An improved multi-objective imperialist competitive algorithm for surgical case scheduling problem with switching and...

Area of Science:

• Operations Research
• Healthcare Management
• Computer Science

Background:

• Surgical case scheduling is a critical yet complex aspect of healthcare operations.
• Efficiently assigning limited resources to patients presents significant challenges.
• Existing methods often struggle to account for dynamic factors like patient preparation times.

Purpose of the Study:

• To address the complexities of surgical case scheduling by modeling it as a flexible job shop scheduling problem (FJSP).
• To develop and evaluate an improved multi-objective imperialist competitive algorithm (IMOICA) for optimizing surgical scheduling.
• To incorporate patient switching and preparation times into the scheduling optimization process.

Main Methods:

• The study frames surgical case scheduling as a flexible job shop scheduling problem (FJSP).
• An improved multi-objective imperialist competitive algorithm (IMOICA) was developed, incorporating non-dominant sorting.
• Key algorithmic enhancements include social hierarchy initialization, attraction-repulsion (AR) for assimilation, revolution strategy for diversity, and variable neighborhood search (VNS) for exploitation.

Main Results:

• The proposed IMOICA demonstrates efficiency in solving the surgical case scheduling problem.
• Experiments indicate that advance scheduling leads to significant savings in both time and cost.
• The algorithm effectively optimizes scheduling by considering patient-specific preparation and switching times.

Conclusions:

• The developed IMOICA is a viable and efficient tool for optimizing surgical case scheduling.
• Proactive scheduling through advanced algorithms can lead to substantial operational efficiencies in healthcare.
• This approach offers a robust solution for complex resource allocation challenges in surgical departments.