Selecting and weighting features using a genetic algorithm in a case-based reasoning approach to personnel rostering

Beddoe, Gareth; Petrović, Sanja

doi:10.1016/j.ejor.2004.12.028

Cited by 95 publications

(54 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the past decades several researchers have studied CBR methods in real world applications, such as medical diagnosis [19], [20], product recommendation [21] and personal rostering decisions [22]. CBR is a cyclic and integrated process of solving a problem and learning from the experience of experts, which is used to build a knowledge domain which is then recorded to be used to help solve future problems.…”

Section: Literature Review Of Cbr and Other Types Of Knowledgementioning

confidence: 99%

“…First, to integrate feature weighting (FW) and feature selection (FS) into KNN. In this framework, FW is used to estimate the optimal weights of the original features of cases [27], [28] , and FS is employed when choosing relevant features of cases [20], [22], or their aggregation is used to leverage their usefulness [19]. Second, to merge data clustering with KNN, where the structure of clustered cases is leveraged to lead to more relevant cases [29], [30].…”

Section: Data Mining and Cbrmentioning

confidence: 99%

See 1 more Smart Citation

A New Strategy for Case-Based Reasoning Retrieval Using Classification Based on Association

Aljuboori

Meziane

Parsons

2016

Machine Learning and Data Mining in Pattern Recognition

View full text Add to dashboard Cite

Section: Literature Review Of Cbr and Other Types Of Knowledgementioning

confidence: 99%

Section: Data Mining and Cbrmentioning

confidence: 99%

A New Strategy for Case-Based Reasoning Retrieval Using Classification Based on Association

Aljuboori

Meziane

Parsons

2016

Machine Learning and Data Mining in Pattern Recognition

View full text Add to dashboard Cite

“…Other new approaches and methodologies for timetabling problems have also been studied as more problem solving experience is collected and new technologies provide new breakthroughs. These include Case-Based Reasoning (Leake, 1996) on educational timetabling (Burke, MacCarthy et al 2000, 2003&2005, Burke, Petrovic and Qu, 2006 and on nurse rostering (Beddoe and Petrovic, 2005), fuzzy methodology on exam timetabling (Asmuni, Burke and Garibaldi, 2004), and hyper-heuristics on timetabling (Burke, Kendall and Soubeiga, 2003, Gaw, Rattadilok and Kwan, 2004, Burke, Dror et al, 2005, Burke, Petrovic and Qu, 2006.…”

Section: Approaches and Techniques In Timetabling Problemsmentioning

confidence: 99%

A graph-based hyper-heuristic for educational timetabling problems

Burke

McCollum

Meisels

et al. 2007

European Journal of Operational Research

Self Cite

412

239

View full text Add to dashboard Cite

This paper presents an investigation of a simple generic hyper-heuristic approach upon a set of widely used constructive heuristics (graph coloring heuristics) in timetabling. Within the hyperheuristic framework, a Tabu Search approach is employed to search for permutations of graph heuristics which are used for constructing timetables in exam and course timetabling problems. This underpins a multi-stage hyper-heuristic where the Tabu Search employs permutations upon a different number of graph heuristics in two stages. We study this graph-based hyper-heuristic approach within the context of exploring fundamental issues concerning the search space of the hyper-heuristic (the heuristic space) and the solution space. Such issues have not been addressed in other hyper-heuristic research. These approaches are tested on both exam and course benchmark timetabling problems and are compared with the fine-tuned bespoke state-of-the-art approaches. The results are within the range of the best results reported in the literature. The approach described here represents a significantly more generally applicable approach than the current state of the art in the literature. Future work will extend this hyper-heuristic framework by employing methodologies which are applicable on a wider range of timetabling and scheduling problems.

show abstract

“…Column generation was employed in nurse scheduling (Bard and Purnomo, 2005) using set covering-type models and integer programming. Knowledge based techniques have also been explored for solving nurse rostering problems (Beddoe andPetrovic, 2005, Lukman et al, 1991).…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Method For Nurse Rostering Based On Granular Pre-Processing Of Constraints

Baskaran

Bargieła

2009

ECMS 2009 Proceedings Edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel Pedrera

View full text Add to dashboard Cite

Nurse Rostering problems represent a subclass of scheduling problems that are hard to solve. Their complexity is due to the large solution spaces and the many objectives and constraints that need to be fulfilled. In this study, we propose a hierarchical method of granulation of problem domain through preprocessing of constraints. A set of zero-cost patterns in the granulated search space provides a basis for the generation of work schedules. Feasible schedules calculated for week 1 are used to define zero-cost shift patterns that can be deployed in week 2. These in turn are used for the generation of feasible schedules for week 2. The process can be applied over the extended time frame. We show that the granulation of the problem description in terms of constraints and scheduling time frames leads to a more manageable computing task.

show abstract

Selecting and weighting features using a genetic algorithm in a case-based reasoning approach to personnel rostering

Cited by 95 publications

References 12 publications

A New Strategy for Case-Based Reasoning Retrieval Using Classification Based on Association

A New Strategy for Case-Based Reasoning Retrieval Using Classification Based on Association

A graph-based hyper-heuristic for educational timetabling problems

Hierarchical Method For Nurse Rostering Based On Granular Pre-Processing Of Constraints

Contact Info

Product

Resources

About