Gareth Beddoe

Bio: Gareth Beddoe is an academic researcher from University of Nottingham. The author has contributed to research in topics: Case-based reasoning & Constraint (information theory). The author has an hindex of 6, co-authored 7 publications receiving 295 citations. Previous affiliations of Gareth Beddoe include Information Technology University.

A hybrid metaheuristic case-based reasoning system for nurse rostering

Abstract: In this paper we present a novel Case-Based Reasoning (CBR) system called CABAROST (CAsed-BAsed ROSTering) which was developed for personnel scheduling problems. CBR is used to capture and store examples of personnel manager behaviour which are then used to solve future problems. Previous examples of constraint violations in schedules and the repairs that were used to solve the violations are stored as cases. The sequence in which violations are repaired can have a great impact on schedule quality. A novel memetic algorithm is proposed which evolves good quality sequences of repairs generated by CABAROST. The algorithm was tested on instances of the real-world nurse rostering problem at the Queens Medical Centre NHS Trust in Nottingham.

Storing and adapting repair experiences in employee rostering

Abstract: The production of effective workforce rosters is a common management problem. Rostering problems are highly constrained and require extensive experience to solve manually. The decisions made by expert rosterers are often subjective and are difficult to represent systematically. This paper presents a formal description of a new technique for capturing rostering experience using case-based reasoning methodology. Examples of previously encountered constraint violations and their corresponding repairs are used to solve new rostering problems. We apply the technique to real-world data from a UK hospital.

Enhancing case-based reasoning for personnel rostering with selected tabu search concepts

Abstract: In this paper, we investigate the advantages of using case-based reasoning (CBR) to solve personnel rostering problems. Constraints for personnel rostering problems are commonly categorized as either ‘hard’ or ‘soft’. Hard constraints are those that must be satisfied and a roster that violates none of these constraints is considered to be ‘feasible’. Soft constraints are more flexible and are often used to measure roster quality in terms of staff satisfaction. We introduce a method for repairing hard constraint violations using CBR. CBR is an artificial intelligence paradigm whereby new problems are solved by considering the solutions to previous similar problems. A history of hard constraint violations and their corresponding repairs, which is captured from human rostering experts, is stored and used to solve similar violations in new rosters. The soft constraints are not defined explicitly. Their treatment is captured implicitly during the repair of hard constraint violations. The knowledge in the case-base is combined with selected tabu search concepts in a hybrid meta-heuristic algorithm. Experiments on real-world data from a UK hospital are presented. The results show that CBR can guide a meta-heuristic algorithm towards feasible solutions with high staff satisfaction, without the need to explicitly define soft constraint objectives.

Storing and Adapting Repair Experiences in Employee Rostering

Abstract: The production of effective workforce rosters is a common management problem. Rostering problems are highly constrained and require extensive experience to solve manually. The decisions made by expert rosterers are often subjective and are difficult to represent systematically. This paper presents a formal description of a new technique for capturing rostering experience using case-based reasoning methodology. Examples of previously encountered constraint violations and their corresponding repairs are used to solve new rostering problems. We apply the technique to real-world data from a UK hospital.

Case-based heuristic selection for timetabling problems

Abstract: This paper presents a case-based heuristic selection approach for automated university course and exam timetabling. The method described in this paper is motivated by the goal of developing timetabling systems that are fundamentally more general than the current state of the art. Heuristics that worked well in previous similar situations are memorized in a case base and are retrieved for solving the problem in hand. Knowledge discovery techniques are employed in two distinct scenarios. Firstly, we model the problem and the problem solving situations along with specific heuristics for those problems. Secondly, we refine the case base and discard cases which prove to be non-useful in solving new problems. Experimental results are presented and analyzed. It is shown that case based reasoning can act effectively as an intelligent approach to learn which heuristics work well for particular timetabling situations. We conclude by outlining and discussing potential research issues in this critical area of knowledge discovery for different difficult timetabling problems.

Workforce Planning Incorporating Skills: State of the Art

Abstract: This paper presents a review and classification of the literature regarding workforce planning problems incorporating skills. In many cases, technical research regarding workforce planning focuses very hard on the mathematical model and neglects the real life implications of the simplifications that were needed for the model to perform well. On the other hand, many managerial studies give an extensive description of the human implications of certain management decisions in particular cases, but fail to provide useful mathematical models to solve workforce planning problems. This review will guide the operations researcher in his search to find useful papers and information regarding workforce planning problems incorporating skills. We not only discuss the differences and similarities between different papers, but we also give an overview of the managerial insights. The objective is to present a combination of technical and managerial knowledge to encourage the production of more realistic and useful solution techniques.