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Method for optimal expression of heuristic variable ordering of binary decision diagrams for workshop manufacturing systems

A binary decision diagram and manufacturing system technology, applied in the field of automatic manufacturing system modeling and control, can solve the problems of dependence on the initial variable sequence and high time complexity, and achieve the effects of fast acquisition speed, speed up and reduction of structure.

Active Publication Date: 2018-03-16
NANJING UNIV OF SCI & TECH
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Problems solved by technology

The sifting algorithm can obtain a local optimal variable order, but its time complexity is high and it depends heavily on the initial variable order (Rudell R. Dynamic variable ordering for ordered binary decision diagrams. In: Proceedings of International Conference on Computer aided Design, 1993.42~ 47)

Method used

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  • Method for optimal expression of heuristic variable ordering of binary decision diagrams for workshop manufacturing systems
  • Method for optimal expression of heuristic variable ordering of binary decision diagrams for workshop manufacturing systems
  • Method for optimal expression of heuristic variable ordering of binary decision diagrams for workshop manufacturing systems

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Embodiment 1

[0048] figure 2 is an example of a workshop manufacturing system, including three robots (R 1 , R 2 , R 3 : Each robot can hold one product at the same time) and four machines (M 1 , M 2 , M 3 , M 4 : Each machine can process two products at the same time), and three input buffers (I 1 ,I 2 ,I 3 ) and three output buffers (O 1 ,O 2 ,O 3 ). The system is mainly divided into two production lines with three robots as the core, and its operation process is as follows:

[0049] J 1 : I 1 →R 1 → M 1 →R 2 → M 2 →R 3 →O 1

[0050] or I 1 →R 1 → M 3 →R 2 → M 4 →R 3 →O 1

[0051] J 2 : I 2 →R 2 → M 2 →R 2 →O 2

[0052] J 3 : I 3 →R 3 → M 4 →R 2 → M 3 →R 1 →O 3

[0053] against figure 2 In the workshop manufacturing system, the steps of the heuristic binary decision graph variable order optimization representation method are as follows:

[0054] first step, yes figure 2 The workshop manufacturing system shown is modeled, image 3 is the...

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Abstract

The invention discloses a method for optimal expression of heuristic variable ordering of binary decision diagrams for workshop manufacturing systems. The method comprises the following steps of: modeling an enterprise workshop production system by adoption of a Petri network; calculating mutual correlation degrees between every two libraries of the Petri network; grouping the libraries accordingto the correlation degrees between the libraries by taking a resource library as a center; and searching a group corresponding to the resource library and OBDD coding sequences of all the libraries byadoption of a depth-first strategy. According to the method, a manufacturing workshop Petri network model is taken as an object, a heuristic OBDD variable ordering optimization technology is adopted,so that the state explosion problem in system model analysis is eased; and the method has the advantages of effectively decreasing model expression structures and improving the model analysis speed.

Description

technical field [0001] The invention relates to modeling and control technology of an automatic manufacturing system, in particular to a heuristic binary decision graph variable sequence optimization expression method of a workshop manufacturing system. Background technique [0002] Pastor et al. established an OBDD-based Petri net coincidence analysis method, using OBDD to represent the characteristic functions identified by the Petri net library, and analyzed various properties of the Petri net. The number of nodes in OBDD is very sensitive to the variable order, and the same function may have a linear or exponential difference in the number of nodes corresponding to the OBDD table under different variable orders. Therefore, variable ordering is an important issue in OBDD applications. [0003] The window algorithm for exchanging variables within a certain range proposed by M.Fujita, its time complexity and the number of OBDD nodes after sorting depend on the size k of th...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/04
CPCG06Q10/04G06Q50/04Y02P90/30
Inventor 黄波丁高瞻蔡志成张皓明杨余旺
Owner NANJING UNIV OF SCI & TECH