An Estimation of Distribution Algorithm with Intelligent Local Search for Rule-based Nurse Rostering

TL;DR

This paper introduces a memetic estimation of distribution algorithm with explicit learning and local search for rule-based nurse rostering, demonstrating superior performance on real-world problems.

Contribution

It presents a novel memetic EDA with explicit learning via probabilistic modeling and local search, improving nurse rostering solutions over existing methods.

Findings

Outperforms most existing nurse rostering approaches

Uses explicit learning to identify effective nurse-rule building blocks

Shows applicability to other rule-based scheduling problems

Abstract

This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work (where learning is implicit), the learning in the memetic estimation of distribution algorithm is explicit, i.e. we are able to identify building blocks directly. The overall approach learns by building a probabilistic model, i.e. an estimation of the probability distribution of individual nurse-rule pairs that are used to construct schedules. The local search processor (i.e. the ant-miner) reinforces nurse-rule pairs that receive higher rewards. A challenging real world nurse rostering problem is used as the test problem. Computational results show that the proposed approach outperforms most existing approaches. It is suggested that the learning methodologies suggested in this paper may be applied to other scheduling problems where schedules are built systematically according to specific rules