning

confidence: 99%

Research on building truss design based on particle swarm intelligence optimization algorithm

Sun

Shabaz

et al. 2021

Int J Syst Assur Eng Manag

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Optimization methodologies are being utilized in various structural designing practices to solve size, shape and topology optimization problems. A heuristic Particle swarm optimization (HPSO) algorithm was anticipated in this article in order to address the size optimization problem of truss with stress and displacement constraints. This article contributes in improvisation in the truss structure design rationality while reducing the engineering cost by proposing the HPSO approach. Primarily, the basic principle of the original PSO algorithm is presented, then the compression factor is established to improve the PSO algorithm, and a reasonable parameter setting value is presented. To validate the performance of the proposed optimization approach, various experimental illustrations were performed. The results show that the convergence history of experimental illustration 2 and experimental illustration 3 is optimal. The experimental illustration 2 converges after about 150 iterations, however, the experimental illustration 3 is close to the optimal solution after about 500 iterations. Therefore, the PSO algorithm can successfully optimize the size design of truss structures, and the algorithm is also time e cient. The improved PSO algorithm has good convergence and stability, and can effectively optimize the size design of truss structures.

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Integrated kitchen design and optimization based on the improved particle swarm intelligent algorithm

Cited by 8 publications

References 5 publications

Optimal Planning Method for Large-Scale Historical Exhibits in the Taiwan Railway Museum

Optimal Planning Method for Large-Scale Historical Exhibits in the Taiwan Railway Museum

Kitchen Layout Design and Individual Work Performance of Eatery in Davao Del Norte, Philippines

Research on building truss design based on particle swarm intelligence optimization algorithm

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