A Hybrid AI Approach for Nurse Rostering Problem

• Main Authors: LI, Haiping, LIM, Andrew, RODRIGUES, Brian
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2003
• Subjects: Algorithms; Constraint theory; Problem solving; Random processes; rostering; Health and Medical Administration; Operations and Supply Chain Management
• Online Access: https://ink.library.smu.edu.sg/lkcsb_research/2070; https://doi.org/10.1145/952532.952675
• Institution: Singapore Management University

This paper presents a hybrid AI approach for a class of over-constrained Nurse Rostering Problems. Our approach comes in two phases. The first phase solves a relaxed version of problem which only includes hard rules and part of nurses' requests for shifts. This involves using a forward checking algorithm with non-binary constraint propagation, variable ordering, random value ordering and compulsory backjumping. In the second phase, adjustments with descend local search and tabu search are applied to improve the solution. This is to satisfy the preference rules as far as possible. Experiments show that our approach is able to solve this class of problems well.