Healthcare Staff Scheduling in a Fuzzy Environment: A Fuzzy Genetic Algorithm Approach

In the presence of imprecise management targets, staff preferences, and patients’ expectations, the healthcare staff scheduling problem becomes complicated. The goals, preferences, and client expectations, being humanistic, are often imprecise and always evolving over time. We present a fuzzy genetic algorithm (FGA) approach for addressing healthcare staff scheduling problems in fuzzy environments. The proposed FGA-based approach can handle multiple conflicting objectives and constraints. To improve the algorithm, fuzzy set theory is used for fitness evaluations of alternative candidate schedules by modeling the fitness of each alternative solution using fuzzy membership functions. Furthermore, the algorithm is designed to incorporate the decision maker’s choices and preferences, in addition to staff preferences. Rather than prescribing a sing solution to the decision maker, the approach provides a population of alternative solutions from which the decision maker can choose the most satisfactory solution. The FGA-based approach is potential platform upon which useful decision support tools can be developing for solving healthcare staff scheduling problems in a fuzzy environment characterized with multiple conflicting objectives and preference constraints.