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Looks at the risk/return characteristics of real estate on the basis of both appraisal and market values. Broadens this analysis to include inflation. Makes a series of observations about the role of real estate in efficient investment portfolios. Concludes that as long as direct real estate is valued at appraisal value it is unmistakably a solid inflation hedge.

Business process models, while primarily intended for process documentation, communication, and improvement, are often also used as input for developing process-oriented software systems (Ouyang et al., 2009). Ensuring correctness, handling complexity, and improving reusability and maintainability of business process models are important for all these goals. The purpose of this paper is to propose an aspect-oriented business process modeling and correctness controlling method based on Petri nets to satisfy these goals.

The aspect-oriented paradigm provides a proper mechanism to modularization, and thus reduces the complexity of models, and also improves reusability and maintainability. However, weaving aspects into base processes may bring in mistakes or errors. To ensure correctness of modeling, this paper presents a formal approach to modeling aspect-oriented business processes and a method to ensure modeling correctness. Petri net is used as the process modeling language and its analysis techniques are applied to analyze the correctness of modeling. Two types of correctness, specifically, aspect-aspect correctness and base-aspect correctness are analyzed. A real banking process model is studied systematically in the case study to evaluate the approach and the performance assessments are conducted to show the cost and effect of the approach.

Designing aspect-oriented business process models help organizations reusing the model elements to reduce redundancy of their model repository, improving their maintainability, and supporting them to adapt to the changes of business requirements with flexible modeling. It is important to stress that the correctness of business process modeling is important in ensuring the quality of the models, especially in the safety-critical business domains, such as financial business domain.

In this paper, separation of concerns is used to separate the cross-cutting activities and core activities in accordance with the different functions of these activities, and an approach to modeling aspect-oriented business processes is proposed. First, the cross-cutting activities are encapsulated as aspects, while core business activities are modeled as base processes. Then, according to the correctness requirements of business process models, based on the weaving mechanisms of aspect-oriented approach, weaving correctness is defined. Weaving correctness controlling methods between multi-aspects and between aspects and base processes are designed. Errors or mistakes of aspect-oriented business process modeling are prevented during the procedure of modeling to ensure error-free business process modeling.

Deadlock is a rather undesirable phenomenon and must be well solved in flexible manufacturing systems (FMS). This paper aims to propose a general iterative deadlock control method for a class of generalized Petri nets (GPN), namely, G-systems, which can model an FMS with assembly and disassembly operations of multiple resource acquisition. When given an uncontrolled G-system prone to deadlocks, the work focuses on the synthesis of a near-optimal, non-blocking supervisor based on reachability graph (RG) analysis.

The concept of a global idle place (GIP) for an original uncontrolled G-system is presented. To simplify the RG computation of an uncontrolled G-system, a GIP is added temporarily to the net model during monitor computation steps. Starting with one token and then by gradually increasing the number of tokens in the GIP at each iteration step, the related net system is obtained. The minimal-covered-set of all bad markings of the related net system suffering from deadlock can be identified and then removed by additional monitors through an established place-invariant control method. Consequently, all related systems are live, and the GIP is finally removed when the non-blockingness of the controlled system is achieved. Meanwhile, the redundancy of monitors is also checked.

A typical G-system example is provided to demonstrate the applicability and effectiveness of the proposed method. Experiments show that the proposed method is easy to use and provides very high behavioral permissiveness for G-system. Generally, it can achieve an optimal or a near-optimal solution of the non-blocking supervisor.

In this work, the concept of GIP for G-systems is presented for synthesis non-blocking supervisors based on RG analysis. By using GIP, an effective deadlock control method is proposed. Generally, the proposed method can achieve an optimal or a near-optimal, non-blocking supervisor for an uncontrolled G-system prone to deadlocks.

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.

The purpose of this paper is to demonstrate that process mining techniques can help to discover process models from event logs, using conventional high-level process modeling languages, such as Business Process Model and Notation (BPMN), leveraging their representational bias.

The integrated discovery approach presented in this work is aimed to mine: control, data and resource perspectives within one process diagram, and, if possible, construct a hierarchy of subprocesses improving the model readability. The proposed approach is defined as a sequence of steps, performed to discover a model, containing various perspectives and presenting a holistic view of a process. This approach was implemented within an open-source process mining framework called ProM and proved its applicability for the analysis of real-life event logs.

This paper shows that the proposed integrated approach can be applied to real-life event logs of information systems from different domains. The multi-perspective process diagrams obtained within the approach are of good quality and better than models discovered using a technique that does not consider hierarchy. Moreover, due to the decomposition methods applied, the proposed approach can deal with large event logs, which cannot be handled by methods that do not use decomposition.

The paper consolidates various process mining techniques, which were never integrated before and presents a novel approach for the discovery of multi-perspective hierarchical BPMN models. This approach bridges the gap between well-known process mining techniques and a wide range of BPMN-complaint tools.

The purpose of this paper is to demonstrate the use of Petri nets in a job shop setup for the improvement in the utilization of machines.

The study discusses concepts such as reachable state, token and matrix equations set, and demonstrates the improvements in machines’ utilization in a job shop. It makes use of algorithms to generate reachable markings to obtain utilization. The study not only describes the application of theory, but also extends the body of knowledge on Petri nets and job shops.

In this study, machines’ utilization has been studied in a job shop with six machines and eight products. The study finds that substantial utilization improvement in job shop set up can be obtained through the application of Petri nets. The study also exposes that Petri nets are mostly used for machines, jobs and tools scheduling problems, but its use in machines’ utilization is neglected. The framework and application presented here along with generalizable findings, is the first to report about machine utilization improvement in job shop manufacturing environment.

Job shops are characterized by high unit production cost, low investments, low volume and high variety, complex flows, flexible and skilled work force, general purpose machines, high material handling; resulting in poor utilization of machines. Therefore, the findings of this study can help in reducing such costs through better machine utilization. This can help in increasing the competitiveness of the companies.

The contribution of study lies in investigating and improving stage wise utilization in a job shop setup. It has never been reported before.

The purpose of this paper is to present a new methodology, named vague lambda‐tau, used for reliability analysis of a combustion system, which could be used for managerial decision making and future system maintenance strategy.

This paper involves the qualitative and quantitative analysis of a combustion system. In qualitative analysis, the Petri net model is obtained from its equivalent fault tree and in quantitative analysis, the reliability parameters are evaluated using vague lambda‐tau methodology. Further, a decision support system based on vague sets is developed to overcome the limitations of traditional risk analysis.

The reliability parameters (such as expected number of failures, mean time between failures, availability, and reliability) of the compressor system are evaluated. The proposed, vague sets‐based reliability analysis and risk analysis not only overcome the limitations associated with traditional approaches but also integrate the confidence level of domain experts and expert's experience, in a more flexible and realistic manner.

Instead of fuzzy sets, this paper used vague sets for reliability analysis with Petri net modelling because in real life problems, there may be hesitation regarding the belongingness of an object to a set or not. In fuzzy set theory, there is no means to incorporate such type of hesitation.

The aim of this work is to propose a logical‐based approach for deriving the critical scenarii from a differential predicate transition Petri nets model and proof tree, to evaluate diagnosis and safety of hybrid systems.

The new approach integrates the Petri net model and linear logic. The design process is twofold: first, it considers the specific translation from Petri net to linear logic, and, second, it studies the relationship between the order of the rule application in the sequent proof and the processes associated with the same reachability problem.

With linear logic and Petri net it is proved that, from a canonical proof tree, a partial order can be derived (based on causality point of view) of transition firing and focusing the search of the parts of the model that are interesting for safety analysis. Each scenario is given by a partial order between the events necessary for the occurrence of the feared event.

This paper deals with safety in design of hybrid systems. For this purpose, it is important to characterize feared behaviours (which are critical) in the early design stage.

The purpose of this paper is to design a consortium-blockchain based framework for cross-organizational business process mining complying with privacy requirements.

Business process modeling in a cross-organizational setting is complicated due to privacy concerns. The process mining in this situation occurs through trusted third parties (TTPs). It uses a special class of Petri-nets called workflow nets (WF-nets) to represent the formal specifications of event logs in a blockchain-enabled cross-organization.

Using a smart contract algorithm, the proposed framework discovers the organization-specific business process models (BPM) without a TTP. The discovered BPMs are formally represented using WF-nets with a message factor to support the authors’ claim. Finally, the applicability and suitability of the proposed framework is demonstrated using a case study of multimodal transportation.

The proposed framework complies with privacy requirements. It shows how to represent the formal specifications of event logs in a blockchain using a special class of Petri-nets called WF-nets. It also presents a smart contract algorithm to discover organization-specific business process models (BPM) without a TTP.

The purpose of this paper is twofold. In view of the importance of process platform‐based production configuration (PPbPC) in sustaining product family production efficiency, it is to study the underlying logic for configuring production processes for a product family based on a process platform. Second, it is to apply the Petri nets (PNs) techniques to model PPbPC, in attempting to shed light on the underlying logic.

The authors first identify the fundamental issues in PPbPC, including variety handling, process variation accommodation, configuration at different abstraction levels, and constraint satisfaction. To accommodate the corresponding modelling difficulties, the authors develop a formalism of hierarchical colored timed PNs (HCTPNs) based on the principles of hierarchical PNs, timed PNs, and colored PNs. In the formalism, three types of nets together with a system of HCTPNs are defined to address the modelling of PPbPC.

Applying HCTPNs to vibration motors' case has revealed the logic of specifying complete production processes of final products at different levels of abstraction to achieve production configuration. The preliminary results also further demonstrate the feasibility of modelling PPbPC based on HCTPNs.

Traditional approaches to planning production processes for individual products may limit production performance improvement when companies need to timely produce a high variety of customized products. Systematic methods should be developed to plan production processes for product families so as to achieve production efficiency while utilizing the existing manufacturing resources.

By integrating the advantages of existing PN techniques, the HCTPNs formalism is developed to shed light on planning production processes for product families. The resulting production configuration model can facilitate practitioners to achieve production efficiency in producing large numbers of customized products.