Picture of classic books on shelf

Literary linguistics: Open Access research in English language

Strathprints makes available Open Access scholarly outputs by English Studies at Strathclyde. Particular research specialisms include literary linguistics, the study of literary texts using techniques drawn from linguistics and cognitive science.

The team also demonstrates research expertise in Renaissance studies, researching Renaissance literature, the history of ideas and language and cultural history. English hosts the Centre for Literature, Culture & Place which explores literature and its relationships with geography, space, landscape, travel, architecture, and the environment.

Explore all Strathclyde Open Access research...

A hybrid constraint integer programming approach to solve nurse scheduling problems

Rahimian, Erfan and Akartunali, Kerem and Levine, John (2015) A hybrid constraint integer programming approach to solve nurse scheduling problems. In: Mista 2015 Proceedings of the 7th Multidisciplinary International Scheduling Conference. Proceedings of the Multidisciplinary International Conference on Scheduling: Theory and Applications . MISTA, Prague, Czech Republic, pp. 429-442. ISBN 978-0954582104

[img]
Preview
Text (Rahimian-MISTA2015-Hybrid-constraint-integer-programming-approach)
Rahimian_MISTA2015_Hybrid_constraint_integer_programming_approach.pdf
Accepted Author Manuscript

Download (598kB) | Preview

Abstract

The Nurse Scheduling Problem can be simply defined as assigning a series of shift sequences (schedules) to several nurses over a planning horizon according to some constraints and preferences. The inherent benefits of having higher-quality and more flexible schedules are a reduction in outsourcing costs and an increase of job satisfaction in health organizations. In this paper, we present a novel systematic hybrid algorithm, which combines Integer Programming (IP) and Constraint Programming (CP) to efficiently solve highly-constrained Nurse Scheduling Problems. Our focus is to exploit the problem-specific information to improve the performance of the algorithm, and therefore obtain high-quality solutions as well as strong lower bounds. We test our algorithm based on some real-world benchmark instances. Very competitive results are reported compared to the state-of-the-art algorithms from the recent literature, showing that the proposed algorithm is able to solve a wide variety of real-world instances with different complex structures.