Scheduling Optimization of Home Health Care Service Considering Patients’ Priorities and Time Windows

Du, Gang; Liang, Xi; Sun, Chuanwang

doi:10.3390/su9020253

Cited by 39 publications

(23 citation statements)

References 82 publications

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“…A home healthcare scheduling multiobjective optimization problem was considered to improve the patients' satisfaction [16]. Again, GA was utilized to solve the multiobjective optimization with objectives travel cost, service cost and penalty cost.…”

Section: Evolutionary Optimizationmentioning

confidence: 99%

“…Three kinds of optimization algorithms, evolutionary [8][9][10][11][12][13][14][15][16][17], stochastic [18][19][20][21][22][23][24][25][26][27][28][29] and combinatorial optimization [30][31][32][33][34][35][36][37][38] will be addressed. For machine learning algorithms, the discussion is based on un-supervised learning [39][40][41][42][43][44][45][46][47][48][49], supervised learning and semi-supervised learning [71][72][73][74][75][76][77][78]…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

et al. 2017

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Abstract:To promote sustainable development, the smart city implies a global vision that merges artificial intelligence, big data, decision making, information and communication technology (ICT), and the internet-of-things (IoT). The ageing issue is an aspect that researchers, companies and government should devote efforts in developing smart healthcare innovative technology and applications. In this paper, the topic of disease diagnosis in smart healthcare is reviewed. Typical emerging optimization algorithms and machine learning algorithms are summarized. Evolutionary optimization, stochastic optimization and combinatorial optimization are covered. Owning to the fact that there are plenty of applications in healthcare, four applications in the field of diseases diagnosis (which also list in the top 10 causes of global death in 2015), namely cardiovascular diseases, diabetes mellitus, Alzheimer's disease and other forms of dementia, and tuberculosis, are considered. In addition, challenges in the deployment of disease diagnosis in healthcare have been discussed.

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Section: Evolutionary Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

et al. 2017

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“…The authors also compared the performance of this two-stage approach using the Kernel regression method with joint assignment and routing solution methods. Du et al [32] studied an HHC scheduling problem considering patients' priorities and time windows constraints. The authors proposed a solution algorithm which combines genetic algorithm (GA) with local search to solve large-scale problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Solving a More Flexible Home Health Care Scheduling and Routing Problem with Joint Patient and Nursing Staff Selection

Nasir

Dang

2018

Sustainability

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Development of an efficient and effective home health care (HHC) service system is a quite recent and challenging task for the HHC firms. This paper aims to develop an HHC service system in the perspective of long-term economic sustainability as well as operational efficiency. A more flexible mixed-integer linear programming (MILP) model is formulated by incorporating the dynamic arrival and departure of patients along with the selection of new patients and nursing staff. An integrated model is proposed that jointly addresses: (i) patient selection; (ii) nurse hiring; (iii) nurse to patient assignment; and (iv) scheduling and routing decisions in a daily HHC planning problem. The proposed model extends the HHC problem from conventional scheduling and routing issues to demand and capacity management aspects. It enables an HHC firm to solve the daily scheduling and routing problem considering existing patients and nursing staff in combination with the simultaneous selection of new patients and nurses, and optimizing the existing routes by including new patients and nurses. The model considers planning issues related to compatibility, time restrictions, contract durations, idle time and workload balance. Two heuristic methods are proposed to solve the model by exploiting the variable neighborhood search (VNS) approach. Results obtained from the heuristic methods are compared with a CPLEX based solution. Numerical experiments performed on different data sets, show the efficiency and effectiveness of the solution methods to handle the considered problem.

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“…Yuan et al [29] devise a B & P algorithm for the HCSRP with stochastic service times and skill requirements. Du et al [5] propose the genetic algorithm to solve the HCSRP when considering patients' priorities and time windows. Liu et al [10] construct the model of the HCSRP with lunch break requirements and caregiver-customer compatibility, and develop a B & P algorithm to exactly solve the problem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraints (5) guarantee that each customer is visited by a caregiver exactly once. Constraints (6) to (8) ensure that each caregiver starts at node 0, visits some nodes, and finishes at node n + 1.…”

Section: Problem Descriptionmentioning

confidence: 99%

Disruption Management for the Real-Time Home Caregiver Scheduling and Routing Problem

Yuan

Jiang

2017

Sustainability

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Abstract:The aggravating trend of the aging population, the miniaturization of the family structure, and the increase of families with empty nesters greatly affect the sustainable development of the national economy and social old-age security system of China. The emergence of home health care or home care (HHC/HC) service mode provides an alternative for elderly care. How to develop and apply this new mobile service mode is crucial for the government. Therefore, the pertinent optimization problems regarding HHC/HC have constantly attracted the attention of researchers. Unexpected events, such as new requests of customers, cancellations of customers' services, and changes of customers' time windows, may occur during the process of executing an a priori visiting plan. These events may sometimes make the original plan non-optimal or even infeasible. To cope with this situation, we introduce disruption management to the real-time home caregiver scheduling and routing problem. The deviation measurements on customers, caregivers, and companies are first defined. A mathematical model that minimizes the weighted sum of deviation measurements is then constructed. Next, a tabu search (TS) heuristic is developed to efficiently solve the problem, and a cost recorded mechanism is used to strengthen the performance. Finally, by performing computational experiments on three real-life instances, the effectiveness of the TS heuristic is tested, and the advantages of disruption management are analyzed.

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Scheduling Optimization of Home Health Care Service Considering Patients’ Priorities and Time Windows

Cited by 39 publications

References 82 publications

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Solving a More Flexible Home Health Care Scheduling and Routing Problem with Joint Patient and Nursing Staff Selection

Disruption Management for the Real-Time Home Caregiver Scheduling and Routing Problem

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