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The purpose of this paper is to introduce a method for modelling the multi-state repairable systems subject to stochastic degradation processes by using the coloured stochastic Petri nets (CSPN). The method is a compact and flexible Petri nets model for multi-state repairable systems and offers an alternative to the combinatory of Markov graphs.

The method is grounded on specific theorems used to design an algorithm for systematic construction of multi-state repairable systems models, whatever is their size.

Stop and constraint functions were derived from these theorems and allow to considering k-out-of-n structure systems and to identifying the minimal cut sets, useful to monitoring the states evolution of the system.

The properties of this model will be studied, and new investigations will help to demonstrate the feasibility of the approach in real world, and more complex structure will be considered.

The simulation models based on CSPN can be used as a tool by maintenance decision makers, for prediction of the effectiveness of maintenance strategies.

The proposed approach and model provide an efficient tool for advanced investigations on the development and implementation of maintenance policies and strategies in real life.

This paper aims to propose a methodology based on Petri nets to evaluate the reliability characteristics of the pulping system of a paper industry in a realistic working environment. The effects of occurrence of failure/course of actions and availability of repair facilities on system performance have been investigated.

Stochastic Reward Nets (SRN), an extension of Petri nets, are applied to model the interactions among the active/standby units of the system and reliability parameters have been evaluated using Monte Carlo simulation.

The methodology adopted in this paper provides a better understanding of the behavior of the system under varying operating conditions. Reliability analysis of a pulping system of a paper industry presented in this paper will help the management in deciding upon the maintenance strategy to be adopted to improve the performance of the system and consequently reduce the operation and maintenance cost.

The application of the proposed technique will not only help the reliability engineers/managers/system engineers to understand the dynamic behavior of complex systems but also helps to plan/adapt suitable maintenance policies to improve system reliability/availability.

Taking into consideration the current human need for agricultural produce such as rice that requires water for growth, the optimal consumption of this valuable liquid is important. Unfortunately, the traditional use of water by humans for agricultural purposes contradicts the concept of optimal consumption. Therefore, designing and implementing a mechanized irrigation system is of the highest importance. This system includes hardware equipment such as liquid altimeter sensors, valves and pumps which have a failure phenomenon as an integral part, causing faults in the system. Naturally, these faults occur at probable time intervals, and the probability function with exponential distribution is used to simulate this interval. Thus, before the implementation of such high-cost systems, its evaluation is essential during the design phase.

The proposed approach included two main steps: offline and online. The offline phase included the simulation of the studied system (i.e. the irrigation system of paddy fields) and the acquisition of a data set for training machine learning algorithms such as decision trees to detect, locate (classification) and evaluate faults. In the online phase, C5.0 decision trees trained in the offline phase were used on a stream of data generated by the system.

The proposed approach is a comprehensive online component-oriented method, which is a combination of supervised machine learning methods to investigate system faults. Each of these methods is considered a component determined by the dimensions and complexity of the case study (to discover, classify and evaluate fault tolerance). These components are placed together in the form of a process framework so that the appropriate method for each component is obtained based on comparison with other machine learning methods. As a result, depending on the conditions under study, the most efficient method is selected in the components. Before the system implementation phase, its reliability is checked by evaluating the predicted faults (in the system design phase). Therefore, this approach avoids the construction of a high-risk system. Compared to existing methods, the proposed approach is more comprehensive and has greater flexibility.

By expanding the dimensions of the problem, the model verification space grows exponentially using automata.

Unlike the existing methods that only examine one or two aspects of fault analysis such as fault detection, classification and fault-tolerance evaluation, this paper proposes a comprehensive process-oriented approach that investigates all three aspects of fault analysis concurrently.

This paper aims to develop a Petri net model for analyzing the quality conflict and its resolution of a complex product. The result aims to assist decision makers (DMs) to properly select their activities when a quality conflict has happened.

According to the features of Petri net and conflict analysis theory, a novel Petri net for conflict analysis (PNCA) is designed which contains transition and preference labels to describe DMs' decision activities and profit comparisons. Additionally, a generating approach is proposed, which can help DMs to construct a PNCA. Furthermore, based on players' bounded rationality, the equilibrium of PNCA is studied to provide scientific supports for DMs' decision‐making. A case study on an aircraft production system is conducted to demonstrate the feasibility and effectiveness of the new model, which furnishes a fresh perspective on the supply chain quality management of a complex product.

A new methodology is proposed for the domain of conflict analysis, which is easier to understand and improves the operation efficiency. What is more important, DMs can clearly be aware of their following choices according to the corresponding transition information.

The paper contributes to conflict analysis theory by designing a new model and develops a new graph model for managing the supply chain quality of a complex product.

This paper seeks to propose a methodology based on Petri nets to evaluate the reliability parameters of a screening system in paper industry. The effects of failures and courses of action on the system performance have also been investigated.

Generalized stochastic Petri nets (GSPN), a class of Petri nets, has been used to model the interactions amongst the active/standby units of the system; and Markovian approach has been used to evaluate the reliability parameters. The data related to equipments' operational behavior were collected, processed and quantified. Using the data, reliability analysis of system in the long run conditions has been carried out. The sensitivity analysis has been performed to study the effect of failure/repair rates of each unit of the system on system performance.

The methodology adopted in this paper provides a better understanding on the behavior of the system through its graphical representation. The reachability graph generated with Petri net model helps to identify the state space evolution of the system.

Reliability analysis of a screening system of the paper industry presented in this paper will help management in deciding upon the maintenance strategy to be adopted with the objective of improving the performance of the system and consequently reducing the operational and maintenance costs.

The purpose of this paper is to examine the potential of applying Petri nets to improve construction material logistics analysis and modelling.

The characteristics of construction logistics are unveiled by analysing the existing practices of logistics management. In views of the dynamic nature of construction logistics problem, a stochastic Petri nets (SPNs) approach is proposed to tackle the time‐evolution property. Using a simulation package called PetriTool™ a simulation model is developed. Finally, a case example is applied to illustrate the way in which SPNs is used for analysing and modelling construction material logistics problems.

The results indicate that the impacts triggered by variations in delivery lead‐time and changes in delivery quantities can be approximated thereby facilitating decision makers to devise a more reliable and optimal materials management plan for construction projects.

The complex routing patterns in demand analysis and materials procurement methods that results in the enlarged supply chains have not been considered in this paper.

The lack of a simple but powerful formalism to analyse and model the decision process under a dynamic environment hinders the implementation of efficient logistics systems in the construction industry. The SPNs model presented in this paper can support planners and managers in making construction logistics management decisions under dynamic environment.

This paper demonstrates that the time‐based SPNs can offer more enriched solutions especially when modelling the time‐evolution behaviours of construction logistics.

The purpose of this paper to present a practical and systematic approach to estimate the availability of a process plant using generalized stochastic Petri nets (GSPNs). The actual live problem at a fluid catalytic cracking unit (FCCU) of a refinery is used to demonstrate this approach.

A majority of models used for estimation of availability of a complex system are based on the assumptions that the failure of the system is associated with only a few states, and the system does not face different operating conditions, repair actions and common-cause failures. In reality, this is often not the case. Therefore, it is necessary to construct more sophisticated models without such assumptions. In this paper, an attempt has been made to model interaction of component failures, partial failures of components and common-cause failures.

The superiority of this approach over other modeling approaches such as fault tree and Markov analysis is demonstrated. The proposed GSPN is a promising tool that can be conveniently used to model and analyze any complex systems.

GSPN was used to model the reactor-regenerator section of FCCU, which is quite a large system, which shows the strength of modeling capability. The use of Petri nets (PNs) for modeling complex systems for the purpose of availability assessment is demonstrated in this paper. Sensitivity analysis was also carried out for various subsystem/components.

No similar work has been conducted for FCCU using GSPN as per literature incorporating different operating conditions and common-cause failures. The understanding and usage of PNs require a steep learning curve for the practitioners, and this paper provides an approach to estimate availability measures for the complex system.

This paper aims to deal with the problems of failure dependence and common cause failure (CCF) that arise in reliability analysis of complex systems.

Firstly, a dynamic fault tree (DFT) is used to capture the dynamic failure behaviours and converted into an equivalent generalized stochastic petri net (GSPN) for quantitative analysis. Secondly, an efficient decomposition and aggregation (EDA) theory is combined with GSPN to deal with the CCF problem, which exists in redundant systems. Finally, Birnbaum importance measure (BIM) is calculated based on the EDA approach and GSPN model, and it is used to take decisions for system improvement and fault diagnosis.

In this paper, a new reliability evaluation method for dynamic systems subject to CCF is presented based on the DFT analysis and the GSPN model. The GSPN model is easy to capture dynamic failure behaviours of complex systems, and the movement of tokens in the GSPN model represent the changes in the state of the systems. The proposed method takes advantage of the GSPN model and incorporates the EDA method into the GSPN, which simplifies the reliability analysis process. Meanwhile, simulation results under different conditions show that CCF has made a considerable impact on reliability analysis for complex systems, which indicates that the CCF should not be ignored in reliability analysis.

The proposed method combines the EDA theory with the GSPN model to improve the efficiency of the reliability analysis.

The paper aims to develop a new methodology for reliability modelling able to use the data collected in an FMECA analysis to simulate the reliability behaviour of a complex system and the effects of different maintenance policies.

A new methodology has been developed, that combines the FMECA analysis with the Petri nets, oriented graphs able to simulate the behaviour of complex systems with concurrent events. This approach was tested on a case study in API oil refinery in Falconara Marittima (AN, Italy).

By using data collected during an FMECA analysis as input for a Petri net, it is possible to obtain important reliability parameters that describe the behaviour of the system, such as MTTR, MTBF, availability of the plant/machine. It is possible to perform what‐if analysis observing how the reliability parameters change depending on changes in preventive maintenance policies.

In the case study proposed it was not necessary to simulate on condition maintenance. Even if the approach here described is able to do that, it would be interesting to evaluate the methodology presented also in other case studies. The approach described can be used as a starting point to develop techniques of optimization of preventive and on condition maintenance.

The application of the methodology of a case study demonstrated its practical effectiveness, obtaining data very close to experimental ones. This approach is able to provide useful data that help maintenance managers in scheduling maintenance activities and assessing their effect on the behaviour of the whole system.

The methodology presented shows a new solution for reliability modelling of complex industrial systems and represents the first combination of Petri nets with FMECA technique.

End‐of‐life (EOL) products have become a major environmental issue among countries and manufacturers. This is due to the growing number of EOL products and their hazardous contents. Many collection strategies and pilot projects have been conducted to manage EOL products, especially Waste Electrical and Electronics equipments (WEEEs). However, as characteristics of a population are different to one another, a customized collection strategy is required. The purpose of this paper is to find an effective collection strategy which considers cost and environmental impact simultaneously.

This paper presents an integrated collection strategy which combines a Fuzzy Colored Petri Net forecasting method and collection network model to collect EOL products. Colored Petri Net is used in modeling the integrated collection strategy. To test the collection strategy, a case study on mobile phone collection in Australia is presented.

The integrated collection strategy developed in this paper finds that by providing demographic data and historical sales of a relevant product in a certain location, the best strategy to collect EOL products in that location can be determined. This paper finds that the best strategy that suits one location might be different to other locations.

This paper presents a model which provides a customized collection strategy that follows the characteristics of a population. This strategy allows government organizations or manufacturers to simulate the strategies to collect EOL products in different locations.