Cycle Time Optimization of Deterministic Timed Weighted Marked Graphs by Transformation

Timed marked graphs, a special class of Petri nets, are extensively used to model and analyze cyclic manufacturing systems. Weighted marked graphs are convenient to model automated production systems, such as robotic work cells or embedded systems, and reduce the size of the model. The main problem...

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Bibliographic Details
Published in:IEEE transactions on control systems technology Vol. 25; no. 4; pp. 1318 - 1330
Main Authors: Zhou He, Zhiwu Li, Giua, Alessandro
Format: Journal Article
Language:English
Published: IEEE 01.07.2017
Institute of Electrical and Electronics Engineers
Subjects:
Automatic Control Engineering
Computer Science
Cycle time optimization
Delays
Manufacturing systems
Mixed integer linear programming
mixed integer linear programming problem (ILPP)
Modeling
Optimization
Throughput
timed Petri net (PN)
Transforms
weighted marked graph
ISSN:1063-6536, 1558-0865
Online Access:Get full text
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Summary:Timed marked graphs, a special class of Petri nets, are extensively used to model and analyze cyclic manufacturing systems. Weighted marked graphs are convenient to model automated production systems, such as robotic work cells or embedded systems, and reduce the size of the model. The main problem for designers is to find a tradeoff between minimizing the cost of the resources and maximizing the system's throughput (also called cycle time). It is possible to apply analytical techniques for the cycle time optimization problem of such systems. The problem consists in finding an initial marking to minimize the cycle time (i.e., maximize the throughput) while the weighted sum of tokens in places is less than or equal to a given value. We transform a weighted marked graph into several equivalent marked graphs and formulate a mixed integer linear programming model to solve this problem. Moreover, several techniques are proposed to reduce the complexity of the proposed method. We show that the proposed method can always find an optimal solution.
ISSN:1063-6536
1558-0865
DOI:10.1109/TCST.2016.2613967