Integration of operations research and artificial intelligence approaches to solve the nurse rostering problem

Rights statement
Awarding institution
  • University of Strathclyde
Date of award
  • 2019
Thesis identifier
  • T15185
Person Identifier (Local)
  • 201375180
Qualification Level
Qualification Name
Department, School or Faculty
  • Nurse Rostering can be defined as assigning a series of shift sequences (schedules)to several nurses over a planning horizon according to some limitations and preferences. The inherent benefits of generating higher-quality rosters are a reduction in outsourcing costs and an increase in job satisfaction of employees.This problem is often very dicult to solve in practice, particularly by applying a sole approach. This dissertation discusses two hybrid solution methods to solve the Nurse Rostering Problem which are designed based on Integer Programming,Constraint Programming, and Meta-heuristics. The current research contributes to the scientific and practical aspects of the state of the art of nurse rostering. The present dissertation tries to address two research questions. First, we study the extension of the reach of exact method through hybridisation. That said, we hybridise Integer and Constraint Programming to exploit their complementary strengths in finding optimal and feasible solutions, respectively. Second,we introduce a new solution evaluation mechanism designed based on the problem structure. That said, we hybridise Integer Programming and Variable Neighbourhood Search reinforced with the new solution evaluation method to efficiently deal with the problem. To benchmark the hybrid algorithms, three different datasets with different characteristics are used. Computational experiments illustrate the effectiveness and versatility of the proposed approaches on a large variety of benchmark instances
Advisor / supervisor
  • Levine, John
  • Akartunali, Kerem
Resource Type
  • Please note, incorrect date on spine and title page (2016). Degree was awarded in 2019.
Date Created
  • 2019
Former identifier
  • 9912709593502996