Engineering and Information Technology Collected Works - Research Publications
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ItemModelling and simulating retail management practices: a first approach(Inderscience Publishers, 2009)Context: The vaccine distribution for the coronavirus disease of 2019 (COVID-19) is a multicriteria decision-making (MCDM) problem based on three issues, namely, identification of different distribution criteria, importance criteria and data variation. Thus, the Pythagorean fuzzy decision by opinion score method (PFDOSM) for prioritising vaccine recipients is the correct approach because it utilises the most powerful MCDM ranking method. However, PFDOSM weighs the criteria values of each alternative implicitly, which is limited to explicitly weighting each criterion. In view of solving this theoretical issue, the fuzzy-weighted zeroinconsistency (FWZIC) can be used as a powerful weighting MCDM method to provide explicit weights for a criteria set with zero inconstancy. However, FWZIC is based on the triangular fuzzy number that is limited in solving the vagueness related to the aforementioned theoretical issues. Objectives: This research presents a novel homogeneous Pythagorean fuzzy framework for distributing the COVID-19 vaccine dose by integrating a new formulation of the Pythagorean fuzzy-weighted zero-inconsistency (PFWZIC) and PFDOSM methods. Methods: The methodology is divided into two phases. Firstly, an augmented dataset was generated that included 300 recipients based on five COVID-19 vaccine distribution criteria (i.e., vaccine recipient memberships, chronic disease conditions, age, geographic location severity and disabilities). Then, a decision matrix was constructed on the basis of an intersection of the ‘recipients list’ and ‘COVID-19 distribution criteria’. Then, the MCDM methods were integrated. An extended PFWZIC was developed, followed by the development of PFDOSM. Results: (1) PFWZIC effectively weighted the vaccine distribution criteria. (2) The PFDOSM-based group prioritisation was considered in the final distribution result. (3) The prioritisation ranks of the vaccine recipients were subject to a systematic ranking that is supported by high correlation results over nine scenarios of the changing criteria weights values. A comparison with previous work also proved the efficiency of the proposed framework. Conclusion: The findings of this study are expected to contribute to ensuring equitable protection against COVID-19 and thus help accelerate vaccine progress worldwide.
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ItemTailored Shaping and Time Resampling Functions for Inversion Pulses at 7T(ISMRM, 2009)The RF transmit field is severely inhomogeneous at ultrahigh field, due to both RF penetration and RF coil design issues. Here we utilised a search algorithm to produce inversion pulses tailored to take account of the heterogeneity of the RF transmit field at 7T. We created a slice selective inversion pulse which worked well over the range of RF amplitudes, while maintaining an experimentally achievable pulse length at 7T. The pulses were based on the FOCI technique as well as time dilation of functions but the RF amplitude, frequency sweep and gradient functions were all optimised using a Genetic Algorithm.
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ItemNo Preview AvailableArtificial Immune Systems: 8th International Conference, ICARIS 2009(Springer, 2009)
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ItemNo Preview Available
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ItemHandbook of metaheuristics (International series in operations research and management science)(PALGRAVE PUBLISHERS LTD, 2005-05)
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Item[Review of the book The Oxford Dictionary of Statistical Terms, edited by Y. Dodge](Taylor & Francis, 2004-09-01)
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Item[Review of the book Operations Research Calculations Handbook, by D. Blumenfeld](Taylor & Francis, 2004-02-01)
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ItemA Recommender System based on the Immune Network(Hewlett-Packard Company, 2002)The immune system is a complex biological system with a highly distributed, adaptive and self-organising nature. This paper presents an artificial immune system (AIS) that exploits some of these characteristics and isapplied to the task of film recommendation by collaborative filtering (CF). Natural evolution and in particular the immune system have not been designed for classical optimisation. However, for this problem, we are not interested in finding a single optimum. Rather we intend to identify a sub-set of good matches on which recommendations can be based. It is our hypothesis that an AIS built on two central aspects of the biological immune system will be an ideal candidate to achieve this: Antigen - antibody interaction for matching and antibody - antibody interaction for diversity. Computational results are presented in support of this conjecture and compared to those found by other CF techniques.
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ItemNo Preview AvailableA Pyramidal Genetic Algorithm for Multiple-Choice Problems(Operational Research, 2001)
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ItemA Component-Based Heuristic Search Method with Evolutionary Eliminations for Hospital Personnel Scheduling(INFORMS, 2009-06-01)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 the 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 for them to remain there. This demonstration employs an evaluation function that evaluates how well each component contributes toward the final objective. Two elimination steps are then applied: the first elimination removes 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.