Decision Support System, Based on the Paradigm of the Petri Nets, for the Design and Operation of a Dairy Plant

Latorre-Biel, Juan-Ignacio; Jiménez‐Macías, Emilio; Blanco‐Fernández, Julio; Cámara, Eduardo Martínez; Sáenz-Díez, J.C.; Pérez-Parte, Mercedes

doi:10.1515/ijfe-2015-0063

Cited by 13 publications

(4 citation statements)

References 18 publications

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“…PN is a widely used mathematical tool for modeling the dynamic behaviors of FMSs and many other manufacturing systems [15][16][17][25][26][27]. This section briefly introduces some basics of PNs first and then the PN models of FMSs for multiobjective scheduling.…”

Section: Pn Models Of Fmssmentioning

confidence: 99%

A Novel MOEA/D for Multiobjective Scheduling of Flexible Manufacturing Systems

et al. 2019

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This paper considers the multiobjective scheduling of flexible manufacturing systems (FMSs). Due to high degrees of route flexibility and resource sharing, deadlocks often exhibit in FMSs. Manufacturing tasks cannot be finished if any deadlock appears. For solving such problem, this work develops a deadlock-free multiobjective evolutionary algorithm based on decomposition (DMOEA/D). It intends to minimize three objective functions, i.e., makespan, mean flow time, and mean tardiness time. The proposed algorithm can decompose a multiobjective scheduling problem into a certain number of scalar subproblems and solves all the subproblems in a single run. A type of a discrete differential evolution (DDE) algorithm is also developed for solving each subproblem. The mutation operator of the proposed DDE is based on the hamming distance of two randomly selected solutions, while the crossover operator is based on Generalization of Order Crossover. Experimental results demonstrate that the proposed DMOEA/D can significantly outperform a Pareto domination-based algorithm DNSGA-II for both 2-objective and 3-objective problems on the studied FMSs.

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Section: Pn Models Of Fmssmentioning

confidence: 99%

A Novel MOEA/D for Multiobjective Scheduling of Flexible Manufacturing Systems

et al. 2019

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“…Depending on the class of Petri net, considered to represent the model of the system, the roles these numbers may play are more or less diverse. For example, an autonomous generalized Petri net would present numbers that represent the initial marking of the places of the net, as well as the weight of every arc between places and transitions [6]. A timed Petri net would present numbers associated to the delay time of firing certain transitions once they are enabled.…”

Section: Petri Nets In Decision Support Systemsmentioning

confidence: 99%

Petri Net Models Optimized for Simulation

Latorre-Biel¹,

Jiménez‐Macías²

2019

Simulation Modelling Practice and Theory

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Petri nets and simulation are a modeling paradigm and a tool, respectively, which may be successfully combined for diverse applications, such as performance evaluation, decision support, or training on complex systems. Simulation may require significant computer resources; hence, in this chapter, two Petri net-based formalisms are analyzed for profiting from their respective advantages for modeling, simulation, and decision-making support: a set of alternative Petri nets and a compound Petri net. These formalisms, as well as the transformation algorithms between them, are detailed and an illustrative example is provided. Among the main advantages of these formalisms, their intuitive application for modeling discrete event systems in the process of being designed, as well as the compactness that may present the resulting model, in the case of a compound Petri net, leading to efficient decision making, can be mentioned.

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“…This formalism has been applied to a wide range of different fields [14,15]. The Petri nets present many favourable features, such as an important body of knowledge referred to as subclasses of formalisms, structural analysis techniques and properties, or methodologies for implementing performance evaluation and simulation [16].…”

Section: Introductionmentioning

confidence: 99%

Equivalent and Efficient Optimization Models for an Industrial Discrete Event System with Alternative Structural Configurations

2018

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Discrete event systems in applications, such as industry and supply chain, may show a very complex behavior. For this reason, their design and operation may be carried out by the application of optimization techniques for decision making in order to obtain their highest performance. In a general approach, it is possible to implement these optimization techniques by means of the simulation of a Petri net model, which may require an intensive use of computational resources. One key factor in the computational cost of simulation-based optimization is the size of the model of the system; hence, it may be useful to apply techniques to reduce it. This paper analyzes the relationship between two Petri net formalisms, currently used in the design of discrete event systems, where it is usual to count on a set of alternative structural configurations. These formalisms are a particular type of parametric Petri nets, called compound Petri nets, and a set of alternative Petri nets. The development of equivalent models under these formalisms and the formal proof of this equivalence are the main topics of the paper. The basis for this formal approach is the graph of reachable markings, a powerful tool able to represent the behavior of a discrete event system and, hence, to show the equivalence between two different Petri net models. One immediate application of this equivalence is the substitution of a large model of a system by a more compact one, whose simulation may be less demanding in the use of computational resources.

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Decision Support System, Based on the Paradigm of the Petri Nets, for the Design and Operation of a Dairy Plant

Cited by 13 publications

References 18 publications

A Novel MOEA/D for Multiobjective Scheduling of Flexible Manufacturing Systems

A Novel MOEA/D for Multiobjective Scheduling of Flexible Manufacturing Systems

Petri Net Models Optimized for Simulation

Equivalent and Efficient Optimization Models for an Industrial Discrete Event System with Alternative Structural Configurations

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