Heterogeneous multi-core FPGAs contain different types of cores, which can improve efficiency when used with an effective online task scheduler. However, it is not easy to find the right cores for tasks when there are...
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ISBN:
(纸本)9781479944781
Heterogeneous multi-core FPGAs contain different types of cores, which can improve efficiency when used with an effective online task scheduler. However, it is not easy to find the right cores for tasks when there are multiple objectives or dozens of cores. Inappropriate scheduling may cause hot spots which decrease the reliability of the chip. Given that, our research builds a simulating platform to evaluate all kinds of scheduling algorithms on a variety of architectures. On this platform, we provide an online scheduler which uses multi-objective evolutionaryalgorithm (EA). Comparing the EA and current algorithms such as Predictive Dynamic Thermal Management (PDTM) and Adaptive Temperature Threshold Dynamic Thermal Management (ATDTM), we find some drawbacks in previous work. First, current algorithms are overly dependent on manually set constant parameters. Second, those algorithms neglect optimization for heterogeneous architectures. Third, they use single-objective methods, or use linear weighting method to convert a multi-objective optimization into a single-objective optimization. Unlike other algorithms, the EA is adaptive and does not require resetting parameters when workloads switch from one to another. EAs also improve performance when used on heterogeneous architecture. A efficient Pareto front can be obtained with EAs for the purpose of multiple objectives.
Supply chain management involves multiple and conflicting objectives. A multi-objective optimization procedure which permits a trade-off evaluation for an integrated location-inventory model is initially presented. In...
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Supply chain management involves multiple and conflicting objectives. A multi-objective optimization procedure which permits a trade-off evaluation for an integrated location-inventory model is initially presented. In this paper, we propose an integrated model to incorporate inventory control decisions - such as economic order quantity, safety stock and inventory replenishment decisions under vendor-managed inventory collaborative initiative into typical facility location models, which are used to solve the distribution network design problem. This model includes elements of total cost, customer service and flexibility as its objectives. Moreover, a multiobjective evolutionary algorithm is developed to determine the optimal facility location portfolio in order to reach best compromise of these conflicting criteria. A hybrid evolutionary approach is proposed and its scenario analysis is implemented on a real large retail supply chain in Taiwan to investigate the model performance and to illustrate how parameter changes influence its output. Some preliminary results are described.
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