BEGIN:VCALENDAR PRODID:-//Microsoft Corporation//Outlook 16.0 MIMEDIR//EN VERSION:2.0 METHOD:REQUEST X-MS-OLK-FORCEINSPECTOROPEN:TRUE BEGIN:VTIMEZONE TZID:Eastern Standard Time BEGIN:STANDARD DTSTART:16011104T020000 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 END:STANDARD BEGIN:DAYLIGHT DTSTART:16010311T020000 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT ATTENDEE;CN="Ran Ji";RSVP=TRUE:mailto:[log in to unmask] CLASS:PUBLIC CREATED:20201005T165658Z DESCRIPTION:SEOR Seminar (Oct 9th\, Friday\, 11:00 AM to 12:00 PM)\n\nTitle : Nurse Staffing under Absenteeism: A Distributionally Robust Optimization Approach\n\nSpeaker: Dr. Ruiwei Jiang\, \n\n Assistant Pro fessor\, University of Michigan\n\n\nSynopsis. We study the nurse staffing problem under random nurse demand and absenteeism. Notably\, the nurse ab senteeism uncertainty is endogenous\, i.e.\, the rate of nurses showing up for work partially depends on the nurse staffing level. For the quality o f care\, many hospitals have developed float pools of nurses by cross-trai ning\, so that a pool nurse can be assigned to the units short of nurses. In this talk\, we propose a distributionally robust nurse staffing (DRNS) model that considers endogenous uncertainty. We derive a decomposition alg orithm to solve the general model. In addition\, we identify several speci al float pool structures that allow us to reformulate the DRNS model as a monolithic mixed-integer linear program\, which facilitates off-the-shelf commercial solvers. Furthermore\, we optimize the float pool design to red uce the cross-training. The numerical case studies\, based on the data of a collaborating hospital\, lead to recommendations for the float pool desi gn from an operational perspective.\n\n\n\nBio. Ruiwei Jiang is an Assista nt Professor of Industrial & Operations Engineering at the University of M ichigan. He conducts research on the theory of stochastic and robust optim ization\, integer programming\, and their applications on power systems an d healthcare operations. Ruiwei’s research has been recognized with an N SF Career Award and two awards in the INFORMS Junior Faculty Interest Grou p paper competition.\n\n\n\n\n-------------------------------------------- -----------------------------------------------------------------------\n\ nJoin Zoom Meeting\n\nhttps://gmu.zoom.us/j/97280792742?pwd=TVY1VW93M3QvSG 9jbFhaQTlkUjNRUT09\n\n \n\nMeeting ID: 972 8079 2742\n\nPasscode: 604272\n \nOne tap mobile\n\n+12678310333\,\,97280792742#\,\,\,\,\,\,0#\,\,604272# US (Philadelphia)\n\n+13017158592\,\,97280792742#\,\,\,\,\,\,0#\,\,604272# US (Germantown)\n\n \n\nDial by your location\n\n +1 267 831 0333 US (Philadelphia)\n\n +1 301 715 8592 US (Germantown)\n\nMeeting ID : 972 8079 2742\n\nPasscode: 604272\n\nFind your local number: https://gmu .zoom.us/u/abdcOEKamo\n\n \n\nJoin by SIP\n\[log in to unmask]\n\n \ n\nJoin by H.323\n\n162.255.37.11 (US West)\n\n162.255.36.11 (US East)\n\n 115.114.131.7 (India Mumbai)\n\n115.114.115.7 (India Hyderabad)\n\n213.19. 144.110 (Amsterdam Netherlands)\n\n213.244.140.110 (Germany)\n\n103.122.16 6.55 (Australia)\n\n149.137.40.110 (Singapore)\n\n64.211.144.160 (Brazil)\ n\n69.174.57.160 (Canada)\n\n207.226.132.110 (Japan)\n\nMeeting ID: 972 80 79 2742\n\nPasscode: 604272\n\n \n\n \n\n DTEND;TZID="Eastern Standard Time":20201009T120000 DTSTAMP:20201005T164309Z DTSTART;TZID="Eastern Standard Time":20201009T110000 LAST-MODIFIED:20201005T165658Z LOCATION:https://gmu.zoom.us/j/97280792742?pwd=TVY1VW93M3QvSG9jbFhaQTlkUjNR UT09 ORGANIZER;CN="Ran Ji":mailto:[log in to unmask] PRIORITY:5 SEQUENCE:0 SUMMARY;LANGUAGE=en-us:SEOR Seminar on Nurse Staffing Optimization - Dr. Ru iwei Jiang (U Michigan) TRANSP:OPAQUE UID:040000008200E00074C5B7101A82E0080000000040259414159BD601000000000000000 010000000957A191400EDFE43A31F13CAFE77D949 X-ALT-DESC;FMTTYPE=text/html:\n\n\n\n\n
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\ nSEOR Seminar (Oct 9th\, Friday\, 11:00 AM to 12:00 PM)

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\nTitle: \;Nurse Staffing under Absenteeism: \;A D istributionally Robust Optimization Approach

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\nSpeaker: Dr. Ruiwei Jiang\, \;

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\n&nbs p\;  \;  \;  \;  \;  \;  \;  \; Assistant Prof essor\, University of Michigan

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\nSynopsis. We study the nurse staf fing problem under random nurse demand and absenteeism. Notably\, the nurs e absenteeism uncertainty is endogenous\, i.e.\, the rate of nurses showin g up for work partially depends on the nurse staffing level. For the quali ty\n of care\, many hospitals have developed float pools of nurses by cros s-training\, so that a pool nurse can be assigned to the units short of nu rses. In this talk\, we propose a distributionally robust nurse staffing ( DRNS) model that considers endogenous uncertainty.\n We derive a decomposi tion algorithm to solve the general model. In addition\, we identify sever al special float pool structures that allow us to reformulate the DRNS mod el as a monolithic mixed-integer linear program\, which facilitates off-th e-shelf commercial\n solvers. Furthermore\, we optimize the float pool des ign to reduce the cross-training. The numerical case studies\, based on th e data of a collaborating hospital\, lead to recommendations for the float pool design from an operational perspective.

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Bio. Ruiwei Jiang is an Assistant Professor of Industrial & \; Operations Engineering at the University of Michigan. He conducts resea rch on the theory of stochastic and robust optimization\, integer programm ing\, and\n their applications on power systems and healthcare operations. Ruiwei’s research has been recognized with an NSF Career Award and two awards in the INFORMS Junior Faculty Interest Group paper competition.
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\nhttps ://gmu.zoom.us/j/97280792742?pwd=TVY1VW93M3QvSG9jbFhaQTlkUjNRUT09\n

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\nMeeting ID: 972 8079 2742

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\nPasscode: 604272

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\nOne tap mobile

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\n+12678310333\,\,97280792742#\,\,\,\,\,\,0# \,\,604272# US (Philadelphia)

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\n+13017158592\,\,97280792742#\,\,\,\,\,\,0#\,\,604272# US (German town)

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\nDial by your location

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\n \; \; \;&nb sp\; \; \; \; +1 267 831 0333 US (Philadelphia)

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\n \; \; \; \; \ ; \; \; +1 301 715 8592 US (Germantown)

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\nMeeting ID: 972 8079 2742

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\nPasscode: 604272

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\nFind your local number: https://gmu.zoom.us /u/abdcOEKamo

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\n \ ;

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\nJoin by SIP

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\n[log in to unmask]

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\nJoin by H.323

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\n162.255.37.11 (US West)

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\n162.255.36.11 (US East)

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\n115.114.131.7 (India Mumbai)

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\n115.114.115.7 (India Hy derabad)

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\n213.19.144. 110 (Amsterdam Netherlands)

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\n213.244.140.110 (Germany)

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\n< span style="">103.122.166.55 (Australia)

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\n149.137.40.110 (Singapore)

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\n64.211.144.160 (Brazil)

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\n69.174.57.160 (Canada)

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\n207.226.132.110 (Japan)

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\nMeeting ID: 972 8079 2742

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\nPasscode: 604272

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