Laura García-Hernández

Handling qualitative aspects in Unequal Area Facility Layout Problem: An Interactive Genetic Algorithm

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 DMs 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.

A novel hybrid evolutionary approach for capturing decision maker knowledge into the unequal area facility layout problem

We are concerned with an unequal area facility layout problem.Existing approaches are normally based on optimization, which can be insufficient in certain cases.Our interactive genetic algorithm involves the decision maker in the search for a suited solution.Our new approach based on niching methods is tested with two real problem cases. 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.

An island model genetic algorithm for unequal area facility layout problems

We are concerned with an unequal area facility layout problem.This paper is the first work to investigate the use of PGAs for solving the UA-FLP.We present a novel island model genetic algorithm for the UA-FLP.Our novel approach has been tested with a set of well-known problems. The Unequal Area Facility Layout Problem (UA-FLP) has been addressed by various methods, including mathematical modelling, heuristic and metaheuristic approaches. Nevertheless, each type of approach presents problems such as premature convergence, lack of diversity, or high computational cost. In this paper, for the first time, an Island Model Genetic Algorithm (IMGA) is proposed to solve these subjects in the UA-FLP. The parallel evolution of several populations is used to maintain the population diversity and to obtain a wider sampling of the search space to obtain better quality solutions in fewer generations. Our novel approach was tested with a well-known set of problems taken from the literature and the results were compared with those of previous reports. In most cases, the results obtained by our novel approach improved on the previous results. Additionally, the proposed approach is able to reach good solutions with a wide range of problem sizes and in a reasonable computational time.

PpcProject: An educational tool for software project management

The University degrees about engineering in general, and software engineering in particular, contain subjects related with Project Management. In this subject, the most commonly used software for practical classes and homework is MS-Project. Nevertheless, the students find that MS-Project is not easy to use and that it requires a high knowledge about Project Management and about the use of the software package. To fill this gap, a new educational software tool, called PpcProject, has been developed and introduced in Software Project Management classes in front of MS-Project. When assessed in practical classes, it was shown to be more useful for educational purposes than Microsoft Project both, in terms of usability and of students performance.

Recycling Plants Layout Design by Means of an Interactive Genetic Algorithm

Facility Layout Design is known to be very important for attaining production efficiency because it directly influences manufacturing costs, lead times, work in process and productivity. Facility Layout problems have been addressed using several approaches. Unfortunately, these approaches only take into account quantitative criteria. However, there are qualitative preferences referred to the knowledge and experience of the designer, which should also be considered in facility layout design. These preferences can be subjective, not known in advance and changed during the design process, so that, it is difficult to include them using a classic optimization approach. For that reason, we propose the use of an Interactive Genetic Algorithm (IGA) for designing the layout of two real recycling plants taking into consideration subjective features from the designer. The designers knowledge guides the evolution of the algorithm evaluating facility layouts in each generation adjusting the search to his/her preferen...

Encoding Structures and Operators Used in Facility Layout Problems with Genetic Algorithms

The allocation of facilities in a plant layout is a complex problem. For solving it, many authors have used Genetic Algorithms (GAs) with the objective of reaching an efficient plant layout design. To represent the plant layout design as a data structure, GAs require a defined encoding scheme. Such a structure defines the types of solutions that can be obtained, and influences the GA´s ability to find good solutions. There are a few surveys on facility layout problems, but they have not addressed evolutionary issues in depth. This work presents a review that focuses on encoding schemes and related operators used in GAs, and suggests a method of classifying the different encoding structures described in the bibliography. We also studied their main characteristics and objectives; and successfully identified the crossover and mutation operators that could be utilized depending on the type of encoding scheme.

The role of interdisciplinary research team in the impact of health apps in health and computer science publications: a systematic review

BackgroundSeveral studies have estimated the potential economic and social impact of the mHealth development. Considering the latest study by Institute for Healthcare Informatics, more than 165.000 apps of health and medicine are offered including all the stores from different platforms. Thus, the global mHealth market was an estimated

An evolutionary neural system for incorporating expert knowledge into the UA-FLP

10.5 billion in 2014 and is expected to grow 33.5 percent annually between 2015 and 2020s. In fact, apps of Health have become the third-fastest growing category, only after games and utilities.MethodsThis study aims to identify, study and evaluate the role of interdisciplinary research teams in the development of articles and applications in the field of mHealth. It also aims to evaluate the impact that the development of mHealth has had on the health and computer science field, through the study of publications in specific databases for each area which have been published until nowadays.ResultsInterdisciplinary nature is strongly connected to the scientific quality of the journal in which the work is published. This way, there are significant differences in those works that are made up by an interdisciplinary research team because of they achieve to publish in journals with higher quartiles. There are already studies that warn of methodological deficits in some studies in mHealth, low accuracy and no reproducibility. Studies of low precision and poor reproducibility, coupled with the low evidence, provide low degrees of recommendation of the interventions targeted and therefore low applicability.ConclusionsFrom the evidence of this study, working in interdisciplinary groups from different areas greatly enhances the quality of research work as well as the quality of the publications derived from its results.

Neural PCA and maximum likelihood hebbian learning on the GPU

This paper presents a hybrid system for incorporating human expert knowledge into the unequal area facility layout problem. A subset of facility designs is generated using a genetic algorithm and then evaluated by a human expert. The hybrid system consists of assigning a mark, where the principal aim is to substitute the human experts knowledge to avoid fatiguing or burdening him or her. The novel proposed approach was tested using a real case study of 365 facility layout designs for an ovine slaughterhouse. The validation phase of the intelligent model presented was performed using a new subset of 181 facility layout designs evaluated by a different human expert. The results of the experiment, which validate the proposed approach, are presented and discussed in this study.

An Interactive Genetic Algorithm with c-Means clustering for the Unequal Area Facility Layout Problem

This study introduces a novel fine-grained parallel implementation of a neural principal component analysis (neural PCA) variant and the maximum Likelihood Hebbian Learning (MLHL) network designed for modern many-core graphics processing units (GPUs). The parallel implementation as well as the computational experiments conducted in order to evaluate the speedup achieved by the GPU are presented and discussed. The evaluation was done on a well-known artificial data set, the 2D bars data set.