Nurse Scheduling with Opposition-Based Parallel Harmony Search Algorithm

Abstract

One of the advances made in the management of human resources for the effective implementation of service delivery is the creation of personnel schedules. In this context, especially in terms of the majority of health-care systems, creating nurse schedules comes to the fore. Nurse scheduling problem (NSP) is a complex optimization problem that allows for the preparation of an appropriate schedule for nurses and, in doing so, considers the system constraints such as legal regulations, nurses’ preferences, and hospital policies and requirements. There are many studies in the literature that use exact solution algorithms, heuristics, and meta-heuristics approaches. Especially in large-scale problems, for which deterministic methods may require too much time and cost to reach a solution, heuristics and meta-heuristic approaches come to the fore instead of exact methods. In the first phase of the study, harmony search algorithm (HSA), which has shown progress recently and can be adapted to many problems is applied for a dataset in the literature, and the algorithm’s performance is evaluated by comparing the results with other heuristics which is applied to the same dataset. As a result of the evaluation, the performance of the classical HSA is inadequate when compared to other heuristics. In the second phase of our study, by considering new approaches proposed by the literature for HSA, the effects on the algorithm’s performance of these approaches are investigated and we tried to improve the performance of the algorithm. With the results, it has been determined that the improved algorithm, which is called opposition-based parallel HSA, can be used effectively for NSPs.

A Survey of the Nurse Rostering Solution Methodologies: The State-of-the-Art and Emerging Trends

“…A parallel HSA in [25] added a new feature that was based on opposition-based learning to tackle the NSPLib dataset. In the proposed parallel HSA, the re-grouping process was omitted.…”

“…The state-of-the-art methods for this dataset are Multi-Assignment Procedures Algorithm (MAPA) [31] and a hybrid of Variable Fixing Heuristic, Iterated Local Search and Mixed Integer Programming [39]. Other approaches that utilized this dataset are Harmony Search Algorithm [25] and a hybrid of Variable Fixing Heuristic and Mixed Integer Programming [38].…”

“…Several researchers hope to hybridize their proposed methodology by replacing certain components or integrat- ing it with other methods. Some want to integrate a local search into their proposed methodology; Harmony Search Algorithm [9], [25]. [79] would like to implement a tabu search mechanism or simulated annealing in their proposed algorithm; a hybrid of Integer Programming and Hill Climbing.…”

“…Some authors plan to apply their proposed methodologies on other benchmark NRP datasets or instances; Adaptive Neighborhood Search [52], Multi-Assignment Procedures Algorithm [31], a hybrid of Evolutionary Algorithm and Integer Programming [45], a hybrid of Artificial Bee Colony and Hill Climbing Optimization [8], Variable Neighborhood Search [77], Directed Bee Colony Optimization [64], Harmony Search Algorithm [25] and Lexicographic Goal Programming [13].…”

“…A number of researchers intend to employ their proposed methodologies (Variable Neighborhood Search [87] and Harmony Search Algorithm [25]) on other combinatorial optimization problems. Rajeswari et al and Rahimian et al plan to apply Directed Bee Colony Optimization [64] and a hybrid of Integer Programming and Constraint Programming [63] respectively, to other scheduling problems.…”