A Facility Layout Planner tool based on Genetic Algorithms

Pinto, Rui; Gonçalves, Joana L.; Cardoso, Henrique Lopes; Oliveira, Eugénio; Gonçalves, Gil; Carvalho, Bruno

doi:10.1109/ssci.2016.7850280

Cited by 8 publications

(7 citation statements)

References 27 publications

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“…AI-based smart systems have been demanded in complex environments where new approaches are needed to achieve optimal performance. For these purposes, it has been observed that different evolutionary computation techniques [108] (genetic algorithms [109], swarm intelligence [110], discrete differential evolution algorithms [111], neuroevolutionary learning techniques [112]) obtain good results and improve the multi-criteria optimization problems that take place in such extreme contexts [113], by providing a holistic approach, taking into consideration not only parameters but also topologies and rules.…”

Section: Deep Learningmentioning

confidence: 99%

Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort

Márquez-Sánchez,

Calvo-Gallego,

Erbad

et al. 2023

Electronics

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Nowadays, in contemporary building and energy management systems (BEMSs), the predominant approach involves rule-based methodologies, typically employing supervised or unsupervised learning, to deliver energy-saving recommendations to building occupants. However, these BEMSs often suffer from a critical limitation—they are primarily trained on building energy data alone, disregarding crucial elements such as occupant comfort and preferences. This inherent lack of adaptability to occupants significantly hampers the effectiveness of energy-saving solutions. Moreover, the prevalent cloud-based nature of these systems introduces elevated cybersecurity risks and substantial data transmission overheads. In response to these challenges, this article introduces a cutting-edge edge computing architecture grounded in virtual organizations, federated learning, and deep reinforcement learning algorithms, tailored to optimize energy consumption within buildings/homes and facilitate demand response. By integrating energy efficiency measures within virtual organizations, which dynamically learn from real-time inhabitant data while prioritizing comfort, our approach effectively optimizes inhabitant consumption patterns, ushering in a new era of energy efficiency in the built environment.

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Section: Deep Learningmentioning

confidence: 99%

Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort

Márquez-Sánchez,

Calvo-Gallego,

Erbad

et al. 2023

Electronics

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“…Dong et al [15] applied the simulation engine to establish the three-dimensional model of the equipment and the layout of the workshop to realize the combination of virtual and reality. Pinto et al [16] studied the layout of the workshop with ReBORN Workbench and a genetic algorithm. Liao et al [17] established the three-dimensional model on Visual Components and designed the layout of the workshop with the systematic layout planning (SLP) method.…”

Section: Related Studiesmentioning

confidence: 99%

Digital Twins-Based Production Line Design and Simulation Optimization of Large-Scale Mobile Phone Assembly Workshop

Zhao

Zou

et al. 2022

Machines

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The mobile phone is a typical 3C electronic product characterized by frequent replacement, multiple product specifications, high flexibility, high-frequency production line switching, and urgent delivery time during production. Therefore, the optimized design of the mobile phone production workshop is crucial. This paper takes the assembly process of a specific type of mobile phone assembly as the research object and adopts the heuristic balance method to combine the production procedures. Moreover, it considers the automation degree of the process and the demand for production line rhythm to carry out station division and working hours design for the assembly process. The advantages and disadvantages of the plug-and-play production line and unit production line architecture are integrated, aiming at the production line’s construction cost and unit area capacity. A hybrid workshop with a mixed combination of two types of production lines is designed and an optimization model of hybrid workshop design is established. The semi-physical simulation technology of digital twins is utilized to verify the proposed design scheme to achieve the balance optimization of the production line, improve production efficiency, and reduce production costs. This work provides a technical scheme for designing and optimizing large-scale mobile phone assembly workshops with multi-batch and high-frequency production changes.

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“…In this paper, the genetic algorithm is used to solve the model. The genetic algorithm can be independent of the specific field of the problem and has strong robustness to this type of the problem [45][46][47][48][49][50]. Therefore, the genetic algorithm can solve the layout problem of the cabin equipment.…”

Section: Genetic Algorithm Designmentioning

confidence: 99%

Optimum Design of Ship Cabin Equipment Layout Based on SLP Method and Genetic Algorithm

Guo

Zhang

et al. 2019

Mathematical Problems in Engineering

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The engine room is the heart of a ship, and almost all of the main electromechanical equipment that supports the work on board can be found here. Finding a way to arrange the equipment in a small cabin space is an essential factor in the design and construction of a ship. However, in existing research, when an intelligent algorithm is used to optimize the design of a cabin, the established mathematical model is not comprehensive and the solution has not been evaluated. The optimal solution obtained is not feasible for the actual design of a ship. This can lead to unnecessary redesign work, which seriously affects design efficiency and increases design costs. In order to solve the above problems, this paper innovatively refers to a Systematic Layout Planning (SLP) method (normally applied to the layout of plant equipment) to the cabin equipment layout issue. The SLP method is used to quantitatively analyze the adjacency and logistics relationship between devices, and the mutual integration relationship between devices is obtained so that a preliminary layout scheme can be retrieved. The problem model is constructed by considering various factors such as the comprehensive relationship between the equipment and the stability of the cabin, and the corresponding objective function and constraint function are established to further design the variables, operators, and steps of the genetic algorithm. The initial solution obtained from the SLP method is used as part of an initial solution to the genetic algorithm, and the genetic algorithm is used to optimize the problem. Finally, the Analytic Hierarchy Process (AHP) is used to evaluate and optimize several groups of better schemes obtained by running multiple genetic algorithms and select the better schemes. The experimental design proves that the integrated design method has certain feasibility and superiority.

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A Facility Layout Planner tool based on Genetic Algorithms

Cited by 8 publications

References 27 publications

Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort

Enhancing Building Energy Management: Adaptive Edge Computing for Optimized Efficiency and Inhabitant Comfort

Digital Twins-Based Production Line Design and Simulation Optimization of Large-Scale Mobile Phone Assembly Workshop

Optimum Design of Ship Cabin Equipment Layout Based on SLP Method and Genetic Algorithm

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