Publication Type
Conference Proceeding Article
Version
submittedVersion
Publication Date
12-2017
Abstract
We address the Home Health Care Delivery Problem (HHCDP), which is concerned with staff scheduling in the home health care industry. The goal is to schedule health care providers to serve patients at their homes that maximizes the total collected preference scores from visited patients subject to several constraints, such as workload of the health care providers, time budget for each provider and so on. The complexity lies in the possibility of cancellation of patient bookings dynamically, and the generated schedule should attempt to patients’ preferred time windows. To cater to these requirements, we model the preference score as a step function which depends on the arrival time of the visit and the resulting problem as the Team Orienteering Problem (TOP) with soft Time Windows and Variable Profits. We propose a fast algorithm, Iterated Local Search (ILS), which has been widely used to solve other variants of the Orienteering Problem (OP). We first solve the modified benchmark Team OP with Time Window instances followed by randomly generated instances. We conclude that ILS is able to provide good solutions within reasonable computational times.
Discipline
Artificial Intelligence and Robotics | Computer Sciences | Medicine and Health Sciences
Research Areas
Intelligent Systems and Optimization
Publication
MISTA 2017: Proceedings of the 8th Multidisciplinary International Conference on Scheduling: Theory and Applications: Kuala Lumpur, December 5-8
First Page
244
Last Page
255
Publisher
MISTA
City or Country
Kuala Lumpur
Citation
GUNAWAN, Aldy; LAU, Hoong Chuin; and LU, Kun.
Home health care delivery problem. (2017). MISTA 2017: Proceedings of the 8th Multidisciplinary International Conference on Scheduling: Theory and Applications: Kuala Lumpur, December 5-8. 244-255.
Available at: https://ink.library.smu.edu.sg/sis_research/3891
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.