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In this paper, the problem of computing budget allocation for finding the simplest good enough design is considered.
The objective of the present work is to propose hybrid algorithm with the application of the nested partitioning (NP) method and the optimal computing budget allocation (OCBA) method to reduce the computational load, hence, to improve the efficiency of supply chain optimization via discrete event simulation.
In this process, the optimal computing budget allocation technique is applied so as to fully utilize the computational resource and potentially save the computational time.
An efficient selection procedure is then designed within the optimal computing budget allocation (OCBA) framework.
Equal allocation (EA), optimal computing budget allocation (OCBA), and OCBA-m are compared over multiple numerical examples.
The proposed approach comprises particle swarm optimization (PSO), constriction factor PSO (CPSO), and elite group optimal computing budget allocation (EGOCBA).
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Forrester predicts that storage will account for 17% of large firms' computing budgets by 2003, up from 4% in 1999; already, spending on storage exceeds spending on web servers.
Sun's N1 (it stands for "Network One") is largely aimed at network management, where it figures 80% of computing budgets go.
These results demonstrate that when limited computing budgets are available, optimal/near-optimal search strategies over multiple search platforms can be obtained more efficiently using SGHC algorithms compared to other generalized hill climbing algorithms.
Even worse, they direct the leader of the House Budget Committee to ignore several costs when computing the budget impact of future actions, as if the costs are the natural course of politics for which no payment is required.
In this section, we compute the budget allocation where the competitive equilibrium meets power demands or for all users under various number of channels and number of users.
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