Nurse Rostering: A Tabu Search Technique With Embedded Nurse Preferences

• Main Author: Siti Nurin Ima, Ahmad
• Format: Thesis
• Language: English; English
• Published: 2010
• Subjects: HD28-70 Management. Industrial Management
• Online Access: http://etd.uum.edu.my/2804/1/Siti_Nurin_Ima_Ahmad.pdf; http://etd.uum.edu.my/2804/2/1.Siti_Nurin_Ima_Ahmad.pdf; http://etd.uum.edu.my/2804/; http://lintas.uum.edu.my:8080/elmu/index.jsp?module=webopac-l&action=fullDisplayRetriever.jsp&szMaterialNo=0000774166

The decision making in assigning all nursing staffs to shift duties in a hospital unit must be done appropriately because it is a crucial task due to various requirements and constraints that need to be fulfilled. The shift assignment or also known as roster has a great impact on the nurses’ operational circumstances which are strongly related to the intensity of quality of health care. The head nurse usually spends a substantial amount of time developing manual rosters, especially when there are many staff requests. Yet, sometimes she could not ensure that all constraints are met. Therefore, this research identified the relevant constraints being imposed in solving the nurse rostering problem (NRP) and examined the efficient method to generate the nurse roster based on constraints involved. Subsequently, as part of this research, we develop a Tabu Search (TS) model to solve a particular NRP. There are two aspects of enhancement in the proposed TS model. The first aspect is in the initialization phase of the TS model, where we introduced a semi-random initialization method to produce an initial solution. The advantage of using this initialization method is that it avoids the violation of hard constraints at any time in the TS process. The second aspect is in the neighbourhood generation phase, where several neighbours need to be generated as part of the TS approach. In this phase, we introduced two different neighbourhood generation methods, which are specific to the NRP. The proposed TS model is evaluated for its efficiency, where 30 samples of rosters generated were taken for analysis. The feasible solutions (i.e. the roster) were evaluated based on their minimum penalty values. The penalty values were given based on different violations of hard and soft constraints. The TS model is able to produce efficient rosters which do not violate any hard constraints and at the same time, fulfill the soft constraints as much as possible. The performance of the model is certainly better than the manually generated model and also comparable to the existing similar nurse rostering model.