Metaheuristic-Based Scheme for Spectrum Resource Schedule Over 5G IoT Network

2018 ◽  
pp. 117-125
Author(s):  
Yao-Chung Chang ◽  
Shih-Yun Huang ◽  
Han-Chieh Chao
Keyword(s):  
Resource Schedule

Control and the Health Effects of Work–Family Conflict: A Longitudinal Test of Generalized Versus Specific Stress Buffering

2020 ◽  
Vol 61 (3) ◽  
pp. 324-341
Author(s):  
Philip Badawy ◽  
Scott Schieman
Keyword(s):  
Health Effects ◽  
Family Conflict ◽  
Process Model ◽  
Physical Symptoms ◽  
Health Study ◽  
Work Family Conflict ◽  
Schedule Control ◽  
Stress Buffering ◽  
Resource Schedule ◽  
Work Family

The stress associated with work-to-family conflict (WFC) and family-to-work conflict (FWC) is well documented. However, surprisingly little is known about the resources that moderate the effects of work–family conflict on health over time. Using four waves of panel data from the Canadian Work, Stress, and Health Study (2011–2017; n = 11,349 person-wave observations), we compare how a core psychosocial resource (personal mastery) and a salient organizationally based resource (schedule control) moderate the health effects of WFC and FWC. After establishing these health effects related to distress and physical symptoms, we discover that mastery has generalized stress-buffering functions whereby it alleviates the health effects of both WFC and FWC. In contrast, schedule control has asymmetrical moderating functions: It attenuates the health effects of WFC only. These findings elaborate and sharpen the scope of resources as moderators in the stress process model—and we integrate these ideas with other conceptual models like the job demands-resources model.

Type-2 fuzzy logic based energy-efficient transceiver resource schedule in multi-radio WSNs

2019 ◽  
Vol 32 (3) ◽  
pp. 197
Author(s):  
Wei Peng ◽  
Dongyan Chen ◽  
Wenhui Sun ◽  
Chengdong Li ◽  
Guiqing Zhang
Keyword(s):  
Fuzzy Logic ◽  
Energy Efficient ◽  
Resource Schedule

scholarly journals Disruption Management of Resource Schedule in Transportation Sector: Understanding the Concept and Strategy

2016 ◽  
Vol 161 ◽  
pp. 1295-1299 ◽  
Author(s):  
Mohd Haniff Bin Osman ◽  
Sakdirat Kaewunruen ◽  
Min Ann ◽  
Serdar Dindar
Keyword(s):  
Disruption Management ◽  
Transportation Sector ◽  
Resource Schedule

scholarly journals An Iterated Local Search Algorithm for a Place Scheduling Problem

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Shicheng Hu ◽  
Zhaoze Zhang ◽  
Qingsong He ◽  
Xuedong Sun
Keyword(s):  
Local Search ◽  
Schedule Problem ◽  
Search Algorithm ◽  
Heuristic Method ◽  
Three Dimensional ◽  
Iterated Local Search ◽  
Scheduling Problem ◽  
Priority Rules ◽  
Resource Schedule ◽  
Best Fit

We study the place scheduling problem which has many application backgrounds in realities. For the block manufacturing project with special manufacturing platform requirements, we propose a place resource schedule problem. First, the mathematical model for the place resource schedule problem is given. On the basis of resource-constrained project scheduling problem and packing problem, we develop a hybrid heuristic method which combines priority rules and three-dimensional best fit algorithm, in which the priority rules determine the scheduling order and the three-dimensional best fit algorithm solves the placement. After this method is used to get an initial solution, the iterated local search is employed to get an improvement. Finally, we use a set of simulation data to demonstrate the steps of the proposed method and verify its feasibility.

Resource Schedule Algorithm Based on Artificial Fish Swarm in Cloud Computing Environment

2014 ◽  
Vol 635-637 ◽  
pp. 1614-1617 ◽  
Author(s):  
Hong Wei Zhao ◽  
Li Wei Tian
Keyword(s):  
Cloud Computing ◽  
Main Idea ◽  
Schedule Algorithm ◽  
Computing System ◽  
Computing Environment ◽  
Swarm Optimization ◽  
Cloud Computing Environment ◽  
Resource Schedule ◽  
Cloud Computing System ◽  
Artificial Fish Swarm

Cloud computing needs to manage a large number of computing resources, while resources scheduling strategy plays a key role in determining the efficiency of cloud computing. evolutionary algorithms (EA) as appropriate tools to optimize multi-objective problems have been applied to optimize Resources Scheduling of cloud computing ,However, studies on improving the convergence ratio and processing time in the most applied algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) in Resources Scheduling domains remain poorly understood. the resource schedule algorithmbased on Artificial Fish Swarm Optimization(AFSA) for Cloud Computing Environment has been designed and implemented after the study on the resource schedule of Cloud Computing. The main idea of improved AFSA is to extend Fish Swarm Optimization to the interacting swarms model by cooperative Models . The improved AFSA probability analysis indicates that searching solution is much more efficient and speeds up the multi-swarm to converge to the global optimum.Finally, the result of the experiment indicates that the scheduling system can improve the efficiency of dispatching resource and the utilization ratio in the Cloud Computing system.

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