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Mission planning plays an important role in satellite control systems, especially with increase of number of satellites and more complex missions to be planned. In a general setting, the satellite mission scheduling consists in allocating tasks such as observation, communication, etc. to resources (spacecrafts (SCs), satellites, ground stations). For instance, in ground station scheduling the aim...
Computing intensive applications are an important family of applications in distributed computing domain. They have been object of study using different distributed computing paradigms and infrastructures. Such applications distinguish for their demanding needs for CPU computing, independently of the amount of data associated with the problem instance. Among computing intensive applications, there...
Ground station scheduling problem arises in spacecraft operations and aims to allocate ground stations to spacecraft to make possible the communication between operations teams and spacecraft systems. This problem consists in computing an optimal planning of communications between satellites or spacecraft (SC) and operations teams of Ground Station (GS). The information transmitted in these communications...
The Ground Station Scheduling is one of the most important problems in the field of Satellite-Scheduling. This problem consists in planning feasible planning of communications between satellites or spacecraft (SC) and operations teams of Ground Station (GS). The information received in these communications is usually basic information such as telemetry, tracking information or tasks to perform, so...
Ground station scheduling problem arises in spacecraft operations and aims to allocate ground stations to spacecraft to make possible the communication between operations teams and spacecraft systems. The problem belongs to the family of satellite scheduling for the specific case of mapping communications to ground stations. Ground stations are terrestrial terminals designed for extra-planetary communications...
Computational grids have become attractive and promising platforms for solving large-scale high-performance applications of multi-institutional interest. However, the management of resources and computational tasks is a critical and complex undertaking as these resources and tasks are geographically distributed and a heterogeneous in nature. This paper proposes a novel Rank Based Genetic Scheduler...
Aiming for the actual production of carbon enterprises, the optimization problem of master production scheduling in carbon enterprises was studied. With the purpose of minimizing production and inventory costs in planning horizon, the paper established an optimization model of master production scheduling by using the unit time earliness and tardiness penalty of products. The paper proposed the use...
In most cases, the number of resources and tasks in grid computing environment is large. Accordingly, the complexity of task scheduling is significantly increased. This results very complex optimization problem. This paper proposes an improved rank-based roulette wheel selection genetic algorithm (IRRWSGA) for scheduling independent tasks in the grid environment. The modified algorithm speeds up convergence...
Complex applications are describing using work-flows. Execution of these workflows in Grid environments require optimized assignment of tasks on available resources according with different constrains. This paper presents a decentralized scheduling algorithm based on genetic algorithms for the problem of DAG scheduling. The genetic algorithm presents a powerful method for optimization and could consider...
In grid computing the number of resources and tasks is usually very large, which makes the scheduling task very complex optimization problem. Genetic algorithms (GAs) have been broadly used to solve these NP-complete problems efficiently. On the other hand, the standard genetic algorithm (SGA) is too slow when used in a realistic scheduling due to its time consuming iteration. This paper proposes...
Grid workflow applications are emerging as a more and more important way to manage and process large data sets, and execute scientific experiments on heterogeneous and distributed grid environment. Therefore, many efforts have been made towards the development of grid workflow management engine for grid computing. This paper proposes and simulates a new workflow process engine with hierarchical architecture...
Scheduling of semiconductor wafer fabrication system is identified as a complex problem, involving multiple and conflicting objectives (makespan and minimizing waiting time for instance) to satisfy. In this study, we propose an effective approach based an artificial neural network technique embedded in a multi-objective optimization loop for multi-decision scheduling problems in a semiconductor wafer...
In this paper we introduce the completed unit algorithm (CU-AL), a probabilistic scheduling methodology for repetitive projects. The algorithm has two main advantages, simplicity and short computational time, that facilitate and expedite its use in simulation modeling and optimization. An integration between CU-AL and genetic algorithm (GA) is established to optimize the problem of maximizing profit...
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