A Review of Optimization Studies for System Appointment Scheduling
Abstract
:1. Introduction
2. System Structure of AS
2.1. AS Characteristics
 Uncertainty AS performance;
 2.
 ASs can be extended to represent other industries.
2.2. System Structure of AS
2.2.1. Appointment Rules
 Fixedinterval rules.
 2.
 Variableinterval rules.
 The “offset” rule.
 The “dome” appointment rule.
 The “plateaudome” appointment rule.
2.2.2. Patient Classification
2.3. AS System Decision Framework
2.3.1. Strategic Decisions
2.3.2. Tactical Decisions
2.3.3. Operational Decisions
3. Optimization Framework
3.1. Optimization Objective
3.1.1. Societal Benefit
3.1.2. Economic Performance
3.1.3. Resource Utilization
3.1.4. Other Objectives
3.2. Decision Variable
3.3. Constraints
3.3.1. Queue Balance Constraints
3.3.2. Capacity Constraints
 Surgeon availability constraint:
 Operating room availability constraint:
3.3.3. The Completion Time Constraint
4. Optimization Algorithms
4.1. Genetic Algorithm
4.1.1. Objective Function and Constraint
4.1.2. Optimization Process
4.2. Whale Optimization Algorithm
4.2.1. Optimization Model
4.2.2. Objective Function
4.2.3. Constraint
4.2.4. Model Solving
4.3. Tabu Search Algorithm
4.3.1. The Integer Programming Model in a Definitive Model
4.3.2. Algorithm Optimal Progress
4.4. Other Heuristic Algorithm
4.4.1. Problem Description
4.4.2. Constraint
4.4.3. Solution Methodology
5. Bibliometric Analysis of Literature
5.1. Methods and Data
5.1.1. Methods
5.1.2. Data
5.2. Operational Results
5.2.1. Country and Publisher
5.2.2. Author
5.2.3. Thematic Trends
5.2.4. Cited Journals
6. Discussion
6.1. Specifics of Healthcare
6.2. Differences of Related Methods
6.3. Tendencies and Scientific Methods
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Decision Types  Features  Related Studies 

Strategic decisions 
 Robinson et al. [61]; Qu et al. [62]; Turkcan et al. [63]; Gocgun et al. [64]; Saghafian et al. [65]; Sauréet al. [66]; Pérez et al. [67] 
 
 
Tactical decisions 
 Wang et al. [68]; Kong et al. [69]; Erdogan et al. [70]; Turkcan et al. [71]; Ozen et al. [72] 
 
 
Operational decisions 
 Balasubramanian et al. [73]; Conforti et al. [74]; Truong et al. [75]; Feldman et al. [14]; Cayirli et al. [17] 
 

Objectives  Contributions  Related Studies 

Societal benefit 
 Anderson et al. [76]; Han et al. [77]; Guido et al. [78]; Dharmadhikari et al. [79]; Cordier et al. [80]; Nazanin et al. [81]; Ma et al. [82] 
 
 
Economic performance 
 ElSharo et al. [83]; Parizi et al. [84]; Chakraborty et al. [85,86]; Muthuraman et al. [87]; Geng et al. [88]; Patrick et al. [89]; Wang et al. [90] 
 
 
 
Resource utilization 
 Nguyen et al. [91];Zeng et al. [92]; Yan et al. [93]; Tsai et al. [94]; Jiang et al. [95,96]; Sevinc et al. [97] 
 
 
Other objectives 
 Savelsbergh et al. [98]; Ozen et al. [72] Zhu et al. [99]; Erdelyi et al. [100] 
 
 

Algorithms  Features  Related Studies 

GA 
 Braune et al. [112]; Fan et al. [113]; Alizadeh et al. [114]; Squires et al. [123] 
 
 
WOA 
 Mohammad et al. [115]; Ali et al. [116]; Qiu et al. [117]; 
 
 
TSA 
 Meersman et al. [58]; Garaix et al. [118]; Corsini et al. [119]; Ali et al. [120]; 
 
 
Other algorithms (GRASP, contraindicated search approach, etc.).  Rajakumari et al. [121]; Ali et al. [122]; Akbarzadeh et al. [124] 
Keywords  Year  Strength  Begin  End  2016–2023 

patient flow  2016  4.64  2016  2017  
discrete event simulation  2016  2.95  2016  2018  
overbooking model  2017  2.8  2017  2018  
time windows  2017  2.58  2017  2018  
metanalysis  2018  2.59  2018  2019  
simulation  2017  2.56  2018  2019  
digital health  2019  3.31  2019  2020  
health information technology  2019  2.48  2019  2020  
electronic health record  2020  2.93  2020  2021  
robust optimization  2017  2.39  2021  2023  
operations research  2021  2.27  2021  2023  
COVID19 pandemic  2021  2.27  2021  2023 
Country  AS Methods  Advantages  Disadvantages 

China 
 Rapid adoption of digital technologies Integration of traditional Chinese medicine  Higher requirements for system arithmetic operations and maintenance 
 
 
United States 
 Convenient for patients Improved accessibility with telemedicine  Insurance dependence Complex verification 
 
 
Canada 
 Universal healthcare coverage  Variation in healthcare services across provinces 
 
Germany 
 Wellestablished healthcare infrastructure  Gatekeeping may lead to delays in specialty care access 
 
Japan 
 Prevention and early intervention  Aging population strain healthcare resources 

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Niu, T.; Lei, B.; Guo, L.; Fang, S.; Li, Q.; Gao, B.; Yang, L.; Gao, K. A Review of Optimization Studies for System Appointment Scheduling. Axioms 2024, 13, 16. https://doi.org/10.3390/axioms13010016
Niu T, Lei B, Guo L, Fang S, Li Q, Gao B, Yang L, Gao K. A Review of Optimization Studies for System Appointment Scheduling. Axioms. 2024; 13(1):16. https://doi.org/10.3390/axioms13010016
Chicago/Turabian StyleNiu, Tiantian, Bingyin Lei, Li Guo, Shu Fang, Qihang Li, Bingrui Gao, Li Yang, and Kaiye Gao. 2024. "A Review of Optimization Studies for System Appointment Scheduling" Axioms 13, no. 1: 16. https://doi.org/10.3390/axioms13010016