<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Publications on Kenny F.</title><link>https://kennyfh.eu/publications/</link><description>Recent content in Publications on Kenny F.</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>kflores1@us.es (Kenny Flores)</managingEditor><webMaster>kflores1@us.es (Kenny Flores)</webMaster><copyright>© 2026 Kenny Flores</copyright><atom:link href="https://kennyfh.eu/publications/index.xml" rel="self" type="application/rss+xml"/><item><title>Discrete Event Simulation to Support Production Planning in Real Systems</title><link>https://kennyfh.eu/publications/munozsimulation2025/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><author>kflores1@us.es (Kenny Flores)</author><guid>https://kennyfh.eu/publications/munozsimulation2025/</guid><description/><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://kennyfh.eu/publications/munozsimulation2025/featured.png"/></item><item><title>Enhancing Lead Time Prediction in Wind Tower Manufacturing: A ML Approach Compared to Traditional Engineering Models</title><link>https://kennyfh.eu/publications/floreslwcomparison2025/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><author>kflores1@us.es (Kenny Flores)</author><guid>https://kennyfh.eu/publications/floreslwcomparison2025/</guid><description/><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://kennyfh.eu/publications/floreslwcomparison2025/featured.png"/></item><item><title>Enhancing process lead time forecasting with machine learning and upstream process data: A case study in wind tower manufacturing</title><link>https://kennyfh.eu/publications/florescaie2025/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><author>kflores1@us.es (Kenny Flores)</author><guid>https://kennyfh.eu/publications/florescaie2025/</guid><description/><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://kennyfh.eu/publications/florescaie2025/featured.png"/></item><item><title>Evaluating Socio-Economic Drivers of Service Completion in Last-Mile and First-Mile Reverse Logistics</title><link>https://kennyfh.eu/publications/lorenzolastmile2025/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><author>kflores1@us.es (Kenny Flores)</author><guid>https://kennyfh.eu/publications/lorenzolastmile2025/</guid><description/><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://kennyfh.eu/publications/lorenzolastmile2025/featured.png"/></item><item><title>Lead-Time Prediction in Wind Tower Manufacturing: A Machine Learning-Based Approach</title><link>https://kennyfh.eu/publications/flores2024/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><author>kflores1@us.es (Kenny Flores)</author><guid>https://kennyfh.eu/publications/flores2024/</guid><description/><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://kennyfh.eu/publications/flores2024/featured.png"/></item><item><title>Machine Learning Techniques to Predict Process Time in Operations with High Variability</title><link>https://kennyfh.eu/publications/floresspringer2025/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><author>kflores1@us.es (Kenny Flores)</author><guid>https://kennyfh.eu/publications/floresspringer2025/</guid><description/><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://kennyfh.eu/publications/floresspringer2025/featured.png"/></item><item><title>Metodología de determinación de horas de entregas más convenientes en comercio electrónico a través de la caracterización de perfiles de usuario mediante aplicación móvil</title><link>https://kennyfh.eu/publications/escuderolastmile2025/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><author>kflores1@us.es (Kenny Flores)</author><guid>https://kennyfh.eu/publications/escuderolastmile2025/</guid><description/><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://kennyfh.eu/publications/escuderolastmile2025/featured.png"/></item></channel></rss>