Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/67520
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dc.contributor.authorLeung, PLen_US
dc.contributor.authorWu, CHen_US
dc.contributor.authorHo, GTSen_US
dc.contributor.authorIp, WHen_US
dc.contributor.authorMou, WLen_US
dc.date.accessioned2017-07-27T08:33:36Z-
dc.date.available2017-07-27T08:33:36Z-
dc.date.issued2015-
dc.identifier.citation2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, 6-9 Dec 2015, p.1337-1341en_US
dc.identifier.isbn978-1-4673-8066-9 (electronic)-
dc.identifier.isbn978-1-4673-8065-2 (USB)-
dc.identifier.urihttp://hdl.handle.net/10397/67520-
dc.description.abstractHong Kong is becoming an ageing society. The growth in the demand of residential care places, together with the increasing complexities of illnesses and varieties of procedures, result in heavy workloads and pressures of local nursing staff. In particular, nursing homes are experiencing high nursing staff turnover and continual manpower shortage. Despite the increasing demand from academic, commercial and political aspects for better healthcare resources allocation, studies conducted on nursing homes' workforce planning are limited. Also, existing studies seldom formulate the nursing workforce planning problem or propose feasible models for practical usage. In this paper, an optimization-based nursing workforce planning model is proposed for enhancing the service capacity of local nursing homes. In order to show the feasibility, a simplified model has been solved by Excel Solver while the proposed model will be solved by other algorithms in the near-future.en_US
dc.description.sponsorshipDepartment of Industrial and Systems Engineeringen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.subjectModellingen_US
dc.subjectNursing homeen_US
dc.subjectWorkforce planningen_US
dc.subjectStaffing optimizationen_US
dc.titleWorkforce modelling, analysis and planning : a feasibility study in a local nursing homeen_US
dc.typeConference Paperen_US
dc.identifier.spage1337-
dc.identifier.epage1341-
dc.identifier.doi10.1109/IEEM.2015.7385865-
dc.relation.conferenceIEEE International Conference on Industrial Engineering and Engineering Management [IEEM]-
dc.identifier.rosgroupid2015001145-
dc.description.ros2015-2016 > Academic research: refereed > Refereed conference paper-
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