This paper considers the uniform parallel machine scheduling problem with unequal release dates and delivery times to minimize the maximum completion *** this NP-hard problem,the largest sum of release date,processing...
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This paper considers the uniform parallel machine scheduling problem with unequal release dates and delivery times to minimize the maximum completion *** this NP-hard problem,the largest sum of release date,processing time and delivery time first rule is designed to determine a certain machine for each job,and the largest difference between delivery time and release date first rule is designed to sequence the jobs scheduled on the same machine,and then a novel algorithm for the scheduling problem is *** evaluate the performance of the proposed algorithm,a lower bound for the problem is *** accuracy of the proposed algorithm is tested based on the data with problem size varying from 200 jobs to 600 *** computational results indicate that the average relative error between the proposed algorithm and the lower bound is only 0.667%,therefore the solutions obtained by the proposed algorithm are very accurate.
The fractal dimension based clustering ensembles algorithm was studied. It introduced clustering algorithm based on fractal dimension at first in order to create partitions for clustering ensembles, then using voting ...
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The fractal dimension based clustering ensembles algorithm was studied. It introduced clustering algorithm based on fractal dimension at first in order to create partitions for clustering ensembles, then using voting strategy to get ensembles result. Finally, an idea on distributed clustering ensembles under cloud computing environment was brief discussed. Fractal dimension based clustering ensembles algorithm can offer better solutions in terms of robustness, novelty and stability than the single clustering algorithm based on fractal dimension. Combining the approaches based on grid and fractal, the clustering algorithm called grid and fractal dimension based clustering algorithm (GFDC) was presented to create partitions for clustering ensembles instead of clustering algorithm based on fractal dimension. GFDC is able to capture arbitrary shapes and non-neighboring clustering and can be applied to the massive and high-dimension dataset.
Fractal data mining technology is based on the fractal characteristic of data set, the real data set usually exists approximate fractal characteristic in the fractal non-scaling interval. Fractal dimension can describ...
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Workflow mining is a concept in early era, it analyzes the workflow log in the control flow dimensions, to mining the actual process model, but ignores relationships among executors in workflow, hence it fails to effe...
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In order to solve the problem of high dimension in text classification, the paper proposes a method based on manifold learning and Bagging for text classification which imports manifold learning algorithm for dimensio...
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Nowadays, more and more large IT projects need to unit several entities to accomplish together, which forms project-oriented and geographically distributed virtual organizations and leads to new distributed risks. Thi...
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By introducing niche ideas based on sharing mechanism into the domain of interactive evolutionary computation, an interactive genetic algorithm based on improved sharing mechanism (ISMIGA) is developed. In the algorit...
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Currently, the earlier detection, diagnosis and treatment to breast cancer still mainly depend on physicians' experience and knowledge. Case-Based Reasoning(CBR) mimics oncologists' real thinking process and t...
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ISBN:
(纸本)9783037851555
Currently, the earlier detection, diagnosis and treatment to breast cancer still mainly depend on physicians' experience and knowledge. Case-Based Reasoning(CBR) mimics oncologists' real thinking process and therefore is appropriate to the diagnosis decisionmaking. In CBR, weight derivation as a key step is commonly conducted by expert score approaches using Delphi method. The accuracy of case matching largely changes with the selection and experience of experts. In this paper, information entropy for weight determination is introduced into the CBR. We conduct experimental studies to compare the performance of Delphi method and information entropy. The results suggest that: generally, information entropy is a better approach to weight derivation.
Service supply chain features human players as service vendor, service integrator, customer and service resource. It tends to be digitally connected, such as consulting, e-business and integrated enterprises. Our stud...
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Service supply chain features human players as service vendor, service integrator, customer and service resource. It tends to be digitally connected, such as consulting, e-business and integrated enterprises. Our study uses a formal model and simulations to develop the effect of a service supply chain on equilibrium computation. Two insights arise on how a network can obtain equilibrium computation: forming the network structure of service supply chain; exploring entities behavior and equilibrium conditions. These results highlight the importance for service supply chain of adapting its network structure to equilibrium and application.
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