Multi-Objective Home Health Care Routing and Scheduling With Sharing Service via a Problem-Specific Knowledge-Based Artificial Bee Colony Algorithm

Currently, the healthcare of elderly people arouses widespread concerns since the sharp increase of aging population puts severe stress on public medical resources. Home health care (HHC) is regarded as an alternative answer to hospitalization, while it plays an important role in reducing healthcare...

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Published in:IEEE transactions on intelligent transportation systems Vol. 25; no. 2; pp. 1 - 14
Main Authors: Fu, YaPing, Ma, XiaoMeng, Gao, KaiZhou, Li, ZhiWu, Dong, HongYu
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Aging
Archives & records
artificial bee colony algorithm
Bees
Business
Costs
Health care
Home health care
Integer programming
Knowledge based systems
Mathematical programming
Medical services
Mixed integer
multi-objective optimization
Multiple objective analysis
Older adults
Optimization algorithms
Resource scheduling
Routing
scheduling
Search algorithms
Search methods
Statistical analysis
Swarm intelligence
ISSN:1524-9050, 1558-0016
Online Access:Get full text
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Summary:Currently, the healthcare of elderly people arouses widespread concerns since the sharp increase of aging population puts severe stress on public medical resources. Home health care (HHC) is regarded as an alternative answer to hospitalization, while it plays an important role in reducing healthcare cost and improving service satisfaction. This work addresses a service resource routing and scheduling problem with sharing strategy among multiple HHC centers for given customers. Two objective functions are involved: minimizing the total operation cost including the fixed usage cost of centers, caregiver usage cost and service cost, and minimizing the total tardiness caused by delay service. Firstly, a mixed integer programming model is formulated to describe the concerned problem. Secondly, a multi-objective artificial bee colony algorithm with problem-specific knowledge (MABC-PK) is proposed. Three problem-specific knowledge-based heuristics are designed to initialize population. A crossover operation and a self-learning neighborhood selection method are developed to prompt collaborative search of population and external archive. Furthermore, two knowledge-based local search methods are proposed for refining solutions in the external archive via employing some observations and priority properties derived from the problem characteristics. Finally, extensive experiments are conducted by comparing the proposed approach with four widely-acknowledged multi-objective optimization methods and a mathematical programming solver CPLEX. The comparative results and statistical analysis confirm the strong competitiveness of MABC-PK for solving the concerned problem.
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2023.3315785