124. A matheuristic for integrated resource allocation to patient appointment series
Contributed abstract in session FA-3: Integrated Planning in Healthcare /2, stream Regular talks.
Friday, 9:20-10:30Room: Room S2
Authors (first author is the speaker)
| 1. | Sara Bigharaz
|
| Industrial Economics and Technology Management, Norwegian University of Science and Technology | |
| 2. | Henrik Andersson
|
| Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology | |
| 3. | Anders N. Gullhav
|
| Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology |
Abstract
Managing the time that patients wait for their next appointment is a well-known challenge in hospitals. Scarce resources must be efficiently planned to create schedules that allow a hospital to keep these waiting times below set targets. The problem studied in this work is based on the situation in the Orthopedic Department at St.Olav's Hospital in Norway, and we have developed a MIP model for the integrated operating room (OR) and outpatient clinic (OC) scheduling problem. The model combines a detailed representation of the queues of patients waiting for consultations and surgeries and creates tactical blueprints that assign activities to surgeons and rooms over a planning horizon. To evaluate the performance of the model in OC and OR, we create cases of different sizes and study two aspects of cyclicity, cyclic or non-cyclic availability of surgeons and non-cyclic room schedules. Due to the large size and complexity of the problem, we propose a matheuristic algorithm to solve the sub-problems iteratively within shorter planning horizons. The algorithm involves three phases; initially, the heuristic begins by solving the model using the rolling-horizon heuristic. If the solution is found to be infeasible, the feasibility phase starts, and finally the algorithm iteratively fixes and reoptimizes the allocation of resources to patient types. The matheuristic is able to find better solutions with lower gaps and less time compared to solving the model using a commercial solver.
Keywords
- Integrated planning of health services
- Resource scheduling
- (Multi) appointment scheduling
Status: accepted
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