Font Size: a A A

Research On The Remotely Sensed Data Processing Task Comprehensive Scheduling Problem

Posted on:2014-03-20Degree:DoctorType:Dissertation
Country:ChinaCandidate:W LiFull Text:PDF
GTID:1108330479479637Subject:Management Science and Engineering
Abstract/Summary:PDF Full Text Request
The mission of the remotely sensed data processing task comprehensive scheduling(RSDPTCS) is to allocate remotely sensed data processing resources optimally, make the most of the limited resources in order to satisfy multifarious remotely sensed data processing requests from customers. RSDPTCS problem is the problem, which rationally distribute the remotely sensed data tasks from the stations to the processing centres and rationally distribute the remotely sensed data tasks to processing resources under the constrains in each processing centres, as far as possible on time and maximize complete remotely sensed data processing tasks in order to and enhance the processing abilities of processing systems and scheduling efficiencies. The research of this problem has important theoretical and application value.Based on the analysis of the remotely sensed data processing task comprehensive scheduling problem, this paper presents a new scheduling model to solve the scheduling problem, named the limited concentration model. This paper builds the mathematical models for task distribution and task scheduling problem of RSDPTCS, and presents the corresponding algorithms to solve these problems. At last, a software system is also presented in the paper. The main work and contributions are outlined as follows:(1) This paper presents the framework and model of RSDPTCSThe scheduling framework and model is the base of building mathematical models and solving the problem. On the basis of analysis of the organization process of the remotely sensed data processing, the standard processing products and the requests of customers, this paper presents the limited concentration model and the framework of RSDPTCS based grid environment. This model is divided RSDPTCS into two main parts: task distribution and task scheduling.(2) This paper presents the remotely sensed task allocation framework and related allocation planConsidering on the allocation problem of the remotely sensed data processing task, this paper establishes a framework for problem solving task allocation. This paper adopts Bayes belief model to individual distribute the remotely sensed tasks in order to reduce the complex and improve the speed of solving the problem. The framework fixs the penalties exceed the deadline with the forecasts of accomplished time of tasks and transmission time of remotely sensed data. An effective allocation strategy is presented to generate the feasible allocation plan.(3) This paper presents the remotely sensed data processing task scheduling modelThe remotely sensed data processing task scheduling problem is a complex problem, it not only relates to a great quantity of resources and operations, but also the relation between the resources and operations is very complex. Based on the analysis of the characters of the remotely sensed data processing task scheduling problem, a model of the remotely sensed data processing scheduling problem is presented.(4) This paper presents intelligent optimization algorithms to solve the model of the remotely sensed data processing scheduling problemThe study of the remotely sensed data processing scheduling algorithms is another key point of this paper. The remotely sensed data processing scheduling problem is a classical multi-objective decision problem. This paper adopts the TOPSIS method to deal with the multi-objective decision problem and presents dynamic heuristic algorithm and learnable ant colony optimization algorithm to solve the model of the remotely sensed data processing scheduling problem.
Keywords/Search Tags:The Remotely Sensed Data Processing, Task Comprehensive Scheduling, Limited Concentration Model, Bayes Belief Model, Dynamic Heuristic Algorithm, Learnable Ant Colony Optimization
PDF Full Text Request
Related items