Multi-objective Optimization for Cloud Task Scheduling Based on the ANP Model

2017 ◽  
Vol 26 (5) ◽  
pp. 889-898 ◽  
Author(s):  
Kunlun Li ◽  
Jun Wang
Keyword(s):  
Task Scheduling ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals Improved genetic algorithm for 3D printing multi-objective optimization task scheduling

2021 ◽  
Vol 336 ◽  
pp. 02020
Author(s):  
Haobin Zhao ◽  
Hongbin Yu
Keyword(s):  
3D Printing ◽  
Task Scheduling ◽  
Related Problem ◽  
Problem Definition ◽  
Scheduling Problem ◽  
Multi Objective Optimization ◽  
Improved Genetic Algorithm ◽  
Problem Model ◽  
Multi Objective ◽  
Optimization Task

For the 3D printing multi-objective optimization task scheduling problem, the problem model is established from the three aspects of related problem definition, constraint conditions, and objective function, which lays the foundation for subsequent research and solution.

scholarly journals Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing

Algorithms ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 77
Author(s):  
Afra A. Alabbadi ◽  
Maysoon F. Abulkhair
Keyword(s):  
Task Scheduling ◽  
Fitness Function ◽  
Optimal Solution ◽  
Multiple Objectives ◽  
Multi Objective Optimization ◽  
Scheduling Optimization ◽  
Spatial Crowdsourcing ◽  
Travel Costs ◽  
Multi Objective ◽  
Ranking Strategy

Recently, with the development of mobile devices and the crowdsourcing platform, spatial crowdsourcing (SC) has become more widespread. In SC, workers need to physically travel to complete spatial–temporal tasks during a certain period of time. The main problem in SC platforms is scheduling a set of proper workers to achieve a set of spatial tasks based on different objectives. In actuality, real-world applications of SC need to optimize multiple objectives together, and these objectives may sometimes conflict with one another. Furthermore, there is a lack of research dealing with the multi-objective optimization (MOO) problem within an SC environment. Thus, in this work we focused on task scheduling based on multi-objective optimization (TS-MOO) in SC, which is based on maximizing the number of completed tasks, minimizing the total travel costs, and ensuring the balance of the workload between workers. To solve the previous problem, we developed a new method, i.e., the multi-objective task scheduling optimization (MOTSO) model that consists of two algorithms, namely, the multi-objective particle swarm optimization (MOPSO) algorithm with our fitness function Alabbadi, et al. and the ranking strategy algorithm based on the task entropy concept and task execution duration. The main purpose of our ranking strategy is to improve and enhance the performance of our MOPSO. The primary goal of the proposed MOTSO model is to find an optimal solution based on the multiple objectives that conflict with one another. We conducted our experiment with both synthetic and real datasets; the experimental results and statistical analysis showed that our proposed model is effective in terms of maximizing the number of completed tasks, minimizing the total travel costs, and balancing the workload between workers.

Sign in / Sign up

Export Citation Format

Share Document