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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link=blue vlink=purple><div class=WordSection1><p class=MsoNormal align=center style='text-align:center;line-height:110%'><b><span style='font-size:16.0pt;line-height:110%;color:red'>INDUSTRIAL ENGINEERING SEMINAR<o:p></o:p></span></b></p><p class=MsoNormal align=center style='text-align:center;line-height:110%'><b><span style='font-size:16.0pt;line-height:110%;color:red'><o:p> </o:p></span></b></p><p class=MsoNormal align=center style='text-align:center;line-height:110%'><b><span style='font-size:16.0pt;line-height:110%;color:red'>Date: January 18, 2013<o:p></o:p></span></b></p><p class=MsoNormal align=center style='text-align:center;line-height:110%'><b><span style='font-size:16.0pt;line-height:110%;color:red'>Time: 10am-11am<o:p></o:p></span></b></p><p class=MsoNormal align=center style='text-align:center;line-height:110%'><b><span style='font-size:16.0pt;line-height:110%;color:red'>Place: D 102<o:p></o:p></span></b></p><p class=MsoNormal align=center style='text-align:center;line-height:110%'><b><span style='font-size:11.0pt;line-height:110%;color:navy'><o:p> </o:p></span></b></p><p class=MsoPlainText align=center style='text-align:center'><b><span style='font-size:14.0pt;font-family:"Times New Roman","serif"'>Generalized Inverse Optimization with Application to Radiation Therapy<o:p></o:p></span></b></p><p class=MsoNormal align=center style='text-align:center'><b><span style='font-size:14.0pt'><o:p> </o:p></span></b></p><p class=MsoNormal align=center style='text-align:center'><b><span style='font-size:14.0pt'>Timothy Chan<o:p></o:p></span></b></p><p class=MsoNormal align=center style='text-align:center'><b><span style='font-size:14.0pt'>Assistant Professor, Mechanical and Industrial Engineering<o:p></o:p></span></b></p><p class=MsoNormal align=center style='text-align:center'><b><span style='font-size:14.0pt'>University of Toronto</span></b><b><span style='font-size:14.0pt;font-family:"Calibri","sans-serif"'><o:p></o:p></span></b></p><p class=MsoNormal style='text-align:justify;line-height:110%'><b><span style='font-size:14.0pt;line-height:110%;color:red'><o:p> </o:p></span></b></p><p class=MsoNormal align=center style='text-align:center;line-height:110%'><b><span style='font-size:14.0pt;line-height:110%;color:red'>Abstract<o:p></o:p></span></b></p><p class=MsoNormal align=center style='text-align:center;line-height:110%'><b><span style='font-size:14.0pt;line-height:110%;color:red'><o:p> </o:p></span></b></p><p class=MsoPlainText style='text-align:justify'><span style='font-size:12.0pt;font-family:"Times New Roman","serif"'>Current clinical approaches to radiation therapy treatment design typically involve trial-and-error parameter tuning in the underlying optimization problem. We develop a generalized inverse optimization methodology to determine objective function weights in a multi-objective radiation therapy treatment planning problem and characterize the structure and geometry of optimal solutions. <a name="_GoBack"></a>Using clinical prostate cancer cases, we demonstrate that our inversely optimized treatments are comparable to clinical treatments, but can be generated using fewer, more intuitive objective functions and weights. This approach has the potential to streamline the planning process and guide the design of future treatments.<o:p></o:p></span></p><p class=MsoNormal align=center style='text-align:center;line-height:110%'><span style='font-size:14.0pt;line-height:110%;color:red'><o:p> </o:p></span></p><p class=MsoNormal style='text-align:justify'><o:p> </o:p></p><p class=MsoNormal align=center style='text-align:center;line-height:110%'><b><span style='font-size:14.0pt;line-height:110%;color:red'>Biography</span></b><span style='font-size:14.0pt;line-height:110%;color:red'><o:p></o:p></span></p><p class=MsoNormal align=center style='text-align:center;line-height:110%'><span style='font-size:14.0pt;line-height:110%;color:red'><o:p> </o:p></span></p><p class=MsoNormal style='text-align:justify;line-height:110%'><span style='background:white'>Timothy C. Y. Chan is an Assistant Professor in the department of Mechanical and Industrial Engineering at the University of Toronto. His primary research interests are in optimization under uncertainty and the application of optimization methods to radiation therapy, health care operations and sustainability. He received his B.Sc. in Applied Mathematics from the University of British Columbia, and his Ph.D. in Operations Research from the Massachusetts Institute of Technology. Before coming to Toronto, he was an Associate in the Chicago office of McKinsey and Company, a global management consulting firm. During that time, he advised leading companies in the fields of medical device technology, travel and hospitality, telecommunications, and energy on issues of strategy, organization, technology and operations.</span><o:p></o:p></p><p class=MsoNormal><span style='font-size:11.0pt;font-family:"Calibri","sans-serif"'><o:p> </o:p></span></p></div></body></html>