Author:
Makoto Ohki
Affiliation:
Department of Information and Electronics, Graduate School of Tottori University, 4, 101 Koyama-Minami, Tottori and Tottori 680-8552 Japan
Keyword(s):
Many-objective Optimization, Combinatorial Optimization, Nurse Scheduling, Evolutionary Algorithm, NSGA-II, Pareto Partial Dominance, Sub-set Size Scheduling.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing
Abstract:
This paper describes a nurse scheduling in Japanese standard general hospitals. In the standard general hospital in Japan basically three shift system is adopted for nurses working in there. We have compiled evaluations of the monthly nurse schedule into twelve penalty functions in the past work. These twelve penalty functions are translated to twelve objective functions in this paper. The nurse scheduling with twelve objective functions is solved as a multi-objective optimization problem by means of NSGA-II. The optimization is insufficient when NSGA-II is applied to such an optimization problem with four or more objective functions, known as a many-objective optimization problem. One method for reducing this problem is a technique based on Pareto partial dominance. In this technique, the partial non-dominated sorting is executed by using a subset selected from all objective functions. In the conventional technique, the schedule of subset size over optimization has to be prepared be
forehand in the form of a list. Moreover, the selection list brings a great influence on the result of optimization. Creating such a selection list is a heavy burden for the user. This paper proposes a technique of NSGA-II based on Pareto partial dominance with a linear subset-size scheduling. By embedding the subset-size scheduling into the algorithm, the user, namely the chief nurse, is released from the designing of the selection list.
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