Home health care routing and scheduling in densely populated communities considering complex human behaviours

Zhang, Ting, Liu, Yang, Yang, Xintong ORCID: https://orcid.org/0000-0002-7612-614X, Chen, Jingjing and Huang, Jiaming 2023. Home health care routing and scheduling in densely populated communities considering complex human behaviours. Computers and Industrial Engineering 182 , 109332. 10.1016/j.cie.2023.109332

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Abstract

This study focuses on the home health care routing problem (HHCRP) in the scenario of high population density areas where many elders live closely together. This study considers two main objectives. The first is to reduce travel and wait times for nurses or elders. The second concerns socially related objectives in scheduling problems, such as ‘quality of life’ and empowerment, by considering assumptions related to the acquaintanceship and mutual preferences of nurses and elders. This study models the effects of mutual preferences and acquaintanceship on service time in HHCRP. We use the Markov decision process and chance-constrained programming (CCP) to model the system to conserve the sequential service provision parameters and better represent the influence of stochastic service times. Because traditional deterministic algorithms cannot solve such a model, we apply a model-free reinforcement learning algorithm, Q-learning (QL), as well as the ant colony optimisation (ACO) algorithm. Thus, we tackle this problem by developing a model and algorithm to solve complex, large-scale systems. This study’s theoretical and practical contributions are verified by feedback from researchers and practitioners.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Engineering
Publisher:	Elsevier
ISSN:	0360-8352
Date of First Compliant Deposit:	10 July 2023
Date of Acceptance:	19 May 2023
Last Modified:	14 Jul 2023 01:43
URI:	https://orca.cardiff.ac.uk/id/eprint/160900

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