Niching method

The Unequal Area Facility Layout Problem (UA-FLP) has been addressed using several methods. However, the UA-FLP has only been solved for criteria that can be quantified. Our approach includes subjective features in the UA-FLP, which are difficult to take into account with a more classical heuristic optimization. In this respect, we propose an Interactive Genetic Algorithm (IGA) that allows an interaction between the algorithm and the Decision Maker (DM). Involving the DM's knowledge in the approach guides the search process, adjusting it to his/her preferences at each generation of the algorithm. In this paper, we are concerned with assisting the DM in finding a good solution according with criteria that can be: subjective, unknown at the beginning or changed during the process, so that, the problem addressed differs from a classic optimization problem. In order to avoid overloading the DM, the whole population is classified into clusters by the fuzzy c-means clustering algorithm and only one representative element of each cluster is directly evaluated by the DM. A memory of the best solutions chosen by the DM is kept as a reference. The tests carried out show that the proposed IGA is capable of capturing DM preferences.

Gravitational Search Algorithm (GSA) is a metaheuristic for solving unimodal problems. In this paper, a K-means based GSA (KGSA) for multimodal optimization is proposed. This algorithm incorporates K-means and a new elitism strategy called “loop in loop” into the GSA. First in KGSA, the members of the initial population are clustered by K-means. Afterwards, new population is created and divided in different niches (or clusters) to expand the search space. The “loop in loop” technique guides the members of each niche to the optimum direction according to their clusters. This means that lighter members move faster towards the optimum direction of each cluster than the heavier members. For evaluations, KGSA is benchmarked on well-known functions and is compared with some of the state-of-the-art algorithms. Experiments show that KGSA provides better results than the other algorithms in finding local and global optima of constrained and unconstrained multimodal functions.

Gravitational Search Algorithm (GSA) is a metaheuristic for solving unimodal problems. In this paper, a K-means based GSA (KGSA) for multimodal optimization is proposed. This algorithm incorporates K-means and a new elitism strategy called “loop in loop” into the GSA. First in KGSA, the members of the initial population are clustered by K-means. Afterwards, new population is created and divided in different niches (or clusters) to expand the search space. The “loop in loop” technique guides the members of each niche to the optimum direction according to their clusters. This means that lighter members move faster towards the optimum direction of each cluster than the heavier members. For evaluations, KGSA is benchmarked on well-known functions and is compared with some of the state-of-the-art algorithms. Experiments show that KGSA provides better results than the other algorithms in finding local and global optima of constrained and unconstrained multimodal functions.

Introducing expert knowledge into evolutionary algorithms for the facility layout design problem can provide better solutions than the mathematically optimal solutions by considering qualitative aspects in the design. However, this approach requires the direct intervention of a designer (normally called the decision maker) in the evolutionary algorithm that guides the search process to adjust it to his/her preferences. To do this, the designer scores each of the most representative designs of the population to avoid fatigue. The selection of the solutions to be presented for human assessment is crucial, so a small number of solutions that represents the characteristics of the population must be selected without losing the variability of the solutions. The novel hybrid system proposed in this study consists of an interactive genetic algorithm that is combined with two different niching methods to allow interactions between the algorithm and the expert designer. The inclusion of niching techniques into the approach allows for the preservation of diversity, which avoids presenting similar solutions to the designer in the same iteration of the algorithm. The proposed approach was tested using two case studies of facility layout designs. The results of the experiments, which successfully validate the approach, are presented, compared and discussed.

This paper proposes the use of ordinal regression for helping the evaluation of Unequal Area Facility Layouts generated by an interactive genetic algorithm. Using this approach, a model obtained taking into account some objective factors and the subjective evaluation of the experts is constructed. Ordinal regression is used in this case because of the ordinal ranking between the different possible evaluations of the facility layouts made by the experts: {very deficient, deficient, intermediate, good, very good}. To do so, we will also make an approximation to some of the most successful ordinal classification methods in the machine learning literature. The best model obtained will be used in order to guide the searching of a genetic algorithm for generating new facility layouts.

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.

Many approaches have been suggested to solve simula tion optimization problems. In classical problems, these approaches p rovide only one solution (optimal or “near optimal”), which is the one that gave the best results on a given performance criterion, on the basis of the si mulation experiments performed. In such types of problems as design prob lems, this “best solution” may not be considered as the most suited for design makers. Indeed, other considerations as the measured performance can have to be taken into account (e. g., certain solutions can be more difficult to implement, or may induce supplementary costs); the simulation model cannot i corporate all the elements that are important to the decision maker. To provid e decision makers with sufficient flexibility in their final choice, we su ggest to provide several efficient solutions (the best found and some other local opti ma with acceptable performance) instead of only one. Then, the decisio n makers can make their choic...

In this paper, an ant colony optimization (ACO) approach is proposed to solve the Facility Layout Problem (FLP) with unequal area departments. The flexible bay structure (FBS) is relaxed by allowing empty spaces in bays, which results in more flexibility while assigning departments in bays. The comparative results show that the ACO approach is very promising.

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 th...

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.

Gravitational Search Algorithm (GSA) is a metaheuristic for solving unimodal problems. In this paper, a K-means based GSA (KGSA) for multimodal optimization is proposed. This algorithm incorporates K-means and a new elitism strategy called “loop in loop” into the GSA. First in KGSA, the members of the initial population are clustered by K-means. Afterwards, new population is created and divided in different niches (or clusters) to expand the search space. The “loop in loop” technique guides the members of each niche to the optimum direction according to their clusters. This means that lighter members move faster towards the optimum direction of each cluster than the heavier members. For evaluations, KGSA is benchmarked on well-known functions and is compared with some of the state-of-the-art algorithms. Experiments show that KGSA provides better results than the other algorithms in finding local and global optima of constrained and unconstrained multimodal functions.

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 (MO-IGA) 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.

The Unequal Area Facility Layout Problem (UA-FLP) has been addressed using several methods. However, the UA-FLP has only been solved for criteria that can be quantified. Our approach includes subjective features in the UA-FLP, which are difficult to take into account with a more classical heuristic optimization. In this respect, we propose an Interactive Genetic Algorithm (IGA) that allows an interaction between the algorithm and the Decision Maker (DM). Involving the DM's knowledge in the approach guides the search process, adjusting it to his/her preferences at each generation of the algorithm. In this paper, we are concerned with assisting the DM in finding a good solution according with criteria that can be: subjective, unknown at the beginning or changed during the process, so that, the problem addressed differs from a classic optimization problem. In order to avoid overloading the DM, the whole population is classified into clusters by the fuzzy c-means clustering algorithm and only one representative element of each cluster is directly evaluated by the DM. A memory of the best solutions chosen by the DM is kept as a reference. The tests carried out show that the proposed IGA is capable of capturing DM preferences.

Introducing expert knowledge into evolutionary algorithms for the facility layout design problem can provide better solutions than the mathematically optimal solutions by considering qualitative aspects in the design. However, this approach requires the direct intervention of a designer (normally called the decision maker) in the evolutionary algorithm that guides the search process to adjust it to his/her
preferences. To do this, the designer scores each of the most representative designs of the population to avoid fatigue. The selection of the solutions to be presented for human assessment is crucial, so a small number of solutions that represents the characteristics of the population must be selected without losing the variability of the solutions. The novel hybrid system proposed in this study consists of an interactive genetic algorithm that is combined with two different niching methods to allow interactions between the
algorithm and the expert designer. The inclusion of niching techniques into the approach allows for the preservation of diversity, which avoids presenting similar solutions to the designer in the same iteration of the algorithm. The proposed approach was tested using two case studies of facility layout designs. The results of the experiments, which successfully validate the approach, are presented, compared and
discussed.