Abstract In cloud computing, task scheduling and resource allocation are the two core issues of IaaS layer. Efficient algorithm can improve matching efficiency between tasks resources. this paper, an enhanced heterogeneous earliest finish time based on rule (EHEFT-R) is proposed to optimize execution efficiency, quality service (QoS) energy consumption. EHEFT-R, ordering rules priority constrai...

The project scheduling and resource allocation problems have been studied using different optimization methods. The resource leveling problem was proposed to reduce the resource fluctuation and was always studied independently resource constraint problem. For example, resource-constrained project scheduling problem (RCPSP) was proposed to optimize scheduling under resource constraints. This res...

During project execution, especially in a multi-project environment unforeseen events arise that disrupt project plans resulting in deviations of project plans and budgets due to missed due dates and deadlines, resource idleness, higher work-in-process inventory and increased system nervousness. Therefore, project schedules should also include solution robustness to cope with the uncertainties ...

Most existing methods for scheduling projects are based on centralized or hierarchical decision making using global models. So far, these methods have not produced the desired results, especially in highly dynamic multi-projects environments. In this study, we investigate a new method based on multi-agent systems and a combinatorial auction mechanism to allocate resources for the projects tasks...

We have developed an architecture called MUSE (MultiUser Scheduling Environment) to enable the integration of multiobjective evolutionary algorithms with existing domain planning and scheduling tools. Our approach is intended to make it possible to re-use existing software, while obtaining the advantages of multi-objective optimization algorithms. This approach enables multiple participants to ...