OR in Practice

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Thanks to all great developments in mathematics and computing power, we are able to solve more complex problems within a reasonable amount of time. Machine learning and other AI-techniques can boost the power of OR. But are we also able to explain the outcome to the industry? Optimal is not always logical, which leads to challenges both in modeling and in implementation. In this presentation I like to share my thoughts, based on my professorship “Logistic Optimization” at Tilburg University and my long experience (in parallel) as “Optimization Evangelist” at ORTEC. ORTEC is a global and leading partner in data-driven decision support.

Goos Kant is a full professor of Logistic Optimization at Tilburg University. He is involved in the Master program of Business Analytics and Operations Research, as well as in the master program Data Science &amp; Entrepreneurship at Jheronimus Academy of Data Science. He is the project leader of a large R&amp;D project on horizontal collaboration in logistics. Goos is also managing partner at ORTEC, active as “Optimization Evangelist” in various (logistic) industries. Goos is involved in courses from MBA-schools TIAS and Nyenrode. He holds both an MSc and a PhD in Computer Science.

EWG POR webinars organisers: Sofiane Oussedik (IBM), Joaquim Gromicho (ORTEC), Patrick Schittekat (SINTEF), Gavin Bell (Optimeering), Adisa Mujezinovic

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OR in Practice

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OR in Practice

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