4 resultados para Assignment of lease
em Nottingham eTheses
Resumo:
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.
Resumo:
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.
Resumo:
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.
Resumo:
Purpose – The purpose of this research is to show how the self-archiving of journal papers is a major step towards providing open access to research. However, copyright transfer agreements (CTAs) that are signed by an author prior to publication often indicate whether, and in what form, self-archiving is allowed. The SHERPA/RoMEO database enables easy access to publishers' policies in this area and uses a colour-coding scheme to classify publishers according to their self-archiving status. The database is currently being redeveloped and renamed the Copyright Knowledge Bank. However, it will still assign a colour to individual publishers indicating whether pre-prints can be self-archived (yellow), post-prints can be self-archived (blue), both pre-print and post-print can be archived (green) or neither (white). The nature of CTAs means that these decisions are rarely as straightforward as they may seem, and this paper describes the thinking and considerations that were used in assigning these colours in the light of the underlying principles and definitions of open access. Approach – Detailed analysis of a large number of CTAs led to the development of controlled vocabulary of terms which was carefully analysed to determine how these terms equate to the definition and “spirit” of open access. Findings – The paper reports on how conditions outlined by publishers in their CTAs, such as how or where a paper can be self-archived, affect the assignment of a self-archiving colour to the publisher. Value – The colour assignment is widely used by authors and repository administrators in determining whether academic papers can be self-archived. This paper provides a starting-point for further discussion and development of publisher classification in the open access environment.