The multi-objective directed acyclic graph schedulingproblem (MDAGSP) is prevalent in cloud scheduling systems, involving the selection, assignment, and execution of multiple tasks/jobs with complex coupling interdep...
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The multi-objective directed acyclic graph schedulingproblem (MDAGSP) is prevalent in cloud scheduling systems, involving the selection, assignment, and execution of multiple tasks/jobs with complex coupling interdependencies. High-quality solutions can yield substantial economic benefits. However, prevailing methods face challenges in obtaining a set of superior solutions for MDAGSP, due to the multifaceted nature of its variables, objectives, constraints, and heterogeneity. Firstly, this paper formulates a three-objective MDAGSP that includes makespan, energy costs and revenue, to model cloud scheduling systems. Subsequently, we propose a composite algorithm consisting of a selection phase and an assignment phase to automatically generate an efficient scheduling policy for this model. During the selection phase, a graph convolutional neural network learns high-level features to extract complex dependencies between tasks. During the assignment phase, an adaptive evolutionary algorithm assigns tasks to the appropriate executors. Finally, a series of experiments are conducted to validate the model's accuracy and assess the algorithm's efficiency. Compared to heuristic approaches, the algorithm achieves at least a 20.1% makespan reduction and 3.17% revenue increase. Remarkably, the algorithm obtains at least an 8.86% reduction in energy costs over the eleven baselines. In conclusion, the proposed algorithm provides decision-makers with a global view of scheduling plan.
The CNC machining schedulingproblem is a classic flexible job shop schedulingproblem (FJSSP) that has been proven to be NP-hard. Although there has been extensive research on FJSSP, its applications in real-world CN...
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The CNC machining schedulingproblem is a classic flexible job shop schedulingproblem (FJSSP) that has been proven to be NP-hard. Although there has been extensive research on FJSSP, its applications in real-world CNC machining schedulingproblems have received limited attention, with few related papers. In reality, several critical characteristics within the CNC machining industry can significantly impact the schedulingproblem, such as process plan flexibility, multiple resource constraints, and lot streaming. This study aims to establish a scheduling system for the CNC machining industry that integrates order information, work in process information, and the aforementioned characteristics, using a hybrid multi-objective genetic algorithm (HMOGA) combined with local search. The optimal parameter combination for HMOGA is determined through a design of experiments, while the effectiveness of local search is evaluated using computational analysis. Finally, real data from a company is used to validate the proposed method. The results demonstrate that the scheduling system developed in this study effectively addresses real-world CNC machining schedulingproblems.
Energy neutrality of Internet of Things devices powered with energy harvesting is a concept introduced to let these devices operate uninterruptedly. A method to achieve it is by letting the device scheduling different...
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ISBN:
(纸本)9781665497923
Energy neutrality of Internet of Things devices powered with energy harvesting is a concept introduced to let these devices operate uninterruptedly. A method to achieve it is by letting the device scheduling different tasks characterized by different energy costs (and quality), depending on the current energy production of the energy harvesting subsystem and on the residual battery charge. In this context, we propose a novel schedulingproblem that aims at keeping the energy neutrality of the scheduling while maximizing the overall quality of the executed tasks and minimizing the leaps of quality among consecutive tasks, so to improve the stability of the output of the device itself. We propose for this problem an algorithm based on a dynamic programming approach that can be executed even on low-power devices. By simulation we show that, with respect to the state of the art, the scheduling by our algorithm greatly improve the stability of the device with a minor penalty in terms of overall quality.
In this paper, we examine the characteristic features of multi-objective scheduling problems formulated with the concept of fuzzy due-date. By computer simulations, we show that various scheduling criteria can be expr...
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In this paper, we examine the characteristic features of multi-objective scheduling problems formulated with the concept of fuzzy due-date. By computer simulations, we show that various scheduling criteria can be expressed by modifying the shape of membership functions of fuzzy due-dates. We also show the difficulty in handling the minimum satisfaction grade as a scheduling criterion. This difficulty is caused by the fact that the minimum satisfaction grade is zero for almost all schedules. This makes many search algorithms inefficient. We suggest an idea to cope with this difficulty. (C) 1998 Elsevier Science Ltd. All rights reserved.
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