Home care staff planning and scheduling an integrated operations management perspective
- Authors: Mutingi, M. , Mbohwa, Charles
- Date: 2013
- Subjects: Health care management , Healthcare staff scheduling , Home health care scheduling
- Type: Article
- Identifier: uj:6173 , http://hdl.handle.net/10210/13780
- Description: The Home Health Care (HHC) sector continues to grow at an increasing rate across the globe. As such, developing proper health care management systems and operations strategies is essential for survivability, growth and competitiveness of healthcare systems. As labor is the most critical asset in the home care organizations, it is crucial to obtain an in-depth understanding of HHC operations, from an operations management view point, with specific emphasis on staff planning and scheduling. The aim of this paper is to provide a taxonomic analysis of HHC staff planning and scheduling decisions, from an operations management perspective. We identify from various cases in the literature, the core activities characterizing staff planning and scheduling. We then present a taxonomic analysis of these activities, and propose an integrated approach to decision making. A conceptual modeling framework is then proposed to assist the decision maker in solving problems in HHC staff planning and scheduling.
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Home healthcare worker scheduling : a group genetic algorithm approach
- Authors: Mutingi, M. , Mbohwa, Charles
- Date: 2013
- Subjects: Healthcare staff scheduling , Healthcare service quality , Group genetic algorithm
- Type: Article
- Identifier: uj:4840 , ISBN 978-988-19251-0-7 , http://hdl.handle.net/10210/12488
- Description: Home healthcare worker scheduling is a hard combinatorial problem concerned with the allocation of care tasks to healthcare givers at a minimal cost while considering healthcare service quality by striving to meet the time window restrictions specified by the patients. This paper proposes a group genetic algorithm (GGA) for addressing the scheduling problem. The approach utilizes the strengths of unique group genetic operators to effectively and efficiently address the group structure of the problem, providing good solutions within reasonable computation times. Computational results obtained show that the GGA approach is effective.
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