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首页> 外文期刊>International Journal of Computer Integrated Manufacturing >A hybrid particle swarm optimisation algorithm and fuzzy logic for process planning and production scheduling integration in holonic manufacturing systems
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A hybrid particle swarm optimisation algorithm and fuzzy logic for process planning and production scheduling integration in holonic manufacturing systems

机译：用于整体制造系统的过程计划和生产调度集成的混合粒子群优化算法和模糊逻辑

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摘要

Modern manufacturing systems have to cope with dynamic changes and uncertainties such as machine breakdown, hot orders and other kinds of disturbances. Holonic manufacturing systems (HMS) provide a flexible and decentralised manufacturing environment to accommodate changes dynamically. HMS is based on the notion of holon, an autonomous, co-operative and intelligent entity which is able to collaborate with other holons to complete the tasks. HMS requires a robust coordination and collaboration mechanism to allocate available resources to achieve the production goals. In this paper, a basic integrated process planning and scheduling system, which is applicable to the holonic manufacturing systems is presented. A basic architecture of holonic manufacturing system is proposed from the viewpoint of the process planning and the scheduling systems. Here, the process planning is defined as a process to select suitable machining sequences of machining features and suitable operation sequences of machining equipments, taking into consideration the short-term and long-term capacities of machining equipments. A fuzzy inference system (FIS), in choosing alternative machines for integrated process planning and scheduling of a job shop in HMS, is presented. Instead of choosing alternative machines randomly, machines are being selected based on the machine's capacity. The mean time for failure (MTF) values are input in a fuzzy inference mechanism, which outputs the machine reliability. The machine is then being penalised based on the fuzzy output. The most reliable machine will have the higher priority to be chosen. In order to overcome the problem of un-utilisation machines, sometimes faced by unreliable machine, the hybrid particle swarm optimisation (PSO) with differential evolution (DE) has been applied to balance the load for all the machines. Simulation studies show that the proposed system can be used as an effective way of choosing machines in integrated process planning and scheduling.
机译：现代制造系统必须应对动态变化和不确定性，例如机器故障，热订单和其他类型的干扰。 Holonic制造系统（HMS）提供了灵活的分散式制造环境，可以动态地适应变化。 HMS基于holon的概念，holon是一个自治，合作且智能的实体，能够与其他Holon合作完成任务。 HMS需要强大的协调和协作机制来分配可用资源以实现生产目标。本文提出了一种适用于整体制造系统的基本集成工艺计划与调度系统。从过程计划和调度系统的角度出发，提出了整体制造系统的基本架构。在此，工艺计划被定义为考虑到加工设备的短期和长期能力来选择合适的加工特征的加工顺序和合适的加工设备的操作顺序的过程。提出了一种模糊推理系统（FIS），用于选择替代机器以对HMS中的车间进行集成过程规划和调度。并非随机选择其他机器，而是根据机器的容量选择机器。平均故障时间（MTF）值输入到模糊推理机制中，该机制可输出机器可靠性。然后根据模糊输出对机器进行惩罚。最可靠的机器将具有更高的优先级。为了克服有时无法使用的机器所面临的未使用机器的问题，已应用具有差分进化（DE）的混合粒子群优化（PSO）来平衡所有机器的负载。仿真研究表明，该系统可作为集成过程计划和调度中机器选择的有效方法。

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