2021年5月7日,【明理講堂2021年第22期】有幸邀請(qǐng)到瑞士洛桑聯(lián)邦理工學(xué)院（EPFL）Daniel Kuhn教授帶來題為“Optimization in the Face of Uncertainty”的學(xué)術(shù)報(bào)告。報(bào)告采用騰訊會(huì)議的形式。管理與經(jīng)濟(jì)學(xué)院教師、研究生以及校外研究學(xué)者等300多名師生參加了本次講座，主持人為管理與經(jīng)濟(jì)學(xué)院管理工程系張玉利副教授。
本次報(bào)告中，Daniel Kuhn教授圍繞面向不確定性的優(yōu)化問題進(jìn)行了多方面的分享。首先，Daniel Kuhn教授以簡單的確定性優(yōu)化問題為基礎(chǔ)，引入不確定性優(yōu)化問題，并通過多個(gè)生動(dòng)的例子，強(qiáng)調(diào)不確定性因素在各類問題中的重要性。Daniel Kuhn教授指出，隨機(jī)規(guī)劃模型和風(fēng)險(xiǎn)感知規(guī)劃模型在解決不確定性問題時(shí)具有局限性，存在著高維問題求解慢、求解存在偏差、統(tǒng)計(jì)估計(jì)難以實(shí)現(xiàn)等困難，且隨機(jī)參數(shù)的精確分布往往不可知，存在“關(guān)于不確定性的不確定性”。進(jìn)而，Daniel Kuhn教授介紹了分布式魯棒優(yōu)化模型，該模型能夠在解決不確定性優(yōu)化問題時(shí)達(dá)到“雙贏”。一方面，分布式魯棒優(yōu)化模型能夠得到更好的決策，另一方面，也能解決隨機(jī)和風(fēng)險(xiǎn)感知規(guī)劃模型面臨的求解難題。最后，Daniel Kuhn教授還指出，當(dāng)問題存在模糊性時(shí)，做出隨機(jī)決策往往是較優(yōu)的。
報(bào)告結(jié)束后，Daniel Kuhn教授和參會(huì)師生展開了熱烈的討論和交流，報(bào)告反響熱烈，受到了廣大師生的一致好評(píng)！
附：Daniel Kuhn教授簡介
Daniel Kuhn is full Professor of Operations Research at the College of Management of Technology atécole polytechnique fédérale de Lausanne, Switzerland (EPFL), where he holds the Chair of Risk Analytics and Optimization (RAO). Before joining EPFL, he was a faculty member at Imperial College London (2007–2013) and a postdoctoral researcher at Stanford University (2005–2006). He received a Ph.D. in Economics from the University of St. Gallen in 2004 and an M.Sc. in Theoretical Physics from ETH Zürich in 1999. His research interests revolve around robust optimization and stochastic programming. He serves as the area editor for continuous optimization for Operations Research and as an associate editor for several other journals including Management Science, Mathematical Programming, Mathematics of Operations Research and Operations Research Letters.
