Weighted tabu search for multi-stage nurse rostering problem

Wang, Zhuo; Su, Zhouxing; Lv, Zhipeng

doi:10.1360/n112015-00284

Cited by 8 publications

(4 citation statements)

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“…After the proposed algorithm sits Ceschia and Schaerf (2018) with achieving the best results for 5 problem instances. The next algorithm in the ranking is Dang et al (2016), which achieves the best performance for 4 problems. Then is GA which achieves the best results for 3 problems.…”

Section: Resultsmentioning

confidence: 99%

“…The proposed algorithm is compared with the original version of QEA, Genetic Algorithm, Differential Evolution, Fast Evolutionary Strategy, Particle Swarm Optimization, Simulated Annealing algorithms, the algorithm proposed in Song et al (2018), Wang et al (2007), Dang et al (2016), Ceschia and Schaerf (2018) and Legrain et al (2017). In order to perform a fair comparison between the algorithms, for each algorithm, the performance of the algorithms is studied for a set of parameters, and the best parameters for the algorithms are found.…”

Section: Parameter Studymentioning

confidence: 99%

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How to exploit fitness landscape properties of timetabling problem: A new operator for quantum evolutionary algorithm

Najaran

2021

Expert Systems with Applications

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Section: Resultsmentioning

confidence: 99%

Section: Parameter Studymentioning

confidence: 99%

How to exploit fitness landscape properties of timetabling problem: A new operator for quantum evolutionary algorithm

Najaran

2021

Expert Systems with Applications

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“…In the work done by [48], the author proposed an integer programming techniques to solve NRP. On the other hand, [49] solved a dynamic version of NRP which was formulated for the second nurse rostering competition (INRC-II). In this proposed method two solvers were created, which were dependent on Mixed Integer Linear Programming (MILP) and Simulated Annealing respectively.…”

Section: Nrp-based Optimization Methodsmentioning

confidence: 99%

Healthcare scheduling in optimization context: a review

et al. 2021

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This paper offers a summary of the latest studies on healthcare scheduling problems including patients’ admission scheduling problem, nurse scheduling problem, operation room scheduling problem, surgery scheduling problem and other healthcare scheduling problems. The paper provides a comprehensive survey on healthcare scheduling focuses on the recent literature. The development of healthcare scheduling research plays a critical role in optimizing costs and improving the patient flow, providing prompt administration of treatment, and the optimal use of the resources provided and accessible in the hospitals. In the last decades, the healthcare scheduling methods that aim to automate the search for optimal resource management in hospitals by using metaheuristics methods have proliferated. However, the reported results are disintegrated since they solved every specific problem independently, given that there are many versions of problem definition and various data sets available for each of these problems. Therefore, this paper integrates the existing results by performing a comprehensive review and analyzing 190 articles based on four essential components in solving optimization problems: problem definition, formulations, data sets, and methods. This paper summarizes the latest healthcare scheduling problems focusing on patients’ admission scheduling problems, nurse scheduling problems, and operation room scheduling problems considering these are the most common issues found in the literature. Furthermore, this review aims to help researchers to highlight some development from the most recent papers and grasp the new trends for future directions.

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“…Other algorithms have been proposed for the multi-stage nurse rostering problem formulation of INRC-II. Mischek and Nysret [37] and Thi Thanh Dang et al [38] developed hybrid methods between integer programming and various local search algorithms. More recently, Ceschia et al [39] proposed an approach that employs a composition of large neighbourhoods guided by the Simulated Annealing metaheuristic to solve the static version of the problem.…”

Section: Related Workmentioning

confidence: 99%