A Simple Evolutionary Algorithm with Self-adaptation for Multi-objective Nurse Scheduling
We present a multi-objective approach to tackle a real-world nurse scheduling problem using an evolutionary algorithm. The aim is to generate a few good quality non-dominated schedules so that the decision-maker can select the most appropriate one. Our ap
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Summary. We present a multi-objective approach to tackle a real-world nurse scheduling problem using an evolutionary algorithm. The aim is to generate a few good quality non-dominated schedules so that the decision-maker can select the most appropriate one. Our approach is designed around the premise of ‘satisfying individual nurse preferences’ which is of practical significance in our problem. We use four objectives to measure the quality of schedules in a way that is meaningful to the decision-maker. One objective represents staff satisfaction and is set as a target. The other three objectives, which are subject to optimisation, represent work regulations and workforce demand. Our algorithm incorporates a self-adaptive decoder to handle hard constraints and a re-generation strategy to encourage production of new genetic material. Our results show that our multi-objective approach produces good quality schedules that satisfy most of the nurses’ preferences and comply with work regulations and workforce demand. The contribution of this paper is in presenting a multi-objective evolutionary algorithm to nurse scheduling in which increasing overall nurses’ satisfaction is built into the self-adaptive solution method. Keywords: Multi-objective, nurse scheduling, evolutionary algorithms, decoder, constraints.
1 Introduction Producing good quality nurse schedules helps to provide better healthcare service, to improve overall job satisfaction and to make more efficient use of workforce. We are interested in tackling the nurse scheduling problem in a multiobjective fashion using an evolutionary algorithm. According to Ernst et al., the tendency in the modern workplace is to focus on individuals rather than on teams and hence, personnel schedules should cater to individual preferences [1]. This is particularly true in nurse scheduling because it is common that each nurse indicates his/her preference schedule. In our multi-objective approach, we set a target for nurse preference satisfaction and attempt to minimise the violation C. Cotta et al. (Eds.): Adaptive and Multilevel Metaheuristics, SCI 136, pp. 133–155, 2008. c Springer-Verlag Berlin Heidelberg 2008 springerlink.com
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D. Landa-silva and K.N. Le
of soft constraints related to work regulations and workforce demand. We refer to nurse scheduling as the construction of rosters for a ward of nurses over a short scheduling period (typically a few weeks). A roster can be defined as an assignment of personnel to specific shifts and/or duties. Here, a nurse schedule is a roster in which a line of work, made of shifts and days off, is assigned to each nurse in the ward over the scheduling period. For a discussion of other phases in the overall personnel scheduling process (e.g. demand modelling, task assignment, etc.) see [2]. Many healthcare institutions use some kind of software to aid the construction of nurse schedules but in many other cases this is still done manually [3]. For problems of considerable size, the non-automated construction of nurse schedules is time con
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