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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Vydané v:	IEEE transactions on intelligent transportation systems Ročník 25; číslo 2; s. 1 - 14
Hlavní autori:	Fu, YaPing, Ma, XiaoMeng, Gao, KaiZhou, Li, ZhiWu, Dong, HongYu
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.02.2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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Abstract	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.
AbstractList	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.
Author	Ma, XiaoMeng Dong, HongYu Gao, KaiZhou Li, ZhiWu Fu, YaPing
Author_xml	– sequence: 1 givenname: YaPing surname: Fu fullname: Fu, YaPing organization: School of Business, Qingdao University, Qingdao, China – sequence: 2 givenname: XiaoMeng surname: Ma fullname: Ma, XiaoMeng organization: School of Business, Qingdao University, Qingdao, China – sequence: 3 givenname: KaiZhou orcidid: 0000-0002-9252-6928 surname: Gao fullname: Gao, KaiZhou organization: Institute of Systems Engineering, Macau University of Science and Technology, Taipa, China – sequence: 4 givenname: ZhiWu surname: Li fullname: Li, ZhiWu organization: Institute of Systems Engineering, Macau University of Science and Technology, Taipa, China – sequence: 5 givenname: HongYu surname: Dong fullname: Dong, HongYu organization: National Engineering Research Center for Agri-Product Quality Traceability and the Research Center for Consumption Big Data and Intelligent Decision-Making, Beijing Technology and Business University, Beijing, China
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SubjectTerms	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
Title	Multi-Objective Home Health Care Routing and Scheduling With Sharing Service via a Problem-Specific Knowledge-Based Artificial Bee Colony Algorithm
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