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并行处理器调度:采用基于感知推理的多目标语言优化求解方法(CS AI)

在工业4.0时代,几乎所有的工业和制造企业都把重心放在了将人的因素最小化,自动化过程最大化智商。这些企业包含许多处理系统,这些处理系统可以使用最少数量的人并行地执行许多任务,这种任务的并行执行是根据调度策略完成的。然而,将人的因素最小化到某一点之后是很困难的。事实上,一组专家的专业知识和经验,成为设计一个富有成效的调度策略的重中之重。调度策略的目标是实现生产时间、成本等目标的最优值。在实际情况中,在任何并行处理场景中通常都有多个目标。此外,专家们通常会用语言术语或词汇提供他们对各种调度标准(与调度策略相关)的意见。使用模糊集(FSs)对语义进行最佳建模。因此,所有这些因素都促使我们将并行处理场景建模为一个多目标语言优化问题(MOLOP),并使用基于感知推理(PR)的新方法来解决它。我们还比较了基于PR的求解方法与基于二元组的求解方法的结果。基于PR的解决方案方法提供了三个主要的优点,即生成独特的推荐,这里的语言推荐匹配一个对应单词,而且单词模型位于单词之前。基于二元组的解决方案方法不能提供所有这些优点。因此,我们认为我们的工作是新颖的,将为未来的研究提供方向。

原文题目:Parallel processor scheduling: formulation as multi-objective linguistic optimization and solution using Perceptual Reasoning based methodology

原文:In the era of Industry 4.0, the focus is on the minimization of human element and maximizing the automation in almost all the industrial and manufacturing establishments. These establishments contain numerous processing systems, which can execute a number of tasks, in parallel with minimum number of human beings. This parallel execution of tasks is done in accordance to a scheduling policy. However, the minimization of human element beyond a certain point is difficult. In fact, the expertise and experience of a group of humans, called the experts, becomes imminent to design a fruitful scheduling policy. The aim of the scheduling policy is to achieve the optimal value of an objective, like production time, cost, etc. In real-life situations, there are more often than not, multiple objectives in any parallel processing scenario. Furthermore, the experts generally provide their opinions, about various scheduling criteria (pertaining to the scheduling policies) in linguistic terms or words. Word semantics are best modeled using fuzzy sets (FSs). Thus, all these factors have motivated us to model the parallel processing scenario as a multi-objective linguistic optimization problem (MOLOP) and use the novel perceptual reasoning (PR) based methodology for solving it. We have also compared the results of the PR based solution methodology with those obtained from the 2-tuple based solution methodology. PR based solution methodology offers three main advantages viz., it generates unique recommendations, here the linguistic recommendations match a codebook word, and also the word model comes before the word. 2-tuple based solution methodology fails to give all these advantages. Thus, we feel that our work is novel and will provide directions for the future research.

原文作者:Prashant K Gupta , Pranab K. Muhuri

原文地址: https://arxiv.org/abs/2004.14955

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