Directed Bee Colony Optimization Algorithm to Solve the Nurse Rostering Problem

Area of Science:

• Operations Research
• Computer Science
• Healthcare Management

Background:

• The Nurse Rostering Problem (NRP) is an NP-hard combinatorial optimization challenge.
• Efficiently assigning nurses to shifts requires balancing numerous hard and soft constraints.
• Existing methods often struggle with the complexity and scale of real-world NRP instances.

Purpose of the Study:

• To propose a novel metaheuristic technique for solving the Nurse Rostering Problem.
• To develop and adapt a Multiobjective Directed Bee Colony Optimization (MODBCO) algorithm for NRP.
• To demonstrate the efficacy of MODBCO in optimizing nurse scheduling.

Main Methods:

• A multiobjective mathematical programming model was employed.
• A Directed Bee Colony Optimization Algorithm integrated with the Modified Nelder-Mead Method was developed.
• The MODBCO algorithm combines deterministic local search, multiagent particle systems, and bee decision-making principles.

Main Results:

• The MODBCO algorithm successfully solved the multiobjective optimization problem for nurse scheduling.
• Performance was evaluated using the standard INRC2010 dataset, reflecting diverse real-world scenarios.
• Statistical analysis confirmed the algorithm's unique performance on assessment criteria.

Conclusions:

• The proposed MODBCO algorithm offers an effective metaheuristic solution for the Nurse Rostering Problem.
• This approach demonstrates strong performance across varying problem sizes and complexities.
• The integration of diverse optimization techniques enhances the algorithm's capability for complex scheduling tasks.