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Most existing studies on spatial crowdsourcing mainly focus on the problems of task assignment [6, 7, 8, 9, 10, 11, 12], which are to assign tasks to suitable workers, and assume that tasks are all simple and trivial.
The experimental results indicate that the adaptive scheduling algorithm significantly improves the balance of task assignment.
Further, we introduce two variants of task assignment mechanisms; without and with incentives.
In any given company, hyperspecialization might reshape the organization in many ways, from the macro to the micro level of task assignment.
The concept of task assignment for process synthesis at the preliminary stage of process design has been extensively applied to generate more complete process flow diagrams.
A parametric simulator has been used to calculate the objective function of each individual, evaluating at the same time the effect of task assignment and buffer allocation decisions on the line throughput.
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However, we do not need such a complex system for the MapReduce operations because the main instruction of task assignments in our system is to specify operations and data chunks for the master and the slaves.
The main difference between task allocation and distributed task scheduling is that task allocation deals with the problem of finding a set of task assignments on a sensor network that minimizes an objective function such as total execution time [12, 13].
A wide range of task assignments were found from the survey and site visits (Table 1, Table 2) and in reviews of existing and proposed models.
Second, with the optimization of tasks assignment algorithm, we can maximize the performance of heterogeneous clusters.
This paper presents the results of a field experiment designed to study the effects of such team incentives on task assignment and performance.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com