Facility layout design using a multi-objective interactive genetic algorithm to support the DM

The unequal area facility layout problem (UA-FLP) has been addressed by many methods. Most of them only take aspects that can be quantified into account. This contribution presents a novel approach, which considers both quantitative aspects and subjective features. To this end, a multi-objective interactive genetic algorithm is proposed with the aim of allowing interaction between the algorithm and the human expert designer, normally called the decision maker (DM) in the field of UA-FLP. The contribution of the DM's knowledge into the approach guides the complex search process, adjusting it to the DM's preferences. The entire population associated to facility layout designs is evaluated by quantitative criteria in combination with an assessment prepared by the DM, who gives a subjective evaluation for a set of representative individuals of the population in each iteration. In order to choose these individuals, a soft computing clustering method is used. Two interesting real-world data sets are analysed to empirically probe the robustness of these models. The first UA-FLP case study describes an ovine slaughterhouse plant and the second, a design for recycling carton plant. Relevant results are obtained, and interesting conclusions are drawn from the application of this novel intelligent framework

A Multi-User Interactive Coral Reef Optimization Algorithm for Considering Expert Knowledge in the Unequal Area Facility Layout Problem

The problem of Unequal Area Facility Layout Planning (UA-FLP) has been addressed by a large number of approaches considering a set of quantitative criteria. Moreover, more recently, the personal qualitative preferences of an expert designer or decision-maker (DM) have been taken into account too. This article deals with capturing more than a single DM’s personal preferences to obtain a common and collaborative design including the whole set of preferences from all the DMs to obtain more complex, complete, and realistic solutions. To the best of our knowledge, this is the first time that the preferences of more than one expert designer have been considered in the UA-FLP. The new strategy has been implemented on a Coral Reef Optimization (CRO) algorithm using two techniques to acquire the DMs’ evaluations. The first one demands the simultaneous presence of all the DMs, while the second one does not. Both techniques have been tested over three well-known problem instances taken from the literature and the results show that it is possible to obtain sufficient designs capturing all the DMs’ personal preferences and maintaining low values of the quantitative fitness function

A novel multi-objective Interactive Coral Reefs Optimization algorithm for the Unequal Area Facility Layout Problem

The Unequal Area Facility Layout Problem (UA-FLP) has been widely analyzed in the literature using several heuristics and meta-heuristics to optimize some qualitative criteria, taking into account different restrictions and constraints. Nevertheless, the subjective opinion of the designer (Decision Maker, DM) has never been considered along with the quantitative criteria and restrictions. This work proposes a novel approach for the UA-FLP based on an Interactive Coral Reefs Optimization (ICRO) algorithm, which combines the simultaneous consideration of both quantitative and qualitative (DM opinion) features. The algorithm implementation is explained in detail, including the way of jointly considering quantitative and qualitative aspects in the fitness function of the problem. The experimental part of the paper illustrates the effect of including qualitative aspects in UA-FLP problems, considering three different hard UA-FLP instances. Empirical results show that the proposed approach is able to incorporate the DM preferences in the obtained layouts, without affecting much to the quantitative part of the solutions

Using eye-tracking into decision makers evaluation in evolutionary interactive UA-FLP algorithms

Unequal area facility layout problem is an important issue in the design of industrial plants, as well as other fields such as hospitals or schools, among others. While participating in an interactive designing process, the human user is required to evaluate a high number of proposed solutions, which produces them fatigue both mental and physical. In this paper, the use of eye-tracking to estimate user’s evaluations from gaze behavior is investigated. The results show that, after a process of training and data taking, it is possible to obtain a good enough estimation of the user’s evaluations which is independent of the problem and of the users as well. These promising results advice to use eye-tracking as a substitute for the mouse during users’ evaluations

A novel Island Model based on Coral Reefs Optimization algorithm for solving the unequal area facility layout problem

This paper proposes a novel approach to address the Unequal Area Facility Layout Problem (UA-FLP), based on the combination of both an Island Model and a Coral Reefs Optimization (CRO) algorithm. Two different versions of this Island Model based on Coral Reefs Optimization Algorithm (IMCRO) are proposed and applied to the UA-FLP. The structure of flexible bays has been selected as effective encoding to represent the facility layouts within the algorithm. The two versions of the proposed approach have been tested in 22 UA-FLP cases, considering small, medium and large size categories. The empirical results obtained are compared with previous state of the art algorithms, in order to show the performance of the IMCRO. From this comparison, it can be extracted that both versions of the proposed IMCRO algorithm show an excellent performance, accurately solving the UA-FLP instances in all the size categories

Learning from Prior Designs for Facility Layout Optimization

Peer reviewe

Gaps and requirements for applying automatic architectural design to building renovation

The renovation of existing buildings provides an opportunity to change the layout to meet the needs of facilities and accomplish sustainability in the built environment at high utilisation rates and low cost. However, building renovation design is complex, and completing architectural design schemes manually needs more efficiency and overall robustness. With the use of computational optimisation, automatic architectural design (AAD) can efficiently assist in building renovation through decision-making based on performance evaluation. This paper comprehensively analyses AAD's current research status and provides a state-of-the-art overview of applying AAD technology to building renovation. Besides, gaps and requirements of using AAD for building renovation are explored from quantitative and qualitative aspects, providing ideas for future research. The research shows that there is still much work to be done to apply AAD to building renovation, including quickly obtaining input data, expanding optimisation topics, selecting design methods, and improving workflow and efficiency

Evolutionary Computation Strategies applied to the UA-FLP

En la presente tesis doctoral se desarrollan dos aproximaciones distintas al problema de distribución en planta de áreas desiguales (UA-FLP). En primer lugar, se trata de incorporar el conocimiento del diseñador experto a los algoritmos clásicos de optimización, de forma que, además de buscar buenas soluciones desde el punto de vista cuantitativo, por ejemplo minimizando el flujo de materiales, se introduzca la posibilidad de que el diseñador aporte su experiencia y preferencias personales. Para facilitar la intervención humana en el proceso de búsqueda de soluciones, se ha utilizado un procedimiento de clustering, el cual permite clasificar las soluciones subyacentes en el conjunto de búsqueda, de forma que se presente al diseñador un número suficientemente representativo y, a la vez, evitándole una fatiga innecesaria. Además, en esta primera propuesta se han implementado dos técnicas de niching, denominadas Deterministic Crowding y Restricted Tournament Selection. Estas técnicas tienen la capacidad de mantener ciertas propiedades dentro de la población de soluciones, preservar múltiples nichos con soluciones cercanas a los óptimos locales, y reducir la probabilidad de quedar atrapado en ellos. De esta manera el algoritmo se enfoca simultáneamente en más de una región (nicho) en el espacio de búsqueda, lo cual es esencial para descubrir varios óptimos en una sola ejecución. Por otro lado, en la segunda aproximación al problema, se ha implementado una estrategia evolutiva paralela, muy útil para los problemas de alta complejidad en los que el tiempo de ejecución con un enfoque evolutivo secuencial es prohibitivo. La propuesta desarrollada, denominada IMGA, está basada en un algoritmo genético paralelo de grano grueso con múltiples poblaciones o islas. Este enfoque se caracteriza por evolucionar varias subpoblaciones independientemente, entre las que se intercambian individuos, haciendo posible explorar diferentes regiones del espacio de búsqueda, al mismo tiempo que se mantiene la diversidad de la población, permitiendo la obtención de buenas y diversas soluciones. Con ambas propuestas se han realizado experimentos que han arrojado resultados muy satisfactorios, encontrando buenas soluciones para un conjunto de problemas bien conocidos en la bibliografía. Estos buenos resultados han permitido la publicación de dos artículos indexados en el primer decil del ranking JCR (Journal Citation Reports).The present doctoral thesis develops two different approaches to the Unequal Area Facility Layout Problem (UA-FLP). The first approach encompasses the designer’s knowledge on classic optimization of algorithms in pursuance of good quantitative solutions (e.g. minimizing the materials flow) and also opens the possibility to include the contribution of the designer by means of his expertise and personal preferences. A clustering procedure has been used to facilitate human intervention in the process of finding solutions. This allows the underlying solutions to be classified in the search in order to present the designer with sufficiently representative solutions and, at the same time, avoiding unnecessary fatigue. In addition, two niching techniques have been implemented, called Deterministic Crowding and Restricted Tournament Selection. These techniques have the ability to maintain certain properties within the solutions space, preserve multiple niches with solutions close by local optimums, and reduce the probability of being trapped in them. In this way, the algorithm focuses simultaneously on more than one region (niche) in the search space, which is essential to discover several optimums in a single execution. The second approach to the problem comprises the implementation of a parallel evolutionary strategy. This method is useful for problems of high complexity in which the execution time using a sequential evolutionary approach is prohibitive. The proposal developed, called IMGA (Island Model Genetic Algorithm), is based on a parallel genetic algorithm of multiple-population coarse-grained. This is characterized by evolving several subpopulations independently among which individuals are exchanged. Different regions of the search space can be explored while the diversity of the population is maintained. Satisfactory and diverse solutions have been obtained as a result of this method. Experiments with both proposals have been carried out with satisfactory results, providing good solutions for a set of problems well known in the literature. These results were already published in two papers indexed in the first decile of the JCR (Journal Citation Reports) ranking

Artificial Intelligence Applied to Conceptual Design. A Review of Its Use in Architecture

Financiado para publicación en acceso aberto: Universidade da Coruña/CISUG[Abstract] Conceptual architectural design is a complex process that draws on past experience and creativity to generate new designs. The application of artificial intelligence to this process should not be oriented toward finding a solution in a defined search space since the design requirements are not yet well defined in the conceptual stage. Instead, this process should be considered as an exploration of the requirements, as well as of possible solutions to meet those requirements. This work offers a tour of major research projects that apply artificial intelligence solutions to architectural conceptual design. We examine several approaches, but most of the work focuses on the use of evolutionary computing to perform these tasks. We note a marked increase in the number of papers in recent years, especially since 2015. Most employ evolutionary computing techniques, including cellular automata. Most initial approaches were oriented toward finding innovative and creative forms, while the latest research focuses on optimizing architectural form.This project was supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431G/01, ED431D 2017/16), and the Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER)Xunta de Galicia; ED431G/01Xunta de Galicia; ED431D 2017/1

Solving the comfort-retrofit conundrum through post-occupancy evaluation and multi-objective optimisation

Developing appropriate building retrofit strategies is a challenging task. This case study presents a multi-criteria decision-supporting method that suggests optimal solutions and alternative design references with a range of diversity at the early exploration stage in building retrofit. This method employs a practical two-step method to identify critical comfort and energy issues and generate optimised design options with multi-objective optimisation based on a genetic algorithm. The first step is based on a post-occupancy evaluation, which cross-refers benchmarking and correlation and integrates them with non-linear satisfaction theory to extract critical comfort factors. The second step parameterises previous outputs as objectives to conduct building simulation practice. The case study is a typical post-war highly glazed open-plan office in London. The post-occupancy evaluation result identifies direct sunlight glare, indoor temperature, and noise from other occupants as critical comfort factors. The simulation and optimisation extract the optimal retrofit strategies by analysing 480 generated Pareto fronts. The proposed method provides retrofit solutions with a criteria-based filtering method and considers the trade-off between the energy and comfort objectives. The method can be transformed into a design-supporting tool to identify the key comfort factors for built environment optimisation and create sustainability in building retrofit. Practical application : This study suggested that statistical analysis could be integrated with parametric design tools and multi-objective optimisation. It directly links users’ subjective opinions to the final design solutions, suggesting a new method for data-driven generative design. As a quantitative process, the proposed framework could be automated with a program, reducing the human effort in the optimisation process and reducing the reliance on human experience in the design question defining and analysis process. It might also avoid human mistakes, e.g. overlooking some critical factors. During the multi-objective optimisation process, large numbers of design options are generated, and many of them are optimised at the Pareto front. Exploring these options could be a less human effort-intensive process than designing completely new options, especially in the early design exploration phase. Overall, this might be a potential direction for future study in generative design, which greatly reduce the technical obstacle of sustainable design for high building performance.</p