Hybrid and Cooperative Strategies Using Harmony Search and Artificial Immune Systems for Solving the Nurse Rostering Problem

Abstract

Abstract:The nurse rostering problem is an important search problem that features many constraints. In a nurse rostering problem, these constraints are defined by processes such as maintaining work regulations, assigning nurse shifts, and considering nurse preferences. A number of approaches to address these constraints, such as penalty function methods, have been investigated in the literature. We propose two types of hybrid metaheuristic approaches for solving the nurse rostering problem, which are based on combining harmony search techniques and artificial immune systems to balance local and global searches and prevent slow convergence speeds and prematurity. The proposed algorithms are evaluated against a benchmarking dataset of nurse rostering problems; the results show that they identify better or best known solutions compared to those identified in other studies for most instances. The results also show that the combination of harmony search and artificial immune systems is better suited than using single metaheuristic or other hybridization methods for finding upper-bound solutions for nurse rostering problems and discrete optimization problems.

Advances in Multiple Criteria Decision Making for Sustainability: Modeling and Applications

“…The next paper [20] puts its focus on enhanced hybrid and cooperated searching strategies-for the nurse rostering problem (also known as the nurse scheduling problem)-is contributed. Although the direct tie between this problem and sustainability is not highlighted, the proposed algorithm shows superior results by testing a benchmarking data set against the other well-known methods.…”

An Improved Discrete Grey Wolf Optimizer for the Nurse Rostering Problem

“…Other finalists in the competition were: Nonobe [17] who applied a tabu search to control the constraints weight and tabu tenure dynamically; Lu Zhipeng and Hao Jin-Kao [18] who implemented an adaptive neighborhood search that adaptively switches among three strategies (intermediate, diversification and intensive searches); Burke and Curtois [19] who applied a variable depth search (ejection chain based method) on sprint instances and a branch and price algorithm on medium and long instances. Recent approaches for the INRC-I are a hybrid of dynamic programming and variable neighborhood search [20], population-based local search [21], randomized variable neighborhood search [22] and hybrid harmony search [23].…”

ارائه‌ی مدل ریاضی دوهدفه برای مسئله‌ی زمان‌بندی پرستاران با در نظر گرفتن ترجیحات پرستار مبتنی بر تحلیل پوششی داده‌ها

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