Laura Hervert-Escobar
Monterrey Institute of Technology and Higher Education
16 Papers
31 Citations
Laura Hervert-Escobar is an academic researcher from Monterrey Institute of Technology and Higher Education. The author has contributed to research in topics: Scheduling (production processes) & Scheduling (computing). The author has an hindex of 3, co-authored 16 publications.
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Papers
Production planning and scheduling optimization model: a case of study for a glass container company
TL;DR: An optimization model is proposed 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.
Territorial design optimization for business sales plan
TL;DR: A real life case study to design the sales territory for a business sales plan that enhances customer coverage, increases sales, fosters fair performance and rewards systems and lower travel cost is considered.
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Integrated Approach to Assignment, Scheduling and Routing Problems in a Sales Territory Business Plan
Laura Hervert-Escobar,Francisco Lpez-Ramos,Oscar A. Esquivel-Flores +2 more
- 01 Jun 2016
TL;DR: In this paper, the authors considered 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.
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Bayesian Based Approach Learning for Outcome Prediction of Soccer Matches
Laura Hervert-Escobar,Neil Hernandez-Gress,Timothy I. Matis +2 more
- 11 Jun 2018
TL;DR: A Bayesian Model based on rank position and shared history that predicts the outcome of future soccer matches is proposed that was tested using a data set containing the results of over 200,000 soccer matches from different soccer leagues around the world.
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Predictive Analytics with Factor Variance Association
Raul V. Ramirez-Velarde,Laura Hervert-Escobar,Neil Hernandez-Gress +2 more
- 12 Jun 2019
TL;DR: A Predictive Factor Variance Association (PFVA) is proposed to solve a multi-class classification problem and is robust to execute different processes simultaneously without fail as well as the accuracy of the results.
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