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Development of computer-controlled material handling model by means of fuzzy logic and genetic algorithms

机译:利用模糊逻辑和遗传算法开发计算机控制物料搬运模型

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The aim of the study is to develop a unique approach to the problem of material handling control by means of artificial intelligence (computer intelligence) with respect to automated guided vehicle (AGV) path routing. In the analysed case the material handling system is fully integrated with IT tools and is considered under the constraint that the path cannot be changed during travel along a given loop, therefore, the path is predicted prior to every successive loop. The vector (ordered set) of stations requesting transportation service was determined by means of fuzzy logic, while the sequence of stations in a loop was optimised with genetic algorithms. The dispatch station can play the role of a switching station between the zones, where every AGV path loop begins and ends. This paper presents a system for both AGVs and semi-autonomous transportation vehicles where the human driver is informed of the route to follow. The subsequent verification of the developed model, by means of computer simulation, confirms that the proposed solution is effective. While the presented solution can be applied in a discrete manufacturing system, it can be considered as a part of the general manufacturing concept. (C) 2019 Elsevier B.V. All rights reserved.
机译:该研究的目的是针对自动导引车(AGV)路径路由,通过人工智能(计算机智能)开发一种解决物料搬运控制问题的独特方法。在分析的情况下,物料搬运系统已与IT工具完全集成在一起,并被视为在沿给定回路行进期间无法更改路径的约束下进行考虑,因此,在每个后续回路之前都应预测路径。通过模糊逻辑确定了需要运输服务的站点的向量(有序集),而循环中的站点顺序则通过遗传算法进行了优化。调度站可以充当区域之间的交换站,每个AGV路径回路都在该区域开始和结束。本文介绍了一种适用于AGV和半自动运输车辆的系统,在此系统中,驾驶员会被告知要遵循的路线。通过计算机仿真对开发的模型进行的后续验证,证实了所提出的解决方案是有效的。虽然提出的解决方案可以应用于离散制造系统中,但可以将其视为一般制造概念的一部分。 (C)2019 Elsevier B.V.保留所有权利。

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