Periodic Mutation Operator for Nurse Scheduling by Using Cooperative GA

Periodic Mutation Operator for Nurse Scheduling by Using Cooperative GA

Makoto Ohki (Totorri University, Japan)
Copyright: © 2012 |Pages: 16
DOI: 10.4018/jaec.2012070101
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Abstract

This paper proposes an effective mutation operator for Cooperative Genetic Algorithm (CGA) to be applied to a practical Nurse Scheduling Problem (NSP). NSP is a complex combinatorial optimizing problem for which many requirements must be considered. The changes of the shift schedule yields various problems, for example, a drop in the nursing level. The author describes a technique of the reoptimization of the nurse schedule in response to a change. CGA well suits local search, but its failure to handle global search leads to inferior solutions. CGA is superior in ability for local search by means of its crossover operator, but often stagnates at the global search. To solve this problem, a mutation operator activated is proposed depending on the optimization speed. This mutation yields small changes in the population depending on the optimization speed. Then the population is able to escape from a local minimum area by means of the mutation. However, this mutation operator is composed of two well-defined parameters. This means that users have to consider the value of the parameters carefully. To solve this problem, a periodic mutation operator is proposed which has only one parameter to define itself. This simplified mutation operator is effective over a wide range of the parameter value.
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1. Introduction

General hospitals consist of several sections such as the internal medicine department and the pediatrics department. Each section is organized by about fifty to thirty nursing staffs. A section manager makes a roster, or a shift schedule, of all nurses in her/his section every month. The manager considers more than fifteen requirements for this scheduling. Such the schedule arrangement, in other words, the nurse scheduling, is a very complex task. In our investigation, even a veteran manager spends one or two weeks to complete nurse scheduling. This means a great loss of work force and time. Therefore, computer software for the nurse scheduling has recently come to be required in the general hospitals (Goto, Aze, Yamagishi, Hirota, & Fujii, 1993; Berrada, Ferland, & Michelon, 1996; Takaba, Maeda, & Sakaba, 1998; Ikegami, 2001; Kawanaka, Yamamoto, Yoshikawa, Shinogi, & Tsuruoka, 2002; Inoue, Furuhashi, Maeda, & Takabane, 2002; Itoga, Taniguchi, Hoshino, & Kamei, 2003; Cheang, Li, Lim, & Rodrigues, 2003; Burke, De Causmaecker, & Berge, 2004; Burke, De Causmaecker, Berghe, & Landeghem, 2004; Burke, 2001; Burke, De Causmaecker, Petrovic, & Berge, 2006; Ernst, Jiang, Krishnamoorthy, Owens, & Sier, 2004; Li & Aickelin, 2004; Bard & Purnomo, 2005, 2007; Oezcan, 2005).

In an early study (Goto, Aze, Yamagishi, Hirota, & Fujii, 1993), NSP, defined as a discrete planning problem, is solved by using Hopfield model type neural network. Berrada, Ferland, and Michelon (1996) have proposed a technique to define the nurse scheduling problem as a multi-objective problem and to solve it by using a simple optimizing algorithm. The technique by Takaba, Maeda, and Sakaba (1998) provides a simple editing tool and simple GA for the nurse scheduling under Visual Basic environment. There are several techniques (Ikegami, 2001; Inoue, Furuhashi, Maeda, & Takabane, 2002; Bard & Purnomo, 2005, 2007) that require the user to modify or select the nurse schedule in the middle or the final stage of the optimization. Burke, De Causmaecker, and Berge (2004), Burke, De Causmaecker, Berghe, and Landeghem (2004), Burke (2001), and Burke, De Causmaecker, Petrovic, and Berge (2001, 2006) apply a memetic approach to the nurse scheduling problem. Burke, De Causmaecker, Petrovic, and Berghe (2001) also define a technique to evaluate the nurse schedule. Croce and Salassa (2010) propose a variable neighborhood search technique for the nurse scheduling. However, the scheduling problem defined in this manuscript is too easy. And the technique proposed in this manuscript is applied to a private hospital in Italy. Real problem of the nurse scheduling in the general hospital is not so easy and very hard to solve.

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