A Component Based Heuristic Search Method with Evolutionary Eliminations

Nurse rostering is a complex scheduling problem that affects hospital personnel on a daily basis all over the world. This paper presents a new component-based approach with evolutionary eliminations, for a nurse scheduling problem arising at a major UK hospital. The main idea behind this technique is to decompose a schedule into its components (i.e. the allocated shift pattern of each nurse), and then to implement two evolutionary elimination strategies mimicking natural selection and natural mutation process on these components respectively to iteratively deliver better schedules. The worthiness of all components in the schedule has to be continuously demonstrated in order for them to remain there. This demonstration employs an evaluation function which evaluates how well each component contributes towards the final objective. Two elimination steps are then applied: the first elimination eliminates a number of components that are deemed not worthy to stay in the current schedule; the second elimination may also throw out, with a low level of probability, some worthy components. The eliminated components are replenished with new ones using a set of constructive heuristics using local optimality criteria. Computational results using 52 data instances demonstrate the applicability of the proposed approach in solving real-world problems.Comment: 27 pages, 4 figure

A time predefined variable depth search for nurse rostering

This paper presents a variable depth search for the nurse rostering problem. The algorithm works by chaining together single neighbourhood swaps into more effective compound moves. It achieves this by using heuristics to decide whether to continue extending a chain and which candidates to examine as the next potential link in the chain. Because end users vary in how long they are willing to wait for solutions, a particular goal of this research was to create an algorithm that accepts a user specified computational time limit and uses it effectively. When compared against previously published approaches the results show that the algorithm is very competitive

Finding regions of local repair in hierarchical constraint satisfaction

Algorithms for solving constraint satisfcation problems (CSP) have been successfully applied to several fields including scheduling, design, and planning. Latest extensions of the standard CSP to constraint optimization problems (COP) additionally provided new approaches for solving several problems of combinatorial optimization more efficiently. Constraint-based search basically embraces two classes of algorithms: heuristic or local search methods and systematic tree search extended by several constraint-processing techniques. Both paradigms exhibit characteristic advantages and drawbacks. This report presents a new approach for solving constraint optimization problems that combines the advantages of local search and sophisticated tree search algorithms. This method proved applicability in a commercial nurse scheduling system as well as on randomly generated problems. (orig.)SIGLEAvailable from TIB Hannover: RR 1812(97-05) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekBundesministerium fuer Bildung, Wissenschaft, Forschung und Technologie, Bonn (Germany)DEGerman

Knowledge validation and exploration by global analysis - VEGA Final report

The VEGA project (Knowledge Validation and Exploration by Global Analysis) developed methods and techniques for the evolution of declarative knowledge bases. To this end the following were carried out: 1. The declarative representation language DRL, which as a sorted Hom logic with finite domains, is rich enough to save evolution results, but not too complex to be processed by evolution algorithms: 2. The knowledge evolution system KES, which combines algorithms for the validation (e.g. integrity clauses) and for evolution (e.g. inductive logic programming) in order to interactively maintain DRL represented knowledge: 3. User knowledge bases APPLKB, which allow a check of the DRL-KES interaction from the knowledge bases on product and production planning, medication side effects, picture diagnosis and driverless transport systems. (orig.)Available from TIB Hannover: F97B1354+a / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEBundesministerium fuer Bildung, Wissenschaft, Forschung und Technologie, Bonn (Germany)DEGerman

Architektur fuer ein System zur Dokumentanalyse im Unternehmenskontext Integration von Datenbestaenden, Aufbau- und Ablauforganisation

Available from TIB Hannover: RR 1813(98-01)+1 / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEBundesministerium fuer Bildung, Wissenschaft, Forschung und Technologie, Bonn (Germany)DEGerman

Anforderungen an ein System zur Dokumentanalyse im Unternehmenskontext -Integration von Datenbestaenden, Aufbau- und Ablauforganisation

Available from TIB Hannover: RR 1813(97-05) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEBundesministerium fuer Bildung, Wissenschaft, Forschung und Technologie, Bonn (Germany)DEGerman

A scatter search methodology for the nurse rostering problem

The benefits of automating the nurse scheduling process in hospitals include reducing the planning workload and associated costs and being able to create higher quality and more flexible schedules. This has become more important recently in order to retain nurses and to attract more people into the profession. Better quality rosters also reduce fatigue and stress due to overwork and poor scheduling and help to maximise the use of leisure time by satisfying more requests. A more contented workforce will lead to higher productivity, increased quality of patient service and a better level of healthcare. This paper presents a scatter search approach for the problem of automatically creating nurse rosters. Scatter search is an evolutionary algorithm, which has been successfully applied across a number of problem domains. To adapt and apply scatter search to nurse rostering, it was necessary to develop novel implementations of some of scatter search's subroutines. The algorithm was then tested on publicly available real-world benchmark instances and compared against previously published approaches. The results show the proposed algorithm is a robust and effective method on a wide variety of real-world instances