Adaptive dynamic programming algorithms for sequential appointment scheduling with patient preferences

Wang, Jin; Fung, Richard Y. K.

doi:10.1016/j.artmed.2014.12.002

Cited by 23 publications

(9 citation statements)

References 22 publications

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“…It also offers practical support to help decision-makers promote an AI approach that can support its digital healthcare transformation. All investigation outcomes are tracked by AI, which then analyzes patterns to optimize future interactions [ 139 , 140 ]. The AS system optimizes and duplicates the factors that lead to positive results.…”

Section: Results and Analysismentioning

confidence: 99%

Appointment Scheduling Problem in Complexity Systems of the Healthcare Services: A Comprehensive Review

Ala

Chen

2022

Journal of Healthcare Engineering

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This paper provides a comprehensive review of Appointment Scheduling (AS) in healthcare service while we propose appointment scheduling problems and various applications and solution approaches in healthcare systems. For this purpose, more than 150 scientific papers are critically reviewed. The literature and the articles are categorized based on several problem specifications, i.e., the flow of patients, patient preferences, and random arrival time and service. Several methods have been proposed to shorten the patient waiting time resulting in the shortest idle times in healthcare centers. Among existing modeling such as simulation models, mathematical optimization techniques, Markov chain, and artificial intelligence are the most practical approaches to optimizing or improving patient satisfaction in healthcare centers. In this study, various criteria are selected for structuring the recent literature dealing with outpatient scheduling problems at the strategic, tactical, or operational levels. Based on the review papers, some new overviews, problem settings, and hybrid modeling approaches are highlighted.

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Section: Results and Analysismentioning

confidence: 99%

Appointment Scheduling Problem in Complexity Systems of the Healthcare Services: A Comprehensive Review

Ala

Chen

2022

Journal of Healthcare Engineering

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“…They used mixed integer programming (MIP) to solve this problem. Wang and Fung (2015) proposed a Markov decision process for scheduling sequential appointments to maximize patient satisfaction level and, dynamic programming for avoiding the curse of dimensionality. Xiang et al (2015) introduced a modified ACO algorithm with a two-level ant graph model to solve the surgery-scheduling problem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The challenge of this problem is to consider and combine aspects of staff and vehicle routing and scheduling. Nowadays, routing and scheduling of Home Health Care Services planners face challenging and complex optimization problems on various decisionlevels, such as staff assignment, shift scheduling and, staff routing decisions (Wang & Fung, 2015). For competing in the today's markets and lowering public expenses, the major points are increasing service quality and decreasing costs.…”

Section: Introductionmentioning

confidence: 99%

Using a metaheuristic algorithm for solving a home health care routing and scheduling problem

Manavizadeh¹,

Farrokhi-Asl²,

Beiraghdar³

2020

10.5267/j.jpm

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The Health Care system is changing from the hospitalization to the home care, and the World Health Organization has announced that the rate of care-dependent elderly people in Europe will considerably increase within the next decades. Thus, scientific planning for this area is an essential factor to improve the community health. This paper aims to develop a mathematical modeling for Home Health Care Routing and Scheduling Problem and to solve it by means of Simulated Annealing (SA) algorithm considering real condition (staff vehicle traveling, conditions of patients and so forth). We permit interdependent services for patients in which they can order as many services as they want with any relation between them (Multiple Services) and supposed time window for each service. The mathematical formulation of the problem is coded in GMAS software, which is a well-known commercial software for solving optimization problems. In addition, for large-scale problems where GAMS is unable to solve, SA algorithm is applied to tackle the problems. Finally, sensitivity analysis on the most important parameters (number of services and number of patients with interdependent Multiple services) are conducted. The results reveal that when each patient can order infinite services with any relation between them, complexity of the problem increases, but SA algorithm can solve large instances with reasonable solution in the less computational time. Thus, SA algorithm shows a rational performance for large instances. Moreover, the most important factors that affect the objective value and the run time of the problems are number of patients, and number of patients with interdependent multiple services.

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“…Furthermore [7] developed approximate dynamic programming (ADP) algorithms to solve stochastic project scheduling problems. [8] developed adaptive dynamic programming algorithms to schedule consecutive appointments with the consideration of patient preferences in order to maximize the patient satisfaction level. [9] proposed a new dynamic programming algorithm for solving scheduling problem of independent tasks with common due date and to minimize the total weighted tardiness.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Programming to Solve Picking Schedule at the Tea Plantation

Budijati¹,

Iskandar²

2018

IJET

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The tea picking schedule at PT Perkebunan Ciater is set to be the same for all plantation blocks. In fact, the altitude from sea level and the pruning age of each plantation block is different, this results in the difference of buds’ growth. The implementation of the same picking schedule causes the quality and quantity of tea buds often could not be fulfilled. This research is to determine the precise picking schedule by considering the buds’ growth of each plantation block. Two steps are implemented to solve the problem. The first step is to look for picking period and the pattern of buds’ quality for each plantation block, which corresponds to the altitude of the location and the pruning age. The regression method is applied in this first step. The buds’ quality pattern is then used to determine the cost of decreasing buds’ quality and the costs of the buds that left in the plantation. The second step is to develop the picking schedule using dynamic programming, which minimizes the total cost of picking. In addition to this, we also develop a rolling schedule, which schedule time interval is three days. The model results show that the proposed schedule gives a better total cost than the current schedule and the buds’ quality target is easier to achieve.

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Adaptive dynamic programming algorithms for sequential appointment scheduling with patient preferences

Cited by 23 publications

References 22 publications

Appointment Scheduling Problem in Complexity Systems of the Healthcare Services: A Comprehensive Review

Appointment Scheduling Problem in Complexity Systems of the Healthcare Services: A Comprehensive Review

Using a metaheuristic algorithm for solving a home health care routing and scheduling problem

Dynamic Programming to Solve Picking Schedule at the Tea Plantation

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