|
Document Type |
: |
Thesis |
|
Document Title |
: |
Multi-Objective Tasks Scheduling Optimization in Spatial Crowdsourcing Platforms الجدولة الأمثل للمهام متعددة الأهداف في منصة التعهيد الجماعي المكاني |
|
Subject |
: |
Faculty of Computing and Information Technology |
|
Document Language |
: |
Arabic |
|
Abstract |
: |
Recently, with the development of mobile devices and crowdsourcing platform, the spatial crowdsourcing (SC) becomes more widespread. In SC, the workers need to physically travel to complete Spatial-temporal tasks during a period of time.
The main problem in SC platforms is the assignment and scheduling of a set of spatial tasks to a set of proper workers based on different factors like the task’s location, the distance between the task and the hired worker, temporal condition and an incentive reward. On another hand, the SC applications in the real-world need to optimize multi-objective together to exploit the utility of SC; these objectives may sometimes be conflicting with each other. However, there is a lack of studies, that address the multi-objective optimization problem within the SC environment. Thus, we focus on a task scheduling based on multi objective optimization (TS-MOO) problem in SC that aims to maximize the number of completed tasks, minimize the total travel cost and ensure the workload balancing by minimizing the standard deviation of Workload Balancing between workers. To solve the previous problem, we developed a new method that is Multi-Objective Task Scheduling Optimization model by adapting the Multi-Objective Particle Swarm Optimization (MOPSO) algorithm based on a novel fitness function. To enhance the performance of our MOPSO, we introduced the Ranking Strategy algorithm based on Task entropy concept and Task Execution Duration. The primary purpose of the proposed Multi-Objective Task Scheduling Optimization (MOTSO) model is to find an optimal solution based on the multi-objectives, that conflicting together.
We conducted our experiment with both synthetic and real dataset; the experimental results and statistical analysis shows that our proposed model (MOTSO) proved its effectiveness in terms of maximizing the number of completed tasks, minimizing the total travel cost and the workload balancing between workers. |
|
Supervisor |
: |
Dr. Maysoon Abulkhair |
|
Thesis Type |
: |
Master Thesis |
|
Publishing Year |
: |
1441 AH
2020 AD |
|
Added Date |
: |
Saturday, June 13, 2020 |
|
Researchers
| عفراء عبد الله العبادي | Alabbadi, Afra Abdullah | Researcher | Master | |
|