Process mining technology selection with spherical fuzzy AHP and sensitivity analysis

Modern realities dictate the requirements for effective optimization of production. Modernization of business, economics, production, healthcare and medicine in general is an urgent task of our time. One of the technologies for effective process analysis is process mining. Process mining technology allows you to extract knowledge from an event log, forming a process model based on it. Such tools make it possible to identify ineffective management structures and business processes, malfunctions in software, and incompetence of employees, solving many problems of organizing effective management in various industries. Today there are many software tools that implement process analysis methods: Disco, Apromore, ProM. Celonis Process Mining, UiPath Process Mining, etc. Selecting software for certain tasks is a complex process, which subsequently affects the implementation of the project as a whole. Therefore, it is extremely important to comprehensively analyze all the capabilities and limitations of existing process mining tools. The following criteria were selected: cost of the framework; availability of source code; programming language; the ability to build a decision tree based on the process; technical support; technical documentation; developed community of developers; the ability to create and add plugins; experience with the framework. Healthcare was chosen as the target application area. According to the results of the analysis, it was concluded that the use of the ProM framework is effective for solving problems in the field of healthcare.

Autonomous vehicles (AVs) are the newest products in the intelligent transportation system that can move around with minimal human intervention. These products continue their path with all kinds of sensors with different parts. Effective use of these technologies in the logistics industry can create a competitive advantage. Nowadays, there are many AVs, some of which are superior to others in terms of build quality, variety of features, and design. Choosing an efficient, optimal, and reliable vehicle is one of the most important challenges in logistics planning. Therefore, choosing an AV based on a series of criteria can be considered a multi-criteria decision-making (MCDM) problem. Due to the complication of decision-making issues, criteria are usually not independent of each other and there are relationships between them. Therefore, this study develop an extended MCDM framework based on Choquet integral (CI) under group decision-making with a Spherical fuzzy set (SFS) for assessing logistics AVs. The CI technique is expanded with SFS to increase the power of CI. Furthermore, the combination of CI with SFS leads to greater freedom for decision makers to express opinions and use three independent membership functions. Accordingly, the interactions between the criteria are considered and the skepticism and uncertainty present during the decision are controlled. The proposed approach is implemented in selecting the best AVs in the logistics industry, and the results are compared with Pythagorean fuzzy CI and Intuitionistic fuzzy CI. Moreover, sensitivity analysis is done by changing the weights and creating different scenarios to confirm and check the robustness of the proposed approach results. The results indicate the suggested approach's efficiency and the ranking's stability in different scenarios.

A high level of competitiveness of a company is one of the key factors that guarantees stable long-term economic development and a stable position of the enterprise in the market. Competition is a trigger for activating innovative processes and introducing modern information technologies into the enterprise infrastructure. The lack of control and regulation system, the low level of automation of processes, their inconsistency with strategic goals and duplication significantly reduce the overall level of competitiveness of the enterprise and negatively affects economic profit. As a tool that can increase a company’s competitive position in the market, the work explores the digital technology of in-depth analysis, that is Process Mining. This article aims to explore how businesses gain a competitive edge by using business process mining technology. It also examines the current trends in implementing process analytics across different industries. Process Mining technology is the focus of the study. The subject is the impact of business process mining technology on the competitiveness of a company. The study used theoretical and empirical scientific approaches, methods of statistical analysis, and systems analysis techniques. By analyzing competitive advantages, a correlation between the implementation of process analytics and the company’s increased efficiency was established. The most popular areas of application of Process Mining are procurement, customer service and information technology. The leaders in the number of successful implementations of business process analysis technology are the financial services, telecommunications, and oil and gas industries.

The article discusses the methodological basis and practice of using Process mining technology to improve the efficiency of business processes of Russian companies. The challenges of the digital economy and prerequisites for the development of Process mining methods are identified. The directions of transformation of approaches to process management in the conditions of application of Process mining methods are established. The main synergy effects of internal audit and Process mining are identified. The stages of implementation of the Process mining project and quality criteria for corporate data logs are considered. An algorithm of actions is proposed to prepare for the introduction of Process mining technology in Russian companies; to obtain the identified synergy effects by internal audit.There is no conflict of interests.

Process mining technology aims to automatically generate process models by analyzing events, thereby assisting the design and redesign of process models. Although many process mining methods have appeared, they all have deficiencies. These methods focus on mining from the behavioral aspects described by the log, while ignoring the structural nature of the process model itself. The complexity of the process describes the simplicity and ease of understanding of the process. Higher process complexity affects the readability of the process. Genetic programming has strong robustness. Its individual representation based on tree structure can describe the special structure of the process. The introduction of process complexity fitness enables it to consider the complexity of the process model itself while mining log behavior, so as to achieve nonlinear mining of complex processes. This paper analyzes the process mining based on genetic programming and proposes process individuals based on tree structure. By realizing the combination of process complexity measurement and process mining technology, noncomplex process mining can be realized. In this paper, the process complexity is combined with the process mining algorithm based on genetic programming, and a measure of process structural complexity is proposed, which is converted into complexity fitness and introduced into the fitness function of genetic programming, to realize the use of genetic programming. The research results show that the improved new adaptive genetic programming robust scheduling algorithm provides a new idea and method for the evaluation of college sports under different sports conditions. This makes the college sports evaluation management system more intelligent and improves the rational allocation of physical education teaching.

Due to climate change, fossil fuel reduction, growing energy usage, and ecological impacts, the need for renewable energy sources is rising quickly. A large amount of energy comes from the ocean through the tide; renewable energy must be exploited. One important energy source is wave energy converters (WECs), which enhance efficiency and grow energy. This study presented a case study from three layers of the Red Sea in Egypt for selecting optimal wave energy location (WEL), wave energy technology (WET), and WEC. The spherical fuzzy set (SFS) framework deals with uncertainty and vague information in this selection process. The SFS is integrated with multi-criteria decision-making (MCDM) to deal with various criteria in each layer. This study uses two MCDM methods under the SFS environment: The Stepwise Weighted Assessment Ratio Analysis (SWARA) method and the Weighted Euclidean Distance Based Approach (WEDBA) method. The SF-SWARA method is used to compute the criteria weights in each layer. Then, the SF-WEDBA method ranks the alternatives in each layer. The main results in each layer show that the accessibility criterion, Economic factors, and energy production criterion have the highest weight in the three layers. Sensitivity analysis and comparative analysis are conducted in each layer.

Work-related disorders account for a significant part of total healthcare expenditure. Traditionally muscle-skeletal disorders were predominant as source of work absenteeism but in the last years work activity-related disorders have increased remarkably. Too little activity at work, sedentarism, or too much work activity leads to stress. The individualized behavioural analysis of employees can support ergonomy experts in optimizing work environments. However, in order to understand which aspects of the working environment need to be improved a clear understanding on the behaviours and working conditions of employees is needed. This requires analyzing and summarizing the multi-dimensional set of variables that describes a work environment. Process Mining Technologies can offer a human understandable view of what is actually occurring in workplaces in an individualized way. In this paper, we present a proof of concept of how Process Mining technologies can be used for discovering employees work flows in order to support the ergonomy experts in the selection of more accurate interventions for improving occupational health.

Cosine similarity and information measures of [formula omitted] spherical fuzzy sets: Application in selecting migration destination

Business processes in healthcare are complex and multidisciplinary, involving various professional profiles and different healthcare structures, and each medical treatment may require distinct clinical pathways. Process mining can assist in discovering trajectories, verifying compliance, and enabling an understanding of the involvement of different organizational aspects. The main goal of this study is to provide a comprehensive overview of the application of process mining in healthcare. For this, a tertiary review was conducted, gathering 18 secondary reviews that addressed different aspects, such as the objectives of process mining in healthcare, types of activities and perspectives, available resources, primary medical specialties, types of medical processes, and limitations and challenges. The study reveals that process discovery is the most common activity, while the control flow was the most used perspective. The Heuristics Miner and Fuzzy Miner algorithms were the most relevant, and oncology was the medical specialty in which process mining was most used. Process mining has proven to be an effective tool for analyzing healthcare workflows, improving understanding of clinical guidelines and protocols, and supporting decision-making. However, it is necessary to deal with noisy or missing data and establish visualization mechanisms that ensure clarity in data presentation.

Aggregation operators are widely applied to accumulate the vague and uncertain information in these days. Hamy mean (HM) operators play a vital role to accumulate the information. HM operators give us a more general and stretchy approach to develop the connections between the arguments. Spherical fuzzy sets (SpFSs), the further extension of picture fuzzy sets (PcFSs) that handle the data in which square sum of membership degree (MD), non-membership degree (NMD) and neutral degree (ND) always lie between closed interval [0, 1]. In the present article, we modify the HM operators like spherical fuzzy HM (SpFHM) operator and weighted spherical fuzzy HM (WSpFHM) operator to accumulate the spherical fuzzy (SpF) information. Moreover, various properties and some particular cases of SpFHM and the WSpFHM operators are discussed in details. Also, to compare the results obtained from the HM operators a score function is developed. Based on WSpFHM operator and score function, a model for multiple criteria decision-making (MCDM) is established to resolve the MCDM problem. To check the significance and robustness of the result, a comparative analysis and sensitivity analysis is also performed.

Process mining facilitates the discovery, conformance, and enhancement of business processes using event logs. However, incomplete event logs and the complexities of concurrent activities present significant challenges in achieving accurate process models that fulfill the completeness condition required in process mining. This paper introduces a Timed Genetic-Inductive Process Mining (TGIPM) algorithm, a novel approach that integrates the strengths of Timed Genetic Process Mining (TGPM) and Inductive Mining (IM). TGPM extends traditional Genetic Process Mining (GPM) by incorporating time-based analysis, while the IM is widely recognized for producing sound and precise process models. For the first time, these two algorithms are combined into a unified framework to address both missing activity recovery and structural correctness in process discovery. This study evaluates two scenarios: a sequential approach, in which TGPM and IM are executed independently and sequentially, and the TGIPM approach, where both algorithms are integrated into a unified framework. Experimental results using real-world event logs from a health service in Indonesia demonstrate that TGIPM achieves higher fitness, precision, and generalization compared to the sequential approach, while slightly compromising simplicity. Moreover, the TGIPM algorithm exhibits lower computational cost and more effectively captures parallelism, making it particularly suitable for large and incomplete datasets. This research underscores the potential of TGIPM to enhance process mining outcomes, offering a robust framework for accurate and efficient process discovery while driving process innovation across industries.

With the broad use of business process management technology, there are more and more business process models. Since the ability of different modelers is different, the quality of these models varies. A question arises here is that, can we refactor these models to improve the quality as practised in software engineering? Business process modeling can be regarded as declarative programming, and business process models can be used to drive the process aware information systems, which are generally developed with model driven architecture, so business process models are crucial for the efficiency of process aware information systems. In this paper, we propose a novel approach on how to systematically refactor business process models with parallel structures for sequence structures for the first time. More specifically, we analyze the real causal relations between business tasks based on data operation dependency analysis, and refactor business process models with process mining technology. After comprehensive model refactoring, parallel execution of business tasks can be maximized, so the efficiency of business processing can be improved, that is, the quality of business process models can be improved. Analysis and experiments show that our approach is effective and efficient.

The use of Unmanned Aerial Vehicle (UAV) platforms has been increasing day by day and it has become an important technology. In this study, how the engines should be selected in the design of a rotary wing UAV system is considered a multi-criteria decision-making (MCDM) problem. This MCDM problem has not yet been encountered in the literature. Therefore, a hybrid MCDM approach based on the fuzzy Best Worst Method (BWM) and Multi Attributive Ideal-Real Comparative Analysis (MAIRCA) is proposed to solve this problem. In the proposed approach, the decision makers determine 6 criteria (KV value, thrust, weight, efficiency, battery, electronic speed controller (ESC)) and 6 different engine (A1, A2, A3, A4, A5, A6) alternatives. The fuzzy BWM was used to calculate the weights of criteria, while the MAIRCA was used for the selection of alternatives. According to the results obtained, the three most effective criteria were thrust, KV value, and weight, respectively. The three best engine options were found as A3, A1, and A6. Moreover, sensitivity analysis was performed to observe the change in the ranking of alternatives according to different weights of criteria. MABAC, MARCOS, and COPRAS methods were used to compare the alternative rankings found with the MAIRCA.

The main priority of this article is to develop a tool for numerical forecasting of the life cycle of information resources based on actual life cycle data in the form of event logs of the resource history. To solve this problem, we used the GERT network device. To build a GERT network describes the life cycle model of an information resource, we were used methods of intellectual analysis of processes (Process Mining) implemented with the ProM Framework. In the course of the work, an analysis of the life cycle of information resources of the Scientific electronic online library was carried out. The work considers the process of extracting data used for modeling the GERT network, as well as the process of identifying the law of distribution of the probability density of the duration of the life cycle stages. The model of the GERT network is constructed and the law of distribution of the probability density of the duration of the life cycle of an information resource was described. The obtained result confirms the applicability of Process Mining technology to probabilistic analysis and forecasting of the life cycle of information resources.

Explosive Ordnance Risk Education Messages (EORE) is a multi-criteria decision-making problem (MCDM) based on three steps, namely, the identification of distinct evolution criteria, the significance criteria, and the variation of data. Because it makes use of a more sophisticated classification technique, the group decision method (GDM) based on weighted arithmetic mean (AM) to prioritize (EORE) messages is the proper approach. In contrast to GDM, which explicitly weights each criterion, GDM implicitly weights each alternative's criterion values. With the help of the new hybrid method weighting technique, we can overcome this theoretical difficulty by providing explicit weights for criteria generated with zero inconsistencies and combined with the new distance-based weighting method. SFS (spherical fuzzy set) is used in hybrid methods, although it can only be used to solve the ambiguity associated with the theoretical concerns outlined above.