Clustering technique for large-scale home care crew scheduling problems

Clustering technique for large-scale home care crew scheduling problems The Home Health Care Scheduling Problem involves allocating professional caregivers to patients’ places of residence to meet service demands. These services are regular in nature and must be provided at specific times during the week. In this paper, we present a heuristic with two tie-breaking mechanisms suitable for large-scale versions of the problem. The greedy algorithm merges service lots to minimize the accumulated unproductive time. As a result, the solution is restructured in such a way as to increase its efficiency. The approach is tested on a real-world large instance of the problem for a company whose current resource allocation is inefficient. The solutions are benchmarked against the current service assignment and those obtained by a Ward clustering algorithm, and the results show an improvement in efficiency and cost. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Intelligence Springer Journals

Clustering technique for large-scale home care crew scheduling problems

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Mechanical Engineering; Manufacturing, Machines, Tools
ISSN
0924-669X
eISSN
1573-7497
D.O.I.
10.1007/s10489-017-0908-1
Publisher site
See Article on Publisher Site

Abstract

The Home Health Care Scheduling Problem involves allocating professional caregivers to patients’ places of residence to meet service demands. These services are regular in nature and must be provided at specific times during the week. In this paper, we present a heuristic with two tie-breaking mechanisms suitable for large-scale versions of the problem. The greedy algorithm merges service lots to minimize the accumulated unproductive time. As a result, the solution is restructured in such a way as to increase its efficiency. The approach is tested on a real-world large instance of the problem for a company whose current resource allocation is inefficient. The solutions are benchmarked against the current service assignment and those obtained by a Ward clustering algorithm, and the results show an improvement in efficiency and cost.

Journal

Applied IntelligenceSpringer Journals

Published: Apr 5, 2017

References

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