scholarly journals Control of Discrete Event Systems by Means of Discrete Optimization and Disjunctive Colored PNs: Application to Manufacturing Facilities

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Juan-Ignacio Latorre-Biel ◽  
Emilio Jiménez-Macías ◽  
Mercedes Pérez de la Parte ◽  
Julio Blanco-Fernández ◽  
Eduardo Martínez-Cámara
Keyword(s):  
Discrete Event ◽  
Optimal Solution ◽  
Solution Space ◽  
Discrete Systems ◽  
Discrete Control ◽  
Classic Approach ◽  
Event Dynamic ◽  
Time Required ◽  
Computational Resources ◽  
Control Methodology

Artificial intelligence methodologies, as the core of discrete control and decision support systems, have been extensively applied in the industrial production sector. The resulting tools produce excellent results in certain cases; however, the NP-hard nature of many discrete control or decision making problems in the manufacturing area may require unaffordable computational resources, constrained by the limited available time required to obtain a solution. With the purpose of improving the efficiency of a control methodology for discrete systems, based on a simulation-based optimization and the Petri net (PN) model of the real discrete event dynamic system (DEDS), this paper presents a strategy, where a transformation applied to the model allows removing the redundant information to obtain a smaller model containing the same useful information. As a result, faster discrete optimizations can be implemented. This methodology is based on the use of a formalism belonging to the paradigm of the PN for describing DEDS, the disjunctive colored PN. Furthermore, the metaheuristic of genetic algorithms is applied to the search of the best solutions in the solution space. As an illustration of the methodology proposal, its performance is compared with the classic approach on a case study, obtaining faster the optimal solution.

scholarly journals Petri Net-Based Semi-Compiled Code Generation for Programmable Logic Controllers

2021 ◽  
Vol 11 (15) ◽  
pp. 7161
Author(s):  
Igor Azkarate ◽  
Mikel Ayani ◽  
Juan Carlos Mugarza ◽  
Luka Eciolaza
Keyword(s):  
Code Generation ◽  
Resource Sharing ◽  
Discrete Event ◽  
Transition Function ◽  
Discrete Event Dynamic Systems ◽  
Programmable Logic ◽  
External Software ◽  
Implementation Methodology ◽  
Event Dynamic ◽  
Step Transition

Industrial discrete event dynamic systems (DEDSs) are commonly modeled by means of Petri nets (PNs). PNs have the capability to model behaviors such as concurrency, synchronization, and resource sharing, compared to a step transition function chart or GRAphe Fonctionnel de Commande Etape Transition (GRAFCET) which is a particular case of a PN. However, there is not an effective systematic way to implement a PN in a programmable logic controller (PLC), and so the implementation of such a controller outside a PLC in some external software that will communicate with the PLC is very common. There have been some attempts to implement PNs within a PLC, but they are dependent on how the logic of places and transitions is programmed for each application. This work proposes a novel application-independent and platform-independent PN implementation methodology. This methodology is a systematic way to implement a PN controller within industrial PLCs. A great portion of the code will be validated automatically prior to PLC implementation. Net structure and marking evolution will be checked on the basis of PN model structural analysis, and only net interpretation will be manually coded and error-prone. Thus, this methodology represents a systematic and semi-compiled PN implementation method. A use case supported by a digital twin (DT) is shown where the automated solution required by a manufacturing system is carried out and executed in two different devices for portability testing, and the scan cycle periods are compared for both approaches.

How to model planning and scheduling problems using constraint networks on timelines

2010 ◽  
Vol 25 (3) ◽  
pp. 319-336 ◽  
Author(s):  
Gérard Verfaillie ◽  
Cédric Pralet ◽  
Michel Lemaître
Keyword(s):  
Dynamic Systems ◽  
Discrete Event ◽  
Discrete Event Dynamic Systems ◽  
Scheduling Problems ◽  
Planning And Scheduling ◽  
Constraint Networks ◽  
Modeling Power ◽  
Mission Management ◽  
Event Dynamic ◽  
Domain Management

AbstractThe CNT framework (Constraint Network on Timelines) has been designed to model discrete event dynamic systems and the properties one knows, one wants to verify, or one wants to enforce on them. In this article, after a reminder about the CNT framework, we show its modeling power and its ability to support various modeling styles, coming from the planning, scheduling, and constraint programming communities. We do that by producing and comparing various models of two mission management problems in the aerospace domain: management of a team of unmanned air vehicles and of an Earth observing satellite.

Frameworks for evaluating discrete event dynamic systems

1993 ◽  
Vol 3 (2-3) ◽  
pp. 113-118 ◽  
Author(s):  
Bernard P. Zeigler ◽  
William H. Sanders
Keyword(s):  
Dynamic Systems ◽  
Discrete Event ◽  
Discrete Event Dynamic Systems ◽  
Event Dynamic

Predictive Search for Capacitated Multi-Item Lot Sizing Problems

2021 ◽  
Author(s):  
Tao Wu
Keyword(s):  
Mathematical Programming ◽  
Heuristic Search ◽  
Lot Sizing ◽  
Optimal Solution ◽  
Solution Space ◽  
Search Method ◽  
Benchmark Problems ◽  
Solution Quality ◽  
Programming Technique ◽  
Advanced Analytics

For capacitated multi-item lot sizing problems, we propose a predictive search method that integrates machine learning/advanced analytics, mathematical programming, and heuristic search into a single framework. Advanced analytics can predict the probability that an event will happen and has been applied to pressing industry issues, such as credit scoring, risk management, and default management. Although little research has applied such technique for lot sizing problems, we observe that advanced analytics can uncover optimal patterns of setup variables given properties associated with the problems, such as problem attributes, and solution values yielded by linear programming relaxation, column generation, and Lagrangian relaxation. We, therefore, build advanced analytics models that yield information about how likely a solution pattern is the same as the optimum, which is insightful information used to partition the solution space into incumbent, superincumbent, and nonincumbent regions where an analytics-driven heuristic search procedure is applied to build restricted subproblems. These subproblems are solved by a combined mathematical programming technique to improve solution quality iteratively. We prove that the predictive search method can converge to the global optimal solution point. The discussion is followed by computational tests, where comparisons with other methods indicate that our approach can obtain better results for the benchmark problems than other state-of-the-art methods. Summary of Contribution: In this study, we propose a predictive search method that integrates machine learning/advanced analytics, mathematical programming, and heuristic search into a single framework for capacitated multi-item lot sizing problems. The advanced analytics models are used to yield information about how likely a solution pattern is the same as the optimum, which is insightful information used to divide the solution space into incumbent, superincumbent, and nonincumbent regions where an analytics-driven heuristic search procedure is applied to build restricted subproblems. These subproblems are solved by a combined mathematical programming technique to improve solution quality iteratively. We prove that the predictive search method can converge to the global optimal solution point. Through computational tests based on benchmark problems, we observe that the proposed approach can obtain better results than other state-of-the-art methods.

Machine learning-based discrete event dynamic surrogate model of communication systems for simulating the command, control, and communication system of systems

SIMULATION ◽  
2018 ◽  
Vol 95 (8) ◽  
pp. 673-691 ◽  
Author(s):  
Bong Gu Kang ◽  
Kyung-Min Seo ◽  
Tag Gon Kim
Keyword(s):  
Machine Learning ◽  
Communication System ◽  
Surrogate Model ◽  
Discrete Event ◽  
System Of Systems ◽  
System Model ◽  
Characteristic Functions ◽  
Combat Effectiveness ◽  
Communication Effect ◽  
Event Dynamic

Command and control (C2) and communication are at the heart of successful military operations in network-centric warfare. Interoperable simulation of a C2 system model and a communication (C) system model may be employed to interactively analyze their detailed behaviors. However, such simulation would be inefficient in simulation time for analysis of combat effectiveness of the C2 model against possible input combinations while considering the communication effect in combat operations. This study proposes a discrete event dynamic surrogate model (DEDSM) for the C model, which would be integrated with the C2 model and simulated. The proposed integrated simulation reduces execution time markedly in analysis of combat effectiveness without sacrificing the accuracy reflecting the communication effect. We hypothesize the DEDSM as a probabilistic priority queuing model whose semantics is expressed in a discrete event systems specification model with some characteristic functions unknown. The unknown functions are identified by machine learning with a data set generated by interoperable simulation of the C2 and C models. The case study with the command, control, and communication system of systems first validates the proposed approach through an equivalence test between the interoperable simulation and the proposed one. It then compares the simulation execution times and the number of events exchanged between the two simulations.

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