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Development of deterministic collision-avoidance algorithms for routing automated guided vehicles.

机译：确定性防撞算法的开发，用于自动导引车的选路。

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摘要

A manufacturing job spends a small portion of its total flow time being processed on machines, and during the remaining time, either it is in a queue or being transported from one work center to another. In a fully automated material-handling environment, automated guided vehicles (AGV) perform the function of transporting the jobs between workstations, and high operational costs are involved in these material-handling activities. Consequently, the AGV route schedule dictates subsequent work-center scheduling.;For an AGV job transportation schedule to be effective, the issue of collisions amongst AGV during travel needs to be addressed. Such collisions cause stalemate situations that potentially disrupt the flow of materials in the job shop, adding to the non-value time of job processing, and thus, increase the material handling and inventory holding costs. The current research goal was to develop a methodology that could effectively and efficiently derive optimal AGV routes for a given set of transportation requests, considering the issue of collisions amongst AGV during travel.;As part of the solution approach in the proposed work, an integer linear program was formulated in Phase I with the capability of optimally predicting the AGV routes for a deterministic set of transportation requests. Collision avoidance constraints were developed in this model. The model was programmed using OPL/Visual Basic, and the program feasibility were experimentally analyzed for different problem domain specifications. Due to the complexity and combinatorial nature of the formulation in Phase I, computationally it was expected to be NP-Hard. Hence, to improve the computation prediction capability (estimation of upper bounds), it was required that in Phase II, heuristics be developed to relax the computational complexity of the original problem. In Phase III, experimental techniques were used to compute the lower and upper bounds of the original problem. The performances of the different heuristics were compared using experimental analysis.
机译：制造作业将其总流程时间的一小部分花费在机器上进行处理，而在剩余时间中，它要么排在队列中，要么从一个工作中心传输到另一个工作中心。在全自动物料搬运环境中，自动导引车（AGV）执行在工作站之间运输作业的功能，并且这些物料搬运活动涉及较高的运营成本。因此，AGV路线时间表决定了后续的工作中心时间表。为了使AGV作业运输时间表有效，需要解决AGV之间在行驶过程中发生碰撞的问题。这种碰撞会导致僵局，可能会破坏车间中的物料流，增加作业的非价值时间，从而增加物料处理和库存的成本。当前的研究目标是开发一种方法，考虑到旅行期间AGV之间的碰撞问题，该方法可以针对给定的一组运输请求有效而高效地得出最佳AGV路线。作为拟议工作中解决方案的一部分，整数在第一阶段制定了线性程序，能够针对一组确定性的运输请求最优地预测AGV路线。在该模型中开发了避免碰撞的约束。使用OPL / Visual Basic对模型进行编程，并针对不同的问题域规范对程序的可行性进行了实验分析。由于第一阶段制剂的复杂性和组合性质，从计算上讲，它被认为是NP-硬质。因此，为了提高计算预测能力（上限的估计），要求在阶段II中开发启发式算法以缓解原始问题的计算复杂性。在第三阶段，使用实验技术来计算原始问题的上下限。使用实验分析比较了不同启发式方法的性能。

著录项

  • 作者

    Pai, Arun S.;

  • 作者单位

    Rochester Institute of Technology.;

  • 授予单位 Rochester Institute of Technology.;
  • 学科 Engineering Industrial.
  • 学位 M.S.
  • 年度 2008
  • 页码 133 p.
  • 总页数 133
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 一般工业技术;
  • 关键词

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