الفهرس 
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Abstract
In today’s fast-moving economy, there is a strong need for fast and reliable scheduling systems. The scheduling problems involve allocating suitable staff not only to meet customers’ demand, but also to consider workers’ preferences. Health care sector is of importance to consider the scheduling problems. Improving patient satisfaction, managing costs, and improving the quality of care are the main objectives of any health care unit. This is referred to as the “triple aim” of healthcare reform. This is affected by one of the major problem in hospitals which is the Nurse Scheduling Problem (NSP). Nurse Scheduling Problem (NSP) aims to assign a number of nurses to a number of shifts in order to satisfy hospital’s demand. During the last 50 years, NSP becomes important problem in field of operation research and artificial intelligence. The automatic generation of high quality schedules can lead to improvements in: hospital resource efficiency, staff and patient safety, and administrative workload. The main target of NSP is to assign an optimum number of different skilled nurses for each shift, while minimizing the hospital’s cost, and maximizing the nurses’ preferences. The purpose of this study is to solve the NSP using two different methodologies to generate nurses’ schedules while considering different constrains. The first method is an approximate heuristic-based solution using binary particle swarm optimization. The second approach is to develop an optimization model that based on the idea of multi-commodity network flow model. Both methods were verified using benchmark instances. After that, both approached were applied to a real case study in an Egyptian hospital. The results demonstrate the advantage of using the proposed methods in generating schedules to solve the problem. It proves the superiority of the obtained schedules to those generated manually by the Supervisor Head Nurse as they improve the level of nurses’ satisfaction as well as decreases the overall overtime cost by 36%.