Route Planning Integrated Multi Objective Task Allocation for Reconfigurable Robot Teams Using Genetic Algorithm

2018 ◽  
Vol 15 (2) ◽  
pp. 627-636 ◽  
Author(s):  
K. Padmanabhan Panchu ◽  
M. Rajmohan ◽  
M. R. Sumalatha ◽  
R. Baskaran
Keyword(s):  
Genetic Algorithm ◽  
Task Allocation ◽  
Completion Time ◽  
Route Planning ◽  
Task Completion ◽  
Task Completion Time ◽  
Robot Teams ◽  
Reconfigurable Robot ◽  
Robot Tasks ◽  
Multi Robot

This research work aims at multi objective optimization of integrated route planning and multi-robot task allocation for reconfigurable robot teams. Genetic Algorithm based methodology is used to minimize the overall task completion time for all the multi-robot tasks and to minimize the cumulative running time of all the robots. A modified matrix based chromosome is used to accommodate the robot information and task information for route planning integrated task allocation. The experimental validation is done with 3 robots and 4 tasks. For larger number of robots and tasks were simulated to perform route planning for maximum of 20 robots that would attend the maximum of 40 different multi-robot tasks. The results shows that the average task completion time per robot and average travel time per robot, decreases exponentially with increase in number of robots for fixed number of tasks. This method finds its application in allocating a robot teams to tasks and finding the best sequence for robots that work in coordination for material handling in hospital management, warehouse operations, military operations, cleaning tasks etc.

scholarly journals Task Scheduling Allocation based on Task Completion Time in Cloud Environment

2019 ◽  
Vol 8 (4) ◽  
pp. 5207-5213
Keyword(s):  
Resource Allocation ◽  
Cloud Computing ◽  
Load Balancing ◽  
Task Scheduling ◽  
Task Allocation ◽  
Completion Time ◽  
Resource Provisioning ◽  
Task Completion ◽  
Task Completion Time ◽  
Survey Paper

Cloud computing is a prominent computing model wherein shared resources can be given as per the customer request at a time. The available resources in the cloud are gathered to execute several tasks that are submitted by the customer. While implementing the tasks, there is a need to optimize performance in terms of execution time, response time and resource utilization of the cloud. The optimization of the mentioned factors in the Cloud Computing can be achieved by one of the major areas known as Load balancing which refers to dealing with client requests from diverse application servers that are functioning in the cloud. An efficient Load Balancing algorithm enables the cloud to be more proficient and enhances customer contentment. So, this survey paper highlights the latest studies regarding the application of Load Balancing techniques for task allocation such as resource allocation (RA) strategies, cloud task scheduling centered on Load Balancing, dynamic Resource Allocation schemes, and cloud resource provisioning scheduling heuristics. Finally, Load Balancing performance for task allocation methods is compared based on task completion time.

scholarly journals Genetic Algorithm-Based Optimization of Offloading and Resource Allocation in Mobile-Edge Computing

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 83 ◽  
Author(s):  
Zhi Li ◽  
Qi Zhu
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Completion Time ◽  
Access Network ◽  
Optimization Method ◽  
Edge Computing ◽  
Smart Devices ◽  
Task Completion ◽  
Mobile Edge Computing ◽  
Task Completion Time

Mobile edge computing (MEC) can use a wireless access network to serve smart devices nearby so as to improve the service experience of users. In this paper, a joint optimization method based on the Genetic Algorithm (GA) for task offloading proportion, channel bandwidth, and mobile edge servers’ (MES) computing resources is proposed in the scenario where some computing tasks can be partly offloaded to the MES. Under the limitation of wireless transmission resources and MESs’ processing resources, GA was used to solve the optimization problem of minimizing user task completion time, and the optimal offloading task strategy and resource allocation scheme were obtained. The simulation results show that the proposed algorithm can effectively reduce the task completion time and ensure the fairness of users’ completion times.

Research of Cloud Computing Task Scheduling Algorithm Based on Improved Genetic Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 2426-2429 ◽  
Author(s):  
Jun Wei Ge ◽  
Yong Sheng Yuan
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Task Scheduling ◽  
Completion Time ◽  
Scheduling Algorithm ◽  
Task Completion ◽  
Improved Genetic Algorithm ◽  
Task Completion Time ◽  
Task Scheduling Algorithm ◽  
Reasonable Use

Use genetic algorithm for task allocation and scheduling has get more and more scholars' attention. How to reasonable use of computing resources make the total and average time of complete the task shorter and cost smaller is an important issue. The paper presents a genetic algorithm consider total task completion time, average task completion time and cost constraint. Compared with algorithm that only consider cost constraint (CGA) and adaptive algorithm that only consider total task completion time by the simulation experiment. Experimental results show that this algorithm is a more effective task scheduling algorithm in the cloud computing environment.

The Effects of Language Barriers and Time Constraints on Online Learning Performance: An Eye-Tracking Study

2021 ◽  
pp. 001872082110109
Author(s):  
Auður Anna Jónsdóttir ◽  
Ziho Kang ◽  
Tianchen Sun ◽  
Saptarshi Mandal ◽  
Ji-Eun Kim
Keyword(s):  
Online Learning ◽  
Eye Movement ◽  
Completion Time ◽  
Language Use ◽  
Time Pressure ◽  
Time Constraints ◽  
Task Completion ◽  
Esl Students ◽  
Task Completion Time ◽  
Eye Fixation

Objective The goal of this study is to model the effect of language use and time pressure on English as a first language (EFL) and English as a second language (ESL) students by measuring their eye movements in an on-screen, self-directed learning environment. Background Online learning is becoming integrated into learners’ daily lives due to the flexibility in scheduling and location that it offers. However, in many cases, the online learners often have no interaction with one another or their instructors, making it difficult to determine how the learners are reading the materials and whether they are learning effectively. Furthermore, online learning may pose challenges to those who face language barriers or are under time pressure. Method The effects of two factors, language use (EFL vs. ESL) and time constraints (high vs. low time pressure), were investigated during the presentation of online materials. The effects were analyzed based on eye movement measures (eye fixation rate—the total number of eye fixations divided by the task duration and gaze entropy) and behavioral measures (correct rate and task completion time). Results The results show that the ESL students had higher eye fixation rates and longer task completion times than the EFL students. Moreover, high time pressure resulted in high fixation rates, short task completion time, low correct rates, and high gaze entropy. Conclusion and Application The results suggest the possibility of using unobtrusive eye movement measures to develop ways to better assist those who struggle with learning in the online environment.

scholarly journals Bandwidth-Aware Rescheduling Mechanism in SDN-Based Data Center Networks

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1774
Author(s):  
Ming-Chin Chuang ◽  
Chia-Cheng Yen ◽  
Chia-Jui Hung
Keyword(s):  
Data Center ◽  
Completion Time ◽  
Network Performance ◽  
Data Locality ◽  
Task Completion ◽  
Data Center Networks ◽  
Task Completion Time ◽  
Data Packets ◽  
Network Bandwidth ◽  
Data Process

Recently, with the increase in network bandwidth, various cloud computing applications have become popular. A large number of network data packets will be generated in such a network. However, most existing network architectures cannot effectively handle big data, thereby necessitating an efficient mechanism to reduce task completion time when large amounts of data are processed in data center networks. Unfortunately, achieving the minimum task completion time in the Hadoop system is an NP-complete problem. Although many studies have proposed schemes for improving network performance, they have shortcomings that degrade their performance. For this reason, in this study, we propose a centralized solution, called the bandwidth-aware rescheduling (BARE) mechanism for software-defined network (SDN)-based data center networks. BARE improves network performance by employing a prefetching mechanism and a centralized network monitor to collect global information, sorting out the locality data process, splitting tasks, and executing a rescheduling mechanism with a scheduler to reduce task completion time. Finally, we used simulations to demonstrate our scheme’s effectiveness. Simulation results show that our scheme outperforms other existing schemes in terms of task completion time and the ratio of data locality.

scholarly journals The Impact of Notification Modality and Ambient Sound on Users’ Mobile Interaction

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 395
Author(s):  
Chien-Hsiung Chen ◽  
Miao Huang
Keyword(s):  
Completion Time ◽  
Sampling Method ◽  
Sound Level ◽  
Task Completion ◽  
Maze Task ◽  
Task Completion Time ◽  
Mobile Interaction ◽  
Ambient Sound ◽  
Convenience Sampling ◽  
The Impact

This study investigated the impacts of different notification modalities used in low and high ambient sound environments for mobile phone interaction. Three different notification modalities—Shaking Visual, Shaking Visual + Vibration, and Vibration—were designed and experimentally tested by asking users to conduct a maze task. A total of 72 participants were invited to take part in the experiment through the convenience sampling method. The generated results indicated that (1) the notification modality affects participants’ task completion time, (2) the error rate pertinent to the number of notifications is positively related to the participants’ task completion time, and (3) the ambient sound level and notification modalities impact the overall experience of the participants. The main contributions of this study are twofold. First, it verifies that the multi-dimensional feature of a Shaking Visual + Vibration synesthesia notification design is implementable. Second, this study demonstrated that the synesthesia notification could be feasible for mobile notification, and it was more perceptible by the users.

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