9,295 research outputs found
Building Better Nurse Scheduling Algorithms
The aim of this research is twofold: Firstly, to model and solve a complex nurse scheduling problem with an integer programming formulation and evolutionary algorithms. Secondly, to detail a novel statistical method of comparing and hence building better scheduling algorithms by identifying successful algorithm modifications. The comparison method captures the results of algorithms in a single figure that can then be compared using traditional statistical techniques. Thus, the proposed method of comparing algorithms is an objective procedure designed to assist in the process of improving an algorithm. This is achieved even when some results are non-numeric or missing due to infeasibility. The final algorithm outperforms all previous evolutionary algorithms, which relied on human expertise for modification
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
Partnering Strategies for Fitness Evaluation in a Pyramidal Evolutionary Algorithm
This paper combines the idea of a hierarchical distributed genetic algorithm
with different inter-agent partnering strategies. Cascading clusters of
sub-populations are built from bottom up, with higher-level sub-populations
optimising larger parts of the problem. Hence higher-level sub-populations
search a larger search space with a lower resolution whilst lower-level
sub-populations search a smaller search space with a higher resolution. The
effects of different partner selection schemes for (sub-)fitness evaluation
purposes are examined for two multiple-choice optimisation problems. It is
shown that random partnering strategies perform best by providing better
sampling and more diversity
'On the Application of Hierarchical Coevolutionary Genetic Algorithms: Recombination and Evaluation Partners'
This paper examines the use of a hierarchical coevolutionary genetic algorithm under different partnering strategies. Cascading clusters of sub-populations are built from the bottom up, with higher-level sub-populations optimising larger parts of the problem. Hence higher-level sub-populations potentially search a larger search space with a lower resolution whilst lower-level sub-populations search a smaller search space with a higher resolution. The effects of different partner selection schemes amongst the sub-populations on solution quality are examined for two constrained optimisation problems. We examine a number of recombination partnering strategies in the construction of higher-level individuals and a number of related schemes for evaluating sub-solutions. It is shown that partnering strategies that exploit problem-specific knowledge are superior and can counter inappropriate (sub-) fitness measurements
An Estimation of Distribution Algorithm for Nurse Scheduling
Schedules can be built in a similar way to a human scheduler by using a set of rules that involve domain knowledge. This paper presents an Estimation of Distribution Algorithm (EDA) for the nurse scheduling problem, which involves choosing a suitable scheduling rule from a set for the assignment of each nurse. Unlike previous work that used Genetic Algorithms (GAs) to implement implicit learning, the learning in the proposed algorithm is explicit, i.e. we identify and mix building blocks directly. The EDA is applied to implement such explicit learning by building a Bayesian network of the joint distribution of solutions. The conditional probability of each variable in the network is computed according to an initial set of promising solutions. Subsequently, each new instance for each variable is generated by using the corresponding conditional probabilities, until all variables have been generated, i.e. in our case, a new rule string has been obtained. Another set of rule strings will be generated in this way, some of which will replace previous strings based on fitness selection. If stopping conditions are not met, the conditional probabilities for all nodes in the Bayesian network are updated again using the current set of promising rule strings. Computational results from 52 real data instances demonstrate the success of this approach. It is also suggested that the learning mechanism in the proposed approach might be suitable for other scheduling problems
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