BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//EURO Practitioners&#039; Forum - ECPv6.15.13.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:EURO Practitioners&#039; Forum
X-ORIGINAL-URL:https://www.euro-online.org/websites/or-in-practice
X-WR-CALDESC:Events for EURO Practitioners&#039; Forum
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:UTC
BEGIN:STANDARD
TZOFFSETFROM:+0000
TZOFFSETTO:+0000
TZNAME:UTC
DTSTART:20230101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=UTC:20240607T150000
DTEND;TZID=UTC:20240607T160000
DTSTAMP:20260405T162326
CREATED:20240605T215712Z
LAST-MODIFIED:20240605T220110Z
UID:1631-1717772400-1717776000@www.euro-online.org
SUMMARY:Three model problem: Combining machine learning (ML) and operations research (OR) through horizontal computing
DESCRIPTION:Speaker: Ryan O’Neil\, CTO and Co-Founder of Nextmv \nMore and more\, data science and decision science practitioners are seeking to combine machine learning forecasts with actionable and optimized decisions. This can include anything from predicting traffic patterns for delivery scheduling to consumer buying behavior for inventory management. But bridging these two disciplines can be challenging. \nIn the on-demand logistics space\, these worlds are colliding more frequently with practitioners generating demand forecasts that feed into shift scheduling models that feed into vehicle routing models. Getting to an 80% good solution for the optimization side is not hard. What is hard is the remaining 20% where people tend to over-optimize their models using fixed inputs (which makes the model more brittle in the face of uncertainty). What if\, instead\, we could take those 80% solutions and use horizontal compute to scale them up in the face of uncertainty? \nIn this talk\, we will explore what has made blending ML and OR outputs challenging\, the roles of deterministic and stochastic optimization in relation to ML\, and how scenario testing techniques via horizontal computing provide an expedient and more accessible path for combining these worlds. \n\n\n\n\nAbout the speaker:\n\n\n\n\nRyan O’Neil is CTO and cofounder of Nextmv (nextmv.io). Previously\, he led the Decision Engineering department at Grubhub and Zoomer\, which owned forecasting\, scheduling\, routing\, and simulation. Ryan worked as an Operations Research Analyst at MITRE\, and led software teams at The Washington Post\, Yhat\, and Polimetrix. During this\, he earned a PhD in Operations Research at George Mason University\, and wrote his dissertation on real time routing for pickup and delivery problems.‍ \n\n\n\n\n\n\n\n\n\n\n\nEURO Practitioners’ Forum past and planned activities are available to the Forum members\, as well as the wider public. \nVisit the website and register as a member for free\, to get the regular updates on all activities: EPF Member registration page. The recordings and details from previous webinars are also available on this website. \nFollow the Forum on Twitter and LinkedIN \, and feel free to get in touch. \n\n\n\n\n\n\n\n\n\n\n\nEURO Practitioners’ Forum webinars organisers: Sofiane Oussedik (IBM)\, Joaquim Gromicho (ORTEC)\, Torkel Haufmann (Sintef)\, Adisa Mujezinovic \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nTags
URL:https://www.euro-online.org/websites/or-in-practice/event/three-model-problem-combining-machine-learning-ml-and-operations-research-or-through-horizontal-computing/
END:VEVENT
END:VCALENDAR