2024-02-28
OBP:
The paper “Optimal outpatient appointment scheduling” by Kaandorp and Koole (2007) applies a local search procedure to solve the scheduling problem. This approach falls under the category of a greedy algorithm strategy. Greedy algorithms make locally optimal choices at each step with the hope of finding a global optimum, while dynamic programming solves subproblems recursively and reuses their solutions to avoid repeated calculations. In this case, the local search procedure in the paper aims to converge to the optimal schedule based on various factors like expected waiting times of patients, idle time of the doctor, and tardiness as objectives.
Generally greedy algorithms do not guarantee global optimums. However, because Kaandorp and Koole (2007) have proved that the objective function is multimodular, a local optimum must be also a global optimum.
To do:
Establish the complexity (big O) of the strategy developed by Kaandorp and Koole (2007)
Establish recent developed strategies and their complexity
Generate ideas for efficiency improvements
Finish up the coding
Citations:
- Optimal Outpatient Appointment Scheduling - Springer
- Greedy Approach vs Dynamic Programming - GeeksforGeeks
- Optimal Outpatient Appointment Scheduling - ResearchGate
- Difference Between Dynamic Programming and Greedy Approach - Stack Overflow
- Optimal Outpatient Appointment Scheduling - PubMed
- Greedy vs DP - Board Infinity
- Optimal Outpatient Appointment Scheduling (Full Text PDF) - VU Research
- Dynamic Programming vs Greedy Method - JavaTpoint
- Optimal Outpatient Appointment Scheduling Publication - VU Research
- Lecture Notes on Greedy Algorithms vs Dynamic Programming - University of Otago
- Optimal Outpatient Appointment Scheduling - Semantic Scholar
- Discussion on Greedy vs Dynamic Programming - Reddit
- Advanced Methods for Outpatient Appointment Scheduling - Springer
- Comparison Among Greedy, Divide and Conquer, and Dynamic Programming Algorithms - GeeksforGeeks
- Scholar Profile for Outpatient Appointment Scheduling Research