Iterated local search in nurse rostering problem

Authors:
Sen Ngoc Vu;Minh H. Nhat Nguyen;Le Minh Duc;Chantal Baril;Viviane Gascon;Tien Ba Dinh
Affiliations:
University of Science, Ho Chi Minh City, Viet Nam;University of Science, Ho Chi Minh City, Viet Nam;University of Science, Ho Chi Minh City, Viet Nam;Université du Québec à Trois-Rivières, Québec, Canada;Université du Québec à Trois-Rivières, Québec, Canada;University of Science, Ho Chi Minh City, Viet Nam
Venue:
Proceedings of the Fourth Symposium on Information and Communication Technology
Year:
2013

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Abstract

This paper presents how to solve a nurse rostering problem over the real datasets of Centre hospitalier régional de Trois-Rivières hospital in Canada. Due to the complexity of this problem with plenty of hard constraints, we propose an advanced Iterated Local Search, combining Tabu Search with 2 moves: Single Shift Move and Worst-Scheduled Nurse Swap. Greedy Shuffling with Steepest Descent is also used to improve the solution. Experimental results of our proposed algorithm on 5 real datasets improve the current schedules provided by the hospital. Our experimental results satisfy all of the hard constraints and objectives.