scholarly journals Optimum Allocation Method of Standby Taxi Vehicles at Taxi Stands

2013 ◽  
pp. 3-10 ◽  
Author(s):  
Takashi Tanizaki
Keyword(s):  
Optimum Allocation ◽  
Allocation Method

Hybrid maize breeding with doubled haploids: Comparison between selection criteria

2006 ◽  
Vol 54 (3) ◽  
pp. 343-350 ◽  
Author(s):  
C. F. H. Longin ◽  
H. F. Utz ◽  
A. E. Melchinger ◽  
J.C. Reif
Keyword(s):  
Selection Criteria ◽  
Doubled Haploids ◽  
Optimum Allocation ◽  
Finite Sample ◽  
Sample Sizes ◽  
Maize Breeding ◽  
Selection Gain ◽  
Breeding Programmes ◽  
Hybrid Maize ◽  
Breeding Resources

The optimum allocation of breeding resources is crucial for the efficiency of breeding programmes. The objectives were to (i) compare selection gain ΔGk for finite and infinite sample sizes, (ii) compare ΔGk and the probability of identifying superior hybrids (Pk), and (iii) determine the optimum allocation of the number of hybrids and test locations in hybrid maize breeding using doubled haploids. Infinite compared to finite sample sizes led to almost identical optimum allocation of test resources, but to an inflation of ΔGk. This inflation decreased as the budget and the number of finally selected hybrids increased. A reasonable Pk was reached for hybrids belonging to the q = 1% best of the population. The optimum allocations for Pk(q) and ΔGkwere similar, indicating that Pk(q) is promising for optimizing breeding programmes.

A Control Allocation Method to Reduce Roll-Yaw coupling on Tailless Aircraft

2021 ◽  
Author(s):  
Thomas R. Shearwood ◽  
Mostafa R. Nabawy ◽  
William Crowther ◽  
Clyde Warsop
Keyword(s):  
Control Allocation ◽  
Allocation Method ◽  
Tailless Aircraft

Research on Regulation Compensation Allocation Method of Virtual Power Plant

2019 ◽  
Author(s):  
Jianlin Yang ◽  
Jingbang Li ◽  
Mingxing Guo ◽  
Yichao Huang ◽  
Aili Pang ◽  
...  
Keyword(s):  
Power Plant ◽  
Virtual Power ◽  
Virtual Power Plant ◽  
Allocation Method

scholarly journals Optimality Properties of the Disc Modulo Allocation Method for k-ary Multiple Key Hashing Files

1992 ◽  
Vol 35 (4) ◽  
pp. 390-394 ◽  
Author(s):  
C. C. Chang ◽  
H. F. Lin ◽  
C. Y. Chen
Keyword(s):  
Allocation Method ◽  
Optimality Properties

M2FBalancer: A mist-assisted fog computing-based load balancing strategy for smart cities

2021 ◽  
pp. 1-15
Author(s):  
Subhranshu Sekhar Tripathy ◽  
Diptendu Sinha Roy ◽  
Rabindra K. Barik
Keyword(s):  
Load Balancing ◽  
Resource Utilization ◽  
Smart City ◽  
Load Transfer ◽  
Smart Cities ◽  
Fog Computing ◽  
Round Robin ◽  
Healthcare Management ◽  
Optimization Strategy ◽  
Allocation Method

Nowadays, cities are intended to change to a smart city. According to recent studies, the use of data from contributors and physical objects in many cities play a key element in the transformation towards a smart city. The ‘smart city’ standard is characterized by omnipresent computing resources for the observing and critical control of such city’s framework, healthcare management, environment, transportation, and utilities. Mist computing is considered a computing prototype that performs IoT applications at the edge of the network. To maintain the Quality of Service (QoS), it is impressive to employ context-aware computing as well as fog computing simultaneously. In this article, the author implements an optimization strategy applying a dynamic resource allocation method based upon genetic algorithm and reinforcement learning in combination with a load balancing procedure. The proposed model comprises four layers i.e. IoT layer, Mist layer, Fog layer, and Cloud layer. Authors have proposed a load balancing technique called M2F balancer which regulates the traffic in the network incessantly, accumulates the information about each server load, transfer the incoming query, and disseminate them among accessible servers equally using dynamic resources allocation method. To validate the efficacy of the proposed algorithm makespan, resource utilization, and the degree of imbalance (DOI) are considered as the scheduling parameter. The proposed method is being compared with the Least count, Round Robin, and Weighted Round Robin. In the end, the results demonstrate that the solutions enhance QoS in the mist assisted cloud environment concerning maximization resource utilization and minimizing the makespan. Therefore, M2FBalancer is an effective method to utilize the resources efficiently by ensuring uninterrupted service. Consequently, it improves performance even at peak times.

Sign in / Sign up

Export Citation Format

Share Document