A tabu search approach with embedded nurse preferences for solving nurse rostering problem | International Journal for Simulation and Multidisciplinary Design Optimization (IJSMDO)

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Table 3

Results based on different criteria for models comparison (TS model = our approach, MA model = Memetic Algorithm model by Ramli (2004) and HG model = manual approach).

Criteria	Measurements	TS model	MA model	HG model
Coverage	1. Minimum coverage requirement met.	Yes	Yes	Yes
	2. Balanced coverage provided.	Moderate	Moderate	No
Quality	1. Equalization of night shift rotation among staff.	Fair	Fair	Fair
	2. Equalization of other shifts distribu- tions in particular period.	Moderate	Moderate	Not fair
	3. Violation of not more than six days work stretch.	No	No	Yes
	4. Violation of circadian ordering.	No	No	Yes
	5. Existence of single day work stretch.	No	No	Yes
	6. Ability to assign public holidays in their designated week or the follow- ing week.	Yes	Yes	No
Flexibility	1. Adapting to change in staffing level.	Easy	Easy	Moderate
	2. Privilege to have choice in selecting final roster as wanted	Yes	Yes	No
Cost	1. The required to construct a roster	About 1 minute	About 12 minutes	3-8 hours
	2. Category of staff required to con- struct the roster	Any senior staff	Any senior staff	Head nurse/ Assistant

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