Papers by Rogelio Florencia
Engineering Applications of Artificial Intelligence, 2022
This paper presents Outranking-based Particle Swarm Optimisation (O-PSO) a novel metaheuristic to... more This paper presents Outranking-based Particle Swarm Optimisation (O-PSO) a novel metaheuristic to address the multi-objective Unrelated Parallel Machine Scheduling Problem. It is a particle swarm optimisation algorithm enriched with the preferences of the Decision Maker (DM), articulated in a fuzzy relational system based on ELECTRE III. Unlike other multi-objective metaheuristics, O-PSO searches for the Region of Interest (RoI) instead of approximating a sample of the complete Pareto frontier. The RoI is the subset consisting of those Pareto-efficient solutions that satisfy the outranking relations, that is, they are the best solutions in terms of the DM's system of preferences. Therefore, O-PSO not only approximates the Pareto solutions but also supports multicriteria decision analysis of the schedules. The efficiency of O-PSO is validated on a benchmark of synthetic instances from the scientific literature, where the Wilcoxon rank-sum test provides statistical evidence that O-PSO offers high-quality solutions when compared with two state-of-the-art metaheuristics; specifically, O-PSO is capable of generating a greater proportion of solutions (on average, ranging from 7% to 14%) dominating those of the state-of-the-art algorithms, as well as finding more solutions (from 13% to 18%) that satisfy the DM's preferences. O-PSO is also applied to a real-world case study in the transport industry to provide evidence for its applicability.
Advances in Human Resources Management and Organizational Development, 2019
These days the human factor is key to improving order picking processes. For example, in a line o... more These days the human factor is key to improving order picking processes. For example, in a line of processes to replace different parts of equipment or devices, it is necessary to find the best route in each case to minimize the time consumed. One of the tools used to search for solutions are the metaheuristic methods that use probability to find the best solution in the required time. One of them is the “bat algorithm” that has had outstanding results since its appearance in 2010. This metaheuristic is based on how bats hunt, supported by their system of echolocation. It is one of the most recent metaheuristics and has proven to be more effective than other similar optimization algorithms in different processes. However, the algorithm was initially designed for addressing continuous problems. In this chapter, the authors present an adaptation of bat algorithms to solve a discrete problem: order picking.
Res. Comput. Sci., 2020
Kickstarter has become Independent Game Developers’ main means of projects financing. This platfo... more Kickstarter has become Independent Game Developers’ main means of projects financing. This platform has allowed the founding of around 2,500 videogame projects. Despite this being a considerable
Advances in Human Resources Management and Organizational Development, 2019
Mobile ateliers, also called pop-up stores, sell their products away from their warehouse. Theref... more Mobile ateliers, also called pop-up stores, sell their products away from their warehouse. Therefore, it causes them to go back to it once a product in their mobile store runs out since the customer is waiting for them at the store. The need to spend as little time searching for the product at the warehouse is of the utmost importance. To solve this problem, the authors have decided to attack it as an order picking problem. With the use of the elephant search algorithm, they aim to optimize the time it takes to retrieve the product needed to form the warehouse by giving the sales representative the optimum picking order route, so he can go in and out of the warehouse in as few steps as possible.
Information Sciences, 2021
Abstract This paper introduces an interactive approach to support multi-criteria decision analysi... more Abstract This paper introduces an interactive approach to support multi-criteria decision analysis of project portfolios. In high-scale strategic decision domains, scientific studies suggest that the Decision Maker (DM) can find help by using many-objective optimisation methods, which are supposed to provide values in the decision variables that generate high-quality solutions. Even so, DMs usually wish to explore the possibility of reaching some levels of benefits in some objectives. Consequently, they should repeatedly run the optimisation method. However, this approach cannot perform well – in an interactive way – for large instances under the presence of many objective functions. We present a mathematical model that is based on compromise programming and fuzzy outranking to aid DMs in analysing multi-criteria project portfolios on the fly. This approach allows relaxing the problem of rapidly optimising portfolios while preserving the beneficial properties of the DM’s preferences expressed by outranking relations. Our model supports the decision analysis on two instance benchmarks: for the first one, a better compromise solution was generated 84% of the runs; for the second one, this ranged from 93% to 97%. Our model was also applied to a real-world problem involving social projects, obtaining satisfactory results.
Applied Sciences, 2020
'El Diario de Juárez' is a local newspaper in a city of 1.5 million Spanish-speaking inhabitants ... more 'El Diario de Juárez' is a local newspaper in a city of 1.5 million Spanish-speaking inhabitants that publishes texts of which citizens read them on both a website and an RSS (Really Simple Syndication) service. This research applies natural-language-processing and machine-learning algorithms to the news provided by the RSS service in order to classify them based on whether they are about a traffic incident or not, with the final intention of notifying citizens where such accidents occur. The classification process explores the bag-of-words technique with five learners (Classification and Regression Tree (CART), Naïve Bayes, kNN, Random Forest, and Support Vector Machine (SVM)) on a class-imbalanced benchmark; this challenging issue is dealt with via five sampling algorithms: synthetic minority oversampling technique (SMOTE), borderline SMOTE, adaptive synthetic sampling, random oversampling, and random undersampling. Consequently, our final classifier reaches a sensitivity of 0.86 and an area under the precision-recall curve of 0.86, which is an acceptable performance when considering the complexity of analyzing unstructured texts in Spanish.
Research in Computing Science, 2012
In this paper, an educational software used to enhance the skills of children in the area of math... more In this paper, an educational software used to enhance the skills of children in the area of math is presented. This software has a tutor who guides the child in different activities. The tutor exhibits personality traits and emotions in its dialogue to create a sense of immersion in the student and catch his attention towards the game. We adapted an Emotional Extension of the Artificial Intelligence Markup Language, structure here named as EE-AIML.
Mathematical Problems in Engineering, 2018
One of the main concerns in Multicriteria Decision Aid (MCDA) is robustness analysis. Some of the... more One of the main concerns in Multicriteria Decision Aid (MCDA) is robustness analysis. Some of the most important approaches to model decision maker preferences are based on fuzzy outranking models whose parameters (e.g., weights and veto thresholds) must be elicited. The so-called preference-disaggregation analysis (PDA) has been successfully carried out by means of metaheuristics, but this kind of works lacks a robustness analysis. Based on the above, the present research studies the robustness of a PDA metaheuristic method to estimate model parameters of an outranking-based relational system of preferences. The method is considered robust if the solutions obtained in the presence of noise can maintain the same performance in predicting preference judgments in a new reference set. The research shows experimental evidence that the PDA method keeps the same performance in situations with up to 10% of noise level, making it robust.
Studies in Computational Intelligence, 2014
ABSTRACT Our research is focused on the implementation of a Natural Language Interface to Databas... more ABSTRACT Our research is focused on the implementation of a Natural Language Interface to Database. We propose the use of ontologies to model the knowledge required by the interface with the aim of correctly answering natural language queries and facilitate its configuration on other databases. The knowledge of our interface is composed by modeling information about the database schema, its relationship to natural language and some linguistic functions. The design of this modeling allows users to configure the interface without performing complex and tedious tasks, facilitating its portability to other databases. To evaluate the knowledge-domain portability, we configured our interface and the commercial interface ELF in the Northwind database. The results obtained of the experimentation show that the knowledge modeled in our interface allowed it to achieve a good performance.
Lecture Notes in Computer Science, 2010
This paper is based on a project at the University of Barcelona to develop the skills to diagnose... more This paper is based on a project at the University of Barcelona to develop the skills to diagnose the Generalized Anxiety Disorder (GAD) in students of psychology and psychiatry using a chatbot. The problem we address in this paper is to convert a chatbot in an emotional conversational agent capable of generating a believable and dynamic dialogue in natural language. For it, the dialogues convey traits of personality, emotions and its intensity. We propose to make an AIML language extension for the generation of believable ...
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Papers by Rogelio Florencia