Papers by Laura Hervert Escobar
International Journal of Disaster Risk Reduction, 2021
Abstract In this study, we designed a geospatial and mathematical tool for the distribution of vo... more Abstract In this study, we designed a geospatial and mathematical tool for the distribution of volunteers during lockdowns to aid vulnerable groups in obtaining supplies, within the context of underdeveloped regions with insufficient resources and data. We established services proximity, senior-citizen population and marginalisation as crucial aspects of the model, which was developed in three stages: (1) assessing residential proximity to services by Voronoi diagram, (2) calculating the number of volunteers needed based on senior-citizen population and proximity to services, and (3) identifying the distress index of neighbourhoods for a sequential allocation of volunteers focused on equity. We applied the tool in the municipality of Atizapan de Zaragoza (Mexico) and identified the most-conservative scenario for volunteers without motorised transport attending to the entire senior-citizen population. The tool provides decision-support according to available resources and socioeconomic circumstances and ensures effective and equitable assistance to citizens during large-scale health contingencies.
2018 Seventeenth Mexican International Conference on Artificial Intelligence (MICAI), 2018
Humanitarian Logistics from the Disaster Risk Reduction Perspective, 2022
Lecture Notes in Computer Science, 2018
In the current world, sports produce considerable data such as players skills, game results, seas... more In the current world, sports produce considerable data such as players skills, game results, season matches, leagues management, etc. The big challenge in sports science is to analyze this data to gain a competitive advantage. The analysis can be done using several techniques and statistical methods in order to produce valuable information. The problem of modeling soccer data has become increasingly popular in the last few years, with the prediction of results being the most popular topic. In this paper, we propose a Bayesian Model based on rank position and shared history that predicts the outcome of future soccer matches. The model was tested using a data set containing the results of over 200,000 soccer matches from different soccer leagues around the world.
Computational Science – ICCS 2021, 2021
More than four out of 10 sports fans consider themselves soccer fans, making the game the world's... more More than four out of 10 sports fans consider themselves soccer fans, making the game the world's most popular sport. Sports are season based and constantly changing over time, as well, statistics vary according to the sport and league. Understanding sports communities in Social Networks and identifying fan's expertise is a key indicator for soccer prediction. This research proposes a Machine Learning Model using polarity on a dataset of 3,000 tweets taken during the last game week on English Premier League season 19/20. The end goal is to achieve a flexible mechanism, which automatizes the process of gathering the corpus of tweets before a match, and classifies its sentiment to find the probability of a winning game by evaluating the network centrality. Keywords: Graph theory • Machine learning • Sentiment analysis • Social networks • Sports analytics 1.1 Review on Social Network Analysis: Spread Influence Some research studies, as the one developed by Yan, [16] evaluate the influence of users, represented as nodes, on other entities under the Social Network
Advances in Soft Computing, 2017
Pricing is one of the most vital and highly demanded component in the mix of marketing along with... more Pricing is one of the most vital and highly demanded component in the mix of marketing along with the Product, Place and Promotion. An organization can adopt a number of pricing strategies, which usually will be based on corporate objectives. The purpose of this paper is to propose a methodology to define an optimal pricing strategy for convenience stores. The solution approach involves a multiple linear regression as well as a linear programming optimization model. To prove the value of the proposed methodology a pilot was performed for selected stores. Results show the value of the solution methodology. This model provides an innovative solution that allows the decision maker include business rules of their particular environment in order to define a price strategy that meet the objective business goals.
Lecture Notes in Computer Science, 2020
Typically, women are scored with a lower financial risk than men. However, the understanding of v... more Typically, women are scored with a lower financial risk than men. However, the understanding of variables and indicators that lead to such results, are not fully understood. Furthermore, the stochastic nature of the data makes it difficult to generate a suitable profile to offer an adequate financial portfolio to the women segment. As the amount, variety, and speed of data increases, so too does the uncertainty inherent within, leading to a lack of confidence in the results. In this research, machine learning techniques are used for data analysis. In this way, faster, more accurate results are obtained than in traditional models (such as statistical models or linear programming) in addition to their scalability.
Advances in Computational Intelligence, 2021
Lecture Notes in Computer Science, 2019
Organizations are turning to predictive analytics to help solve difficult problems and uncover ne... more Organizations are turning to predictive analytics to help solve difficult problems and uncover new opportunities. Nowadays, the processes are saturated in data, which must be used properly to generate the necessary key information in the decision making process. Although there are several useful techniques to process and analyze data, the main value starts with the treatment of key factors. In this way, a Predictive Factor Variance Association (PFVA) is proposed to solve a multi-class classification problem. The methodology combines well-known machine learning techniques along with linear algebra and statistical models to provide the probability that a particular sample belongs to a class or not. It can also give predictions based on regression for quantitative dependent variables and carry-out clustering of samples. The main contribution of this research is its robustness to execute different processes simultaneously without fail as well as the accuracy of the results.
Lecture Notes in Computer Science, 2017
This work presents a model for the Tramp Ship Scheduling problem including berth allocation consi... more This work presents a model for the Tramp Ship Scheduling problem including berth allocation considerations, motivated by a real case of a shipping company. The aim is to determine the travel schedule for each vessel considering multiple docking and multiple time windows at the berths. This work is innovative due to the consideration of both spatial and temporal attributes during the scheduling process. The resulting model is formulated as a mixed-integer linear programming problem, and a heuristic method to deal with multiple vessel schedules is also presented. Numerical experimentation is performed to highlight the benefits of the proposed approach and the applicability of the heuristic. Conclusions and recommendations for further research are provided.
Annals of Operations Research, 2018
Based on a case study, this paper deals with the production planning and scheduling problem of th... more Based on a case study, this paper deals with the production planning and scheduling problem of the glass container industry. This is a facility production system that has a set of furnaces where the glass is produced in order to meet the demand, being afterwards distributed to a set of parallel molding machines. Due to huge setup times involved in a color changeover, manufacturers adopt their own mix of furnaces and machines to meet the needs of their customers as flexibly and efficiently as possible. In this paper we proposed an optimization model that maximizes the fulfillment of the demand considering typical constraints from the planning production formulation as well as real case production constraints such as the limited product changeovers and the minimum run length in a machine. The complexity of the proposed model is assessed by means of an industrial real life problem.
Procedia Computer Science, 2017
This paper proposes a methodology to define an optimal pricing strategy for convenience stores ba... more This paper proposes a methodology to define an optimal pricing strategy for convenience stores based on dimension reduction methods and uncertainty of data. The solution approach involves a multiple linear regression (MLR) as well as a linear programming optimization model. Two strategies Principal Component Analysis (PCA) and Best Subset Regression (BSR) methods for the selection of a set of variables among a large number of predictors is presented. A linear optimization model then is solved using diverse business rules. To show the value of the proposed methodology optimal prices calculation results are compared with previous results obtained in a pilot performed for selected stores. This strategy provides an alternative solution that shows how a decision maker can include proper business rules of their particular environment in order to define a pricing strategy that meets business goals.
Procedia Computer Science, 2016
This paper considers a real life case study that determines the minimum number of sellers require... more This paper considers a real life case study that determines the minimum number of sellers required to attend a set of customers located in a certain region taking into account the weekly schedule plan of the visits, as well as the optimal route. The problem is formulated as a combination of assignment, scheduling and routing problems. In the new formulation, case studies of small size subset of customers of the above type can be solved optimally. However, this subset of customers is not representative within the business plan of the company. To overcome this limitation, the problem is divided into three phases. A greedy algorithm is used in Phase I in order to identify a set of cost-effective feasible clusters of customers assigned to a seller. Phase II and III are then used to solve the problem of a weekly program for visiting the customers as well as to determine the route plan using MILP formulation. Several real life instances of different sizes have been solved demonstrating the efficiency of the proposed approach.
International Journal of Machine Learning and Cybernetics, 2015
Radio frequency identification (RFID) is a technology with numerous benefits in applications wher... more Radio frequency identification (RFID) is a technology with numerous benefits in applications where objects have to be identified automatically. However, cost, fragile tags, collision and reading errors are some of issues to be concerned with in an RFID implementation. Mainly, this paper proposes a method for tag identification and a method for the selection of the binary codes to program on the tags in order to facilitate the identification process. For the identification method a heuristic based on Hamming distance is developed where the basic idea is to utilize the information obtained in consecutive read attempts to help identify a tag. For the selection method three models based on Hamming distance are also developed which strive to find the set with the greatest dissimilarity among the codes. Computer simulations are performed to verify the validity of the proposed methods.
Journal of Computational and Applied Mathematics, 2018
Procedia Computer Science, 2017
A well designed territory enhances customer coverage, increases sales, fosters fair performance a... more A well designed territory enhances customer coverage, increases sales, fosters fair performance and rewards systems and lower travel cost. This paper considers a real life case study to design a sales territory for a business sales plan. The business plan consists in assigning the optimal quantity of sellers to a territory including the scheduling and routing plans for each seller. The problem is formulated as a combination of assignment, scheduling and routing optimization problems. The solution approach considers a meta-heuristic using stochastic iterative projection method for large systems. Several real life instances of different sizes were tested with stochastic data to represent raise/fall in the customers demand as well as the appearance/loss of customers.
Annals of Operations Research, 2015
Radio Frequency Identification (RFID)is a technology most commonly used for the tracking and iden... more Radio Frequency Identification (RFID)is a technology most commonly used for the tracking and identification of objects. Optimally implementing RFID is challenging, especially in supply chain management where the use of passive tags is more common. Due to the nature of RF communications, many RFID systems involve multiple readers. Therefore, determining the number and position of reader antennas has a significant effect on success of the deployment. In this paper, we propose two optimization models and a GRASP metaheuristic that consider the effect of the orientation of antennas, the type of material to identify, and the interference from obstacles in a three-dimensional warehouse. The solution gives the minimal number of readers along with their positions for 100% coverage of the tagged items.
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Papers by Laura Hervert Escobar