Modeling, simulation and optimization of maintenance cost aspects on multi-unit systems by stochastic Petri nets with predicates

Santos, Fernando P.; Teixeira, A.P.; Soares, C. Guedes

doi:10.1177/0037549718782655

Cited by 25 publications

(10 citation statements)

References 51 publications

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“…Moreover, other variants of PNs, besides CPNs, have been used to optimize maintenance strategies of complex real-life systems. Santos et al 62 proposed a methodology combining generalized stochastic Petri Nets with predicates with Monte Carlo simulation to model and optimize different maintenance strategies of repairable multi-unit systems, their dynamic behavior under different failure and repair distributions as well as economic dependencies of their subsystems.…”

Section: Cpn-based Supply Chain Simulationmentioning

confidence: 99%

A colored petri net-based modeling method for supply chain inventory management

Drakaki

Tzionas

2021

SIMULATION

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Supply chain planning and control approaches need to include a wide range of factors in order to optimize production. Supply chain simulation modeling has been identified as a potential methodology toward increasing the efficiency of current systems to this end. The purpose of this paper is to evaluate the impact of inventory management decisions on supply chain performance using a Colored Petri Net based simulation modeling method. The presented method uses hierarchical timed Colored Petri Nets to model inventory management in a multi-stage serial supply chain, under normal operating conditions, and under the presence of disruptions, for both traditional and information sharing configurations. Disruptions are introduced as canceled orders and canceled deliveries, in a time period. Supply chain performance has been evaluated, in the context of order variance amplification and stockout amplification. Validation of the method is done by comparing results obtained for the bullwhip effect with published literature results, as well as by state space analysis results.

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Section: Cpn-based Supply Chain Simulationmentioning

confidence: 99%

A colored petri net-based modeling method for supply chain inventory management

Drakaki

Tzionas

2021

SIMULATION

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“…Thus, the authors in [38] presented the stochastic Petri nets where transitions are related with time-driven probabilistic models. In [39], a complex system with multi-unit configuration is modeled by generalized stochastic Petri nets (GSPN) where the time to failure of degraded components follows a Weibull distribution.…”

Section: Generalized Stochastic Petri Netsmentioning

confidence: 99%

Impact of Remediation-Based Maintenance on the Reliability of a Coal-Fired Power Plant Using Generalized Stochastic Petri Nets

2021

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Rapid changes in electricity power markets have increased the production costs of coal-fired power plants and pushed their production to the limits of profitability. For power plants currently in operation, a possible approach to cope with this issue is to introduce novel methods that increase the plant’s reliability and availability. Coal mills are a subsystem that should ensure a plant’s availability without unexpected breakdowns. Remediation-based maintenance is defined as a set of actions performed after fault detection that do not require instant shutdown due to safety reasons. The aim of this paper was to provide a scientific confirmation that by implementing a novel remediation-based maintenance strategy, electricity production breakdowns can be significantly reduced. First, the performance of the proposed maintenance method was proved in simulation where coal mills were modeled by generalized stochastic Petri nets. The maintenance strategy was then experimentally verified in a 220 MW coal-fired power plant located in Croatia, where the plant’s availability, reliability and efficiency were increased.

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“…A full description of the Genetic Algorithm method can be found in Santos et al (2018) and Holland (1983). In a Genetic Algorithm, an initial population is cross-bred and mutated over a number of generations, with the best candidates selected to proceed at each generation.…”

Section: Optimizationmentioning

confidence: 99%

Efficient Risk Based Optimization of Large System Models using a Reduced Petri Net Methodology

Naybour¹,

Andrews²,

Chiachío³

2019

Proceedings of the 29th European Safety and Reliability Conference (ESREL)

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The methodology presented in this paper is a two-stage optimization approach that can be applied to large system level models, in this case using a Stochastic Petri Net (SPN) framework, to produce an equivalent model response at a reduced computational cost. The method consists of generating a reduced SPN which approximates the behavior of its large counterpart with a shorter simulation time. Parameters in this reduced structure are updated following a combined Approximate Bayesian Computation and Subset Simulation framework. In the first stage, optimization of the reduced model via a Genetic Algorithm provides a first approximation of the optimal solutions for the full system level model. In the second stage, these approximate optimal solutions then form the starting point of a short optimization of the large SPN to fine tune the results using a reduced solution space. This method is demonstrated for a sub-section of an SPN of a fire protection system. Optimization of the full model with a Genetic Algorithm is compared to the optimization through this two-stage approach to demonstrate the capability of the methodology. Results show good model agreement at a reduced computational cost.

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Modeling, simulation and optimization of maintenance cost aspects on multi-unit systems by stochastic Petri nets with predicates

Cited by 25 publications

References 51 publications

A colored petri net-based modeling method for supply chain inventory management

A colored petri net-based modeling method for supply chain inventory management

Impact of Remediation-Based Maintenance on the Reliability of a Coal-Fired Power Plant Using Generalized Stochastic Petri Nets

Efficient Risk Based Optimization of Large System Models using a Reduced Petri Net Methodology

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