Abstract
Healthcare staff scheduling is often inundated with fuzzy conflicting (i) patient preferences (ii) staff preferences, and (iii) management goals. In such a fuzzy multi-criteria situation, the decision maker needs interactive fuzzy evaluation heuristics for effective decision making. Hence, the aim of this thesis is to develop fuzzy multi-criteria heuristic approaches for solving healthcare staff scheduling problems.
This thesis comprises three parts: The first part develops multi-criteria fuzzy heuristic approaches to address nurse scheduling problems with conflicting fuzzy goals and nurse preferences. An enhanced fuzzy simulated evolution algorithm and a novel fuzzy simulated metamorphosis algorithm are developed, based on fuzzy evaluation techniques and problem specific heuristics. The approaches can model fuzzy preferences, incorporate decision maker’s choices, and provide reliable solutions efficiently.
The second part focuses on homecare staff scheduling in a home healthcare setting where management goals, staff preferences, and patient preferences are fuzzy. The objective is to construct high quality schedules with minimal violation of patient preferences, fair staff workload, and minimal schedules costs. A novel grouping particle swarm optimization algorithm is proposed for the problem. Computational results show that the algorithm can efficiently provide a pool of optimal or near-optimal solutions.
The third part focuses on daily assignment of healthcare tasks to care workers in a hospital setting, so that patients receive the expected healthcare service, howbeit, with minimal violation of restrictions on care giver capacity and task precedence relationships. By viewing the problem...
D.Ing.