Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence

Process discovery techniques return process models that are either formal (precisely describing the possible behaviors) or informal (merely a "picture" not allowing for any form of formal reasoning). Formal models are able to classify traces (i.e., sequences of events) as fitting or non-fitting. Most process mining approaches described in the literature produce such models. This is in stark contrast with the over 25 available commercial process mining tools that only discover informal process models that remain deliberately vague on the precise set of possible traces. There are two main reasons why vendors resort to such models: scalability and simplicity. In this paper, we propose to combine the best of both worlds: discovering hybrid process models that have formal and informal elements. As a proof of concept we present a discovery technique based on hybrid Petri nets. These models allow for formal reasoning, but also reveal information that cannot be captured in mainstream formal models. A novel discovery algorithm returning hybrid Petri nets has been implemented in ProM and has been applied to several real-life event logs. The results clearly demonstrate the advantages of remaining "vague" when there is not enough "evidence" in the data or standard modeling constructs do not "fit". Moreover, the approach is scalable enough to be incorporated in industrial-strength process mining tools.Comment: 25 pages, 12 figure

Modelling Freight Allocation and Transportation Lead-Time

The authors have investigated sustainable environment delivery systems and identified transportation lead-time investigation cases. This research study aimed to increase freight delivery lead-time and minimize distance in transportation. To reach the goal, the paper\u27s authors, after analysis of the hierarchy of quantitative methods and models, proposed the framework for modeling freight allocation and transportation lead-time and delivered a study that includes discrete event simulation. During the simulation, various scenarios have been revised. Following the simulation mentioned above analysis, around 3.8 % of distance could be saved during freight delivery if lead-time for transportation were revised by choosing five days criteria for modeling freight allocation. The savings depend on the number of received orders from different geographic locations

Compositional dependability analysis of dynamic systems with uncertainty

Over the past two decades, research has focused on simplifying dependability analysis by looking at how we can synthesise dependability information from system models automatically. This has led to the field of model-based safety assessment (MBSA), which has attracted a significant amount of interest from industry, academia, and government agencies. Different model-based safety analysis methods, such as Hierarchically Performed Hazard Origin & Propagation Studies (HiP-HOPS), are increasingly applied by industry for dependability analysis of safety-critical systems. Such systems may feature multiple modes of operation where the behaviour of the systems and the interactions between system components can change according to what modes of operation the systems are in.MBSA techniques usually combine different classical safety analysis approaches to allow the analysts to perform safety analyses automatically or semi-automatically. For example, HiP-HOPS is a state-of-the-art MBSA approach which enhances an architectural model of a system with logical failure annotations to allow safety studies such as Fault Tree Analysis (FTA) and Failure Modes and Effects Analysis (FMEA). In this way it shows how the failure of a single component or combinations of failures of different components can lead to system failure. As systems are getting more complex and their behaviour becomes more dynamic, capturing this dynamic behaviour and the many possible interactions between the components is necessary to develop an accurate failure model.One of the ways of modelling this dynamic behaviour is with a state-transition diagram. Introducing a dynamic model compatible with the existing architectural information of systems can provide significant benefits in terms of accurate representation and expressiveness when analysing the dynamic behaviour of modern large-scale and complex safety-critical systems. Thus the first key contribution of this thesis is a methodology to enable MBSA techniques to model dynamic behaviour of systems. This thesis demonstrates the use of this methodology using the HiP-HOPS tool as an example, and thus extends HiP-HOPS with state-transition annotations. This extension allows HiP-HOPS to model more complex dynamic scenarios and perform compositional dynamic dependability analysis of complex systems by generating Pandora temporal fault trees (TFTs). As TFTs capture state, the techniques used for solving classical FTs are not suitable to solve them. They require a state space solution for quantification of probability. This thesis therefore proposes two methodologies based on Petri Nets and Bayesian Networks to provide state space solutions to Pandora TFTs.Uncertainty is another important (yet incomplete) area of MBSA: typical MBSA approaches are not capable of performing quantitative analysis under uncertainty. Therefore, in addition to the above contributions, this thesis proposes a fuzzy set theory based methodology to quantify Pandora temporal fault trees with uncertainty in failure data of components.The proposed methodologies are applied to a case study to demonstrate how they can be used in practice. Finally, the overall contributions of the thesis are evaluated by discussing the results produced and from these conclusions about the potential benefits of the new techniques are drawn

Case-Based Decision Support for Disaster Management

Disasters are characterized by severe disruptions of the society’s functionality and adverse impacts on humans, the environment, and economy that cannot be coped with by society using its own resources. This work presents a decision support method that identifies appropriate measures for protecting the public in the course of a nuclear accident. The method particularly considers the issue of uncertainty in decision-making as well as the structured integration of experience and expert knowledge

Modeling, design and scheduling of computer integrated manufacturing and demanufacturing systems

This doctoral dissertation work aims to provide a discrete-event system-based methodology for design, implementation, and operation of flexible and agile manufacturing and demanufacturing systems. After a review of the current academic and industrial activities in these fields, a Virtual Production Lines (VPLs) design methodology is proposed to facilitate a Manufacturing Execution System integrated with a shop floor system. A case study on a back-end semiconductor line is performed to demonstrate that the proposed methodology is effective to increase system throughput and decrease tardiness. An adaptive algorithm is proposed to deal with the machine failure and maintenance. To minimize the environmental impacts caused by end-of-life or faulty products, this research addresses the fundamental design and implementation issues of an integrated flexible demanufacturing system (IFDS). In virtue of the success of the VPL design and differences between disassembly and assembly, a systematic approach is developed for disassembly line design. This thesis presents a novel disassembly planning and demanufacturing scheduling method for such a system. Case studies on the disassembly of personal computers are performed illustrating how the proposed approaches work

Hierarchy Process Mining from Multi-Source Logs

Nowadays, large-scale business processes is growing rapidly; in this regards process mining is required to discover and enhance business processes in different departments of an organization. A process mining algorithm can generally discover the process model of an organization without considering the detailed process models of the departments, and the relationship among departments. The exchange of messages among departments can produce asynchronous activities among department process models. The event logs from departments can be considered as multi-source logs, which cause difficulties in mining the process model. Discovering process models from multi-source logs is still in the state of the art, therefore this paper proposes a hierarchy high-to-low process mining approach to discover the process model from a complex multi-source and heterogeneous event logs collected from distributed departments. The proposed method involves three steps; i.e. firstly a high level process model is developed; secondly a separate low level process model is discovered from multi-source logs; finally the Petri net refinement operation is used to integrate the discovered process models. The refinement operation replaced the abctract transitions of a high level process model with the corresponding low level process models. Multi-source event logs from several departments of a yarn manufacturing were used in the computational study, and the results showed that the proposed method combined with the modified time-based heuristics miner could discover a correct parallel process business model containing XOR, AND, and OR relations

Maintenance models applied to wind turbines. A comprehensive overview

Producción CientíficaWind power generation has been the fastest-growing energy alternative in recent years, however, it still has to compete with cheaper fossil energy sources. This is one of the motivations to constantly improve the efficiency of wind turbines and develop new Operation and Maintenance (O&M) methodologies. The decisions regarding O&M are based on different types of models, which cover a wide range of scenarios and variables and share the same goal, which is to minimize the Cost of Energy (COE) and maximize the profitability of a wind farm (WF). In this context, this review aims to identify and classify, from a comprehensive perspective, the different types of models used at the strategic, tactical, and operational decision levels of wind turbine maintenance, emphasizing mathematical models (MatMs). The investigation allows the conclusion that even though the evolution of the models and methodologies is ongoing, decision making in all the areas of the wind industry is currently based on artificial intelligence and machine learning models