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Petri Net Decomposition Approach to Optimization of Route Planning Problems for AGV Systems

机译:用于AGV系统路径规划问题优化的Petri网分解方法。

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In this paper, we propose a Petri Net (PN) decomposition approach to the optimization of route planning problems for automated guided vehicles (AGVs) in semiconductor fabrication bays. An augmented PN is developed to model the concurrent dynamics for multiple AGVs. The route planning problem to minimize the total transportation time is formulated as an optimal transition firing sequence problem for the PN. The PN is decomposed into several subnets such that the subnets are made independent by removing the original shared places and creating its own set of resource places for each subnet with the appropriate connections. The partial solution derived at each subnet is not usually making a feasible solution for the entire PN. The penalty function algorithm is used to integrate the solutions derived at the decomposed subnets. The optimal solution for each subnet is repeatedly generated by using the shortest-path algorithm in polynomial time with a penalty function embedded in the objective function. The effectiveness of the proposed method is demonstrated for a practical-sized route planning problem in semiconductor fabrication bay from computational experiments.
机译:在本文中,我们提出了一种Petri网(PN)分解方法来优化半导体制造湾中自动导引车(AGV)的路线规划问题。开发了增强型PN来模拟多个AGV的并发动态。将使总运输时间减至最少的路线规划问题被公式化为PN的最佳过渡点火顺序问题。 PN分解为几个子网,这样,通过删除原始共享位置并为每个具有适当连接的子网创建自己的资源位置集,可以使子网独立。从每个子网导出的部分解决方案通常不能为整个PN制定可行的解决方案。惩罚函数算法用于集成在分解子网中得出的解。通过在多项式时间内使用最短路径算法,并在目标函数中嵌入惩罚函数,反复生成每个子网的最优解。通过计算实验证明了所提出方法的有效性。

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