A Study of Distributed Scheduling Problem with Machine Maintenance

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI:10.1109/ICCIS.2006.252261

F. Chan, S. Chung, L. Chan

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引用次数: 7

Abstract

In this paper, we study the influence of machine maintenance to distributed scheduling problems. Distributed scheduling is aiming to maximize the system efficiency by simultaneously solving two problems: (i) allocation of jobs to suitable factories, and (ii) determination of the corresponding production scheduling in each factory. Scheduling of machine maintenance problems aim to reduce the effect of breakdown and maximize the facility availability at minimum cost. However, in many distributed scheduling problems, machine scheduling assumes that machines are available all the time. In fact, every machine requires maintenance, and the maintenance policy directly affects the machine's availability. Consequently, it interrupts the production scheduling determined. This paper designed a hypothetical distributed scheduling model with three different problem sizes to demonstrate the significance of simultaneously solving machine maintenance problem with distributed scheduling problem. We applied genetic algorithm with dominant genes methodology to solve the model. Several optimization approaches, including separating and integrating the two problems, are tested and compared. The results show the merit of integration

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考虑机器维修的分布式调度问题研究

本文研究了机器维修对分布式调度问题的影响。分布式调度的目标是通过同时解决两个问题来实现系统效率的最大化:(1)将工作分配到合适的工厂，(2)确定每个工厂相应的生产调度。机器维修问题调度的目的是减少故障的影响，以最小的成本最大化设备的可用性。然而，在许多分布式调度问题中，机器调度假设机器始终可用。实际上，每台机器都需要维护，维护策略直接影响机器的可用性。因此，它中断了已确定的生产调度。本文设计了一个具有三种不同问题规模的分布式调度模型，以说明用分布式调度问题同时解决机器维修问题的意义。采用显性基因遗传算法对模型进行求解。对分离和整合两种优化方法进行了测试和比较。结果表明了集成的优点

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2006 IEEE Conference on Cybernetics and Intelligent Systems

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