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Abstract

Service processes, for example in transportation, telecommunications or the health sector, are the backbone of today's economies. Conceptual models of service processes enable operational analysis that supports, e.g., resource provisioning or delay prediction. In the presence of event logs containing recorded traces of process execution, such operational models can be mined automatically.In this work, we target the analysis of resource-driven, scheduled processes based on event logs. We focus on processes for which there exists a pre-defined assignment of activity instances to resources that execute activities. Specifically, we approach the questions of conformance checking (how to assess the conformance of the schedule and the actual process execution) and performance improvement (how to improve the operational process performance). The first question is addressed based on a queueing network for both the schedule and the actual process execution. Based on these models, we detect operational deviations and then apply statistical inference and similarity measures to validate the scheduling assumptions, thereby identifying root-causes for these deviations. These results are the starting point for our technique to improve the operational performance. It suggests adaptations of the scheduling policy of the service process to decrease the tardiness (non-punctuality) and lower the flow time. We demonstrate the value of our approach based on a real-world dataset comprising clinical pathways of an outpatient clinic that have been recorded by a real-time location system (RTLS). Our results indicate that the presented technique enables localization of operational bottlenecks along with their root-causes, while our improvement technique yields a decrease in median tardiness and flow time by more than 20%.

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... AND/OR/XOR joins and splits) are suitable. The reviewed papers included BPMN [49,57,77,78], HeuristicNet [12,26], Inductive Visual Model [3,45,59], Petri Net [26,59,68,77,78], Fork/Join Network [75], and declarative process models [60,60]. ...
... Lastly, to evaluate the conformance of cancer patient pathways, Senderovich et al. [75] used an approach based on interval algebra and Markovian probabilities to discover the structure of the model and characterise its dynamics to build a Fork-Join network that describes the process. ...
... Some studies that did not mention the use of an auxiliary method involved shorter pathways, such as acute illness pathways [15,45,87,34,73,74], emergency department pathways [9,10,63], and pathways of medicine alternation [90]. Other studies comprised department/resource [6,26,75] or organisational pathways [5,47,57]. Lastly, for some studies, the pathway mining method involved some type of filtering or clustering, e.g. ...
Preprint
The sequence of visits and procedures performed by the patient in the health system, also known as the patient's pathway or trajectory, can reveal important information about the clinical treatment adopted and the health service provided. The rise of electronic health data availability made it possible to assess the pathways of a large number of patients. Nevertheless, some challenges also arose concerning how to synthesize these pathways and how to mine them from the data, fostering a new field of research. The objective of this review is to survey this new field of research, highlighting representation models, mining techniques, methods of analysis, and examples of case studies.
... Delays refer to cases where the completion time is later than the planned completion time. Senderovich et al. [38] analyze a process log from the perspective of operational deviations resulting in tardiness (delays) from a process duration perspective. Park et al. [30] analyze delays in a make-to-order manufacturing firm. ...
... Senderovich et al. [36,37] propose a method to discover characteristics of "work queues" from event logs at the level of an entire process or of individ-ual activities. In [38], the authors target the analysis of resource-driven processes based on event logs. In particular, they focus on processes for which there exists a predefined assignment of activity instances to resources that execute activities. ...
... A business seeking to conduct data-driven performance analysis, should first select the type of technique. Descriptive analysis will show the current state and [34], [23], [5], [6], [32], [3] Process Duration -- [41] Fragment Duration Activity Start and End Time [7], [24], [23], [17], [5] Activity Duration -- [19], [31], [29], [34], [7], [24] Waiting Duration [30], [38], [36], [ [21], [22] Framework to extract process characteristics from event logs discriminating between positive and negative cases [5], [6] Comparing waiting duration of similar process in different installations [11], [15] Collaborative Processes [26] Evolution of performance over time [40] Framework for performance-related analysis with information-poor event logs Type Domain ...
Article
Process mining has gained traction over the past decade and an impressive body of research has resulted in the introduction of a variety of process mining approaches measuring process performance. Having this set of techniques available, organizations might find it difficult to identify which approach is best suited considering context, performance indicator, and data availability. In light of this challenge, this paper aims at introducing a framework for categorizing and selecting performance analysis approaches based on existing research. We start from a systematic literature review for identifying the existing works discussing how to measure process performance based on information retrieved from event logs. Then, the proposed framework is built starting from the information retrieved from these studies taking into consideration different aspects of performance analysis.
... Delays refer to cases where the completion time is later than the planned completion time. Senderovich et al. [40] analyze a process log from the perspective of operational deviations resulting in tardiness (delays) from a process duration perspective. Park et al. [32] analyze delays in a make-to-order manufacturing firm. ...
... Senderovich et al. [38,39] propose a method to discover characteristics of "work queues" from event logs at the level of an entire process or of individual activities. In [40], the authors target the analysis of resource-driven processes based on event logs. In particular, they focus on processes for which there exists a predefined assignment of activity instances to resources that execute activities. ...
... In the remaining studies, the authors developed their own applications. [36], [25], [5], [6], [34], [1] Process Duration -- [43] Fragment Duration Activity Start and End Time [7], [26], [25], [19], [5] Activity Duration -- [21], [33], [31], [36], [7], [26] Waiting Duration [32], [40], [38], [39] Delay Duration Internal Quality . ...
Chapter
Process mining has gained traction over the past decade and an impressive body of research has resulted in the introduction of a variety of process mining approaches measuring process performance. Having this set of techniques available, organizations might find it difficult to identify which approach is best suited considering context, performance indicator, and data availability. In light of this challenge, this paper aims at introducing a framework for categorizing and selecting performance analysis approaches based on existing research. We start from a systematic literature review for identifying the existing works discussing how to measure process performance based on information retrieved from event logs. Then, the proposed framework is built starting from the information retrieved from these studies taking into consideration different aspects of performance analysis.
... Process discovery plays a key role in descriptive, predictive, and prescriptive data-driven process analyses [1]. From depicting the underlying processes [2], [3], through providing a basis for predictive monitoring [4] and conformance checking [5], to evidence-based resource scheduling [6], discovered models serve as the workhorse of process-aware data analytics. ...
... We used truncated exponential sampling [21] to perturb the time-of-day attribute, assuming random case arrivals with exponentially distributed and independent inter-arrival times. 6 Durations and resources were sampled uniformly for each event from a given range and a closed list, respectively. Log1, represents a rich log for which we sampled distinct time ranges, resource allocations (with 10% chance for overlapping resources, e.g., activities A and B are performed by 'R1' and 'R2' with 0.9 probability and by the other resource with a probability of 0.1, respectively), and activity executions with varying (and potentially overlapping) durations for each process instance. ...
... The synthetic logs are publicly available at https://github.com/dafna-s/ Inductive-Context-aware-Process-Discovery/tree/master/Synthetic log6 We assume Poisson arrivals, which is a common arrival scheme in the literature[22]. A truncated distribution is a conditional distribution that restricts the domain of a given distribution. ...
... In [53], the question of how to compare actual process execution with the scheduled execution in terms of performance was addressed, using queueing networks. Queueing networks describe inter-case timing dependencies, while traces in stochastic languages (used in this paper) are in princple independent. ...
... Queueing networks describe inter-case timing dependencies, while traces in stochastic languages (used in this paper) are in princple independent. Furthermore, the queueing networks of [53] do not support choice, which is a key concept of stochastic behaviour. Furthermore, where [53] uses Markov chains to compare models of behaviour, we directly compare the behaviour of logs/models with one another without using any abstraction, and we allow approximate matching using Levenshtein. ...
... Furthermore, the queueing networks of [53] do not support choice, which is a key concept of stochastic behaviour. Furthermore, where [53] uses Markov chains to compare models of behaviour, we directly compare the behaviour of logs/models with one another without using any abstraction, and we allow approximate matching using Levenshtein. It would be interesting to combine the concepts of [53] and this paper. ...
Article
Initially, process mining focused on discovering process models from event data, but in recent years the use and importance of conformance checking has increased. Conformance checking aims to uncover differences between a process model and an event log. Many conformance checking techniques and measures have been proposed. Typically, these take into account the frequencies of traces in the event log, but do not consider the probabilities of these traces in the model. This asymmetry leads to various complications. Therefore, we define conformance for stochastic process models taking into account frequencies and routing probabilities. We use the earth movers’ distance between stochastic languages representing models and logs as an intuitive conformance notion. In this paper, we show that this form of stochastic conformance checking enables detailed diagnostics projected on both model and log. To pinpoint differences and relate these to specific model elements, we extend the so-called ‘reallocation matrix’ to consider paths. The approach has been implemented in ProM and our evaluations show that stochastic conformance checking is possible in real-life settings.
... Performance analysis is an important task when improving business processes [1]. Accurate and robust estimation of performance measures, such as cycle times and queuelengths, is crucial for locating bottlenecks and understanding system dynamics [2]. In heavily loaded processes where cases compete over scarce resources, the use of queue mining, which is the discovery of the queueing perspective in process mining [3], plays a key role in performance analytics [4]. ...
... Our method was implemented in Python and is publicly available. 2 We used scikit-learn 3 for supervised learning. ...
... Queueing effects may be considered for time prediction in generative methods by choosing an appropriate target formalisms, such as Generalized Stochastic Petri nets (GSPNs) [29] or queueing networks [30]. Discovery of these models adopts traditional control-flow discovery for the structure and extends it with estimations of model parameters [2], such as arrival rates or resource capacities. ...
... Process discovery plays a key role in descriptive, predictive, and prescriptive data-driven process analyses [1]. From depicting the underlying processes [2,3], through providing a basis for predictive monitoring [4] and conformance checking [5], to evidence-based resource scheduling [6], discovered models serve as the workhorse of process-aware data analytics. ...
... Prof. X, being a research traditionalist, scribbles his research notes on paper (activity A) and sends it to his assistant (activity B), who types the notes using a computer (activity C). When Wolverine heads home, Prof. X must perform voice-to-text typing on his own (activity D). 6 3 A X 10:00 e 7 3 B X 10:00 e 8 3 C W 10:10 e 9 4 A X 9:00 e 10 4 B X 9:00 e 11 4 C W 9:05 ...
... 6 5 The synthetic logs are publicly available at https://github.com/dafna-s/ Inductive-Context-aware-Process-Discovery/tree/master/Synthetic_log 6 We assume a Poisson arrival process, a common arrival scheme in the literature [27]. A truncated distribution is a conditional distribution that restricts the domain of a given distribution. ...
Article
Discovery plays a key role in data-driven analysis of business processes. The vast majority of contemporary discovery algorithms aims at the identification of control-flow constructs. The increase in data richness, however, enables discovery that incorporates the context of process execution beyond the control-flow perspective. A “control-flow first” approach, where context data serves for refinement and annotation, is limited and fails to detect fundamental changes in the control-flow that depend on context data. In this work, we thus propose a novel approach for combining the control-flow and data perspectives under a single roof by extending inductive process discovery. Our approach provides criteria under which context data, handled through unsupervised learning, take priority over control-flow in guiding process discovery. The resulting model is a process tree, in which some operators carry data semantics instead of control-flow semantics. We show that the proposed approach produces trees that are context consistent, deterministic, complete, and can be explainable without a major quality reduction. We evaluate the approach using synthetic and real-world datasets, showing that the resulting models are superior to state-of-the-art discovery methods in terms of measures based on multi-perspective alignments.
... The operation of business processes can be improved by modelling their performance. Specifically, an accurate assessment of key performance measures, such as sojourn times and remaining times, enables bottleneck analysis and optimised resource planning [2]. ...
... We utilise three data sources: two event logs that stem from two different processes of DayHospital, a large cancer outpatient hospital in the United States, and an event log that comes from the Rambam hospital, a general hospital in Haifa, Israel. 2 The first dataset, named 'Consult', corresponds to a patient consultation process in DayHospital. This process involves several procedures including blood draw, physical examination, vital signs, and consulting with a health provider. ...
Conference Paper
Analysing performance of business processes is an important vehicle to improve their operation. Specifically, an accurate assessment of sojourn times and remaining times enables bottleneck analysis and resource planning. Recently, methods to create respective performance models from event logs have been proposed. These works are severely limited, though: They either consider control-flow and performance information separately, or rely on an ad-hoc selection of temporal relations between events. In this paper, we introduce the Temporal Network Representation (TNR) of a log, based on Allen’s interval algebra, as a complete temporal representation of a log, which enables simultaneous discovery of control-flow and performance information. We demonstrate the usefulness of the TNR for detecting (unrecorded) delays and for probabilistic mining of variants when modelling the performance of a process. In order to compare different models from the performance perspective, we develop a framework for measuring performance fitness. Under this framework, we provide guarantees that TNR-based process discovery dominates existing techniques in measuring performance characteristics of a process. To illustrate the practical value of the TNR, we evaluate the approach against three real-life datasets. Our experiments show that the TNR yields an improvement in performance fitness over state-of-the-art algorithms.
... Resource & Resource behavior indicator Categories of behaviors, process outcomes (task-level and case-level), productivity -DEA Investigate the impact of given resource behaviors on process outcomes, evaluate resource productivity, and understand changes in resource behavior over time [53] How to assess the conformance of a pre-defined schedule of a service process to its actual execution? & How to improve operational performance of the scheduled process? ...
... An analogous focus on how conformance checking could be combined with performance improvement can be found in [53], where queuing networks are applied to check the conformance of an existing schedule of the process to its actual execution. Then, authors define three operational performance metrics relevant to the scheduling decisions (completion time, flow time, tardiness) to recommend adaptations to the scheduling policies. ...
Article
Knowledge absorption, information, and management tactics play a vital role for organizations to pursue process improvement. In this work, we aim to support organizations in building a prototype business process improvement plan through an evidence-based approach to make the most of the available information and knowledge. We exploit existing data that describe the business process’s execution to capture the governing process behaviors. By comparing the process’s execution across multiple business units, we assess the versatility of organizations and the pervasiveness of the process behaviors. Through a bipartite network, we suggest the concept of Operations Sophistication and deliver insights for the improvement prospects. Our approach contributes by identifying the behaviors with the greatest potential in a rapid and evidence-based way, by indicating a prioritization customized per organization, and by highlighting the most secure change paths. To illustrate our approach, we applied it to a real-world case from Siemens AG, and we present the methodology and the results through that case study.
... The operation of business processes can be improved by modelling their performance. Specifically, an accurate assessment of key performance measures, such as sojourn times and remaining times, enables bottleneck analysis and optimised resource planning [2]. ...
... (1) There is a delay between activities, i.e., we observe: precedes, overlaps, is finished by, contains. (2) There is no delay between activities, i.e., we observe: meets, starts and equals relations. (3) We do not observe any temporal relation between the pair of activities. ...
Article
Analysing performance of business processes is an important vehicle to improve their operation. Specifically, an accurate assessment of sojourn times and remaining times enables bottleneck analysis and resource planning. Recently, methods to create respective performance models from event logs have been proposed. These works have several limitations, though: They either consider control-flow and performance information separately, or rely on an ad-hoc selection of temporal relations between events. In this paper, we introduce the Temporal Network Representation (TNR) of a log. It is based on Allen's interval algebra, comprises the pairwise temporal relations for activity executions, and potentially incorporates the context in which these relations have been observed. We demonstrate the usefulness of the TNR for detecting (unrecorded) delays and for probabilistic mining of variants when modelling the performance of a process. In order to compare different models from the performance perspective, we further develop a framework for measuring performance fitness. Under this framework, TNR-based process discovery is guaranteed to dominate existing techniques in measuring performance characteristics of a process. In addition, we show how contextual information in terms of the congestion levels of the process can be mined in order to further improve capabilities for performance analysis. To illustrate the practical value of the proposed models, we evaluate our approaches with three real-life datasets. Our experiments show that the TNR yields an improvement in performance fitness over state-of-the-art algorithms, while congestion learning is able to accurately reconstruct congestion levels from event data.
... This object is called the case of the event and each object can be associated to multiple events, forming the event sequence of end-to-end behavior. The event sequences of all objects are the fundamental starting point of many process mining algorithms: control-flow visualization [2], i.e., frequent sequences of conducted actions, bottleneck analysis [3], or outcome prediction [4]. An event log in traditional process mining exhibits the structure depicted in Fig. 1a: a collection of event sequences, one for each case. ...
Preprint
Full-text available
The execution of processes leaves traces of event data in information systems. These event data can be analyzed through process mining techniques. For traditional process mining techniques, one has to associate each event with exactly one object, e.g., the company's customer. Events related to one object form an event sequence called a case. A case describes an end-to-end run through a process. The cases contained in event data can be used to discover a process model, detect frequent bottlenecks, or learn predictive models. However, events encountered in real-life information systems, e.g., ERP systems, can often be associated with multiple objects. The traditional sequential case concept falls short of these object-centric event data as these data exhibit a graph structure. One might force object-centric event data into the traditional case concept by flattening it. However, flattening manipulates the data and removes information. Therefore, a concept analogous to the case concept of traditional event logs is necessary to enable the application of different process mining tasks on object-centric event data. In this paper, we introduce the case concept for object-centric process mining: process executions. These are graph-based generalizations of cases as considered in traditional process mining. Furthermore, we provide techniques to extract process executions. Based on these executions, we determine equivalent process behavior with respect to an attribute using graph isomorphism. Equivalent process executions with respect to the event's activity are object-centric variants, i.e., a generalization of variants in traditional process mining. We provide a visualization technique for object-centric variants. The contribution's scalability and efficiency are extensively evaluated. Furthermore, we provide a case study showing the most frequent object-centric variants of a real-life event log.
... Techniques from the process mining field, which has grown steadily over the last decades, can be applied to gain insights in these event data [27]. In recent years, a lot of attention has been given to the discovery of process models from event logs [11,18,29,35], and subsequently, the quality measurement of these models [1,2,6,25,26]. Assessing the quality of discovered models is essential in order to find out whether it constitutes an appropriate representation of the process. ...
Article
Evaluating the quality of discovered process models is an important task in many process mining analyses. Currently, several metrics measuring the fitness, precision and generalization of a discovered model are implemented. However, there is little empirical evidence how these metrics relate to each other, both within and across these different quality dimensions. In order to better understand these relationships, a large-scale comparative experiment was conducted. The statistical analysis of the results shows that, although fitness and precision metrics behave very similar within their dimension, some are more pessimistic while others are more optimistic. Furthermore, it was found that there is no agreement between generalization metrics. The results of the study can be used to inform decisions on which quality metrics to use in practice. Moreover, they highlight issues which give rise to new directions for future research in the area of quality measurement.
... Subsequent studies concentrated more on algorithms/techniques for conformance checking (Rozinat and van der Aalst, 2008), process discovery (Rozinat et al., 2009), clustering and visualization . Currently, as new tools and techniques continue to emerge (Senderovich et al., 2015(Senderovich et al., , 2016De Smedt et al., 2015), the number of applicationoriented contributions is increasing (Conforti et al., 2015;Schönig et al., 2016). In addition to the major outlets of process mining identified in the previous section, our analysis of the types of process mining studies reveals a certain alignment of the different journals. ...
Article
[Accepted for publication 2017-11-13] Purpose – This paper reviews empirical studies on process mining in order to understand its use by organizations. The aim is further to outline future research opportunities. Design/methodology/approach – We propose a classification model that combines core conceptual elements of process mining with prior models from technology classification from the enterprise resource planning (ERP) and business intelligence field. Our model incorporates an organizational usage, a system-orientation and service nature, adding a focus on physical services. Our application is based on a systematic literature review of 144 research papers. Findings – The results show that, thus far, the literature has been chiefly concerned with realization of single business process management systems in single organizations. We conclude that cross-system or cross-organizational process mining is underrepresented in the ISR, as is the analysis of physical services. Practical implications – Process mining researchers have paid little attention to utilising complex use cases and mining mixed physical-digital services. Practitioners should work closely with academics to overcome these knowledge gaps. Only then will process mining be on the cusp of becoming a technology that allows new insights into customer processes by supplying business operations with valuable and detailed information. Originality/value – Despite the scientific interest in process mining, particularly scant attention has been given by researchers to investigating its use in relatively complex scenarios, e.g. cross-system and cross-organizational process mining. Furthermore, coverage on the use of process mining from a service perspective is limited, which fails to reflect the marketing and business context of most contemporary organizations, wherein the importance of such scenarios is widely acknowledged. The small number of studies encountered may be due to a lack of knowledge about the potential of such scenarios as well as successful examples, a situation we seek to remedy with this study.
... Examples include the seminal replaybased [2], [45] and alignment-based [32], [43] techniques, as well as the decomposition-based technique [29] utilized in this paper. While these techniques focus on conformance from a control-flow perspective, developments have also been made that check conformance with respect to other process perspectives, such as time-based [48] and databased conformance [49]. ...
... It presents an extensive, extremely focused evidence of analytics-enabled process enhancement that reflects the actual complexity of a real-life process. Although it has been widely argued that PM may improve business processes, there is a dearth of comprehensive evidences regarding its application for such an objective, which has been tackled in literature at a lower level of detail only, and for other purposes, e.g., [57]. ...
Article
Full-text available
The current digitalization trend, the increased attention towards sustainability, and the spread of the business analytics call for higher efficiency in port operations and for investigating the quantitative approaches for maritime logistics and freight transport systems. Thus, this manuscript aims at enabling analytics-driven improvements in the port transportation processes efficiency by streamlining the related information flow, i.e., by attaining shorter time frames of the information and document sharing among the export stakeholders. We developed a case study in a mid-sized European port, in which we applied Process Mining (PM)—an emerging type of business analytics—to a seven-month dataset from the freight export process. Four process inefficiencies and an issue that can jeopardize the reliability of the time performance measurements were detected, and we proposed a draft of solutions to cope with them. PM enabled enhancements in the overall export time length, which might improve the vessels’ turnover and reduce the corresponding operational costs, and supported the potential re-design of performance indicators in process control and monitoring. The results answer the above-mentioned calls and they offer a valuable, analytics-based alternative to the extant approaches for improving port performance, because it focuses on the port information flow, which is often related to sustainability issues, rather than the physical one.
... This task tries to find misalignments between event log and model. Example works include [19,2,20]. ...
Conference Paper
While models and event logs are readily available in modern organizations, their quality can seldom be trusted. Raw event recordings are often noisy, incomplete, and contain erroneous recordings. The quality of process models, both conceptual and data-driven, heavily depends on the inputs and parameters that shape these models, such as domain expertise of the modelers and the quality of execution data. The mentioned quality issues are specifically a challenge for conformance checking. Conformance checking is the process mining task that aims at coping with low model or log quality by comparing the model against the corresponding log, or vice versa. The prevalent assumption in the literature is that at least one of the two can be fully trusted. In this work, we propose a generalized conformance checking framework that caters for the common case, when one does neither fully trust the log nor the model. In our experiments we show that our proposed framework balances the trust in model and log as a generalization of state-of-the-art conformance checking techniques.
... Nguyen et al. (2016) proposed the evolution of process performance through the concept of staged phase flow. Senderovich et al. (2016) proposed the operational performance improvement method according to queuing network-based conformance inspections and deviation origin analysis. Sun et al. (2017) adopted a mirror matrix method based on footprint variant and parallel programming that optimizes the original process according to the log. ...
... Selanjutnya, teori penjadwalan juga termasuk dalam bagian riset operasi. Hal ini juga dapat diterapkan pada proses layanan, misalnya dalam transportasi, telekomunikasi atau sektor kesehatan yang merupakan tulang punggung ekonomi saat ini [5]. Model konseptual dari proses layanan memungkinkan analisis operasional yang mendukung, misalnya, penyediaan sumber daya atau prediksi keterlambatan. ...
Article
AbstrakPada waktu tertentu fakultas teknik juga sering mengadakan kegiatan. Susunan panitia yang telah terbentuk terdiri dari pegawai dan dosen. Panitia yang telah terbentuk ini telah siap menyelenggarakan kegiatan yang telah direncanakan. Namun, ada yang tidak efektif dalam penggunaan sarana pendukung seperti dana dan logistik. Optimalisasi sarana pendukung ini masih berbasis intuisi dan kebiasaan. Kemududian, terdapat metode penugasan yang merupakan salah satu materi dari riset operasi. Pada penelitian ini membahasa mengenai metode penugasan yang direalisasikan dalam suatu sistem aplikasi untuk mengatasi optimalisasi penggunaan sarana pendukung pada kepanitiaan di Fakultas Tenik Universitas Nusantara PGRI Kediri. Sistem ini diharapkan mampu melakukan kalkulasi sesuai dengan metode penugasan dan menghasilkan keterangan penggunaan sumber daya atau sarana.Kata kunci: Metode Penugasan, Sistem, Kepanitiaan, Riset OperasiAbstractAt certain times, faculty of engineering also often hold activities. The composition of the committee that has been formed consists of employees and lecturers. The committee that has been formed is ready to organize the activities that have been planned. However, some are not effective in the use of support facilities such as funds and logistics. The optimization of the support facilities is still based on intuition and habit. Then, there is a method of assignment which is one of the part of operations research. In this study, the method of assignment is realized in an application system to overcome the optimization of the use of support facilities at the committee in the Faculty of Engineering Universitas Nusantara PGRI Kediri. The system is expected to perform calculations in accordance with the method of assignment and generates information on the use of resources or facilitiesKeywords: Assignment Method, System, Committee, Operation Research
... Techniques from the process mining field, which has grown steadily over the last decades, can be applied to gain insights into these event data [9]. Over the past decade, a lot of attention has been given to the discovery of process models from event logs [57,97,138,139], and subsequently the quality measurement of these models [6,11,12,99,123]. ...
Book
This book is a revised version of the PhD dissertation written by the author at Hasselt University in Belgium.This dissertation introduces the concept of process realism. Process realism is approached from two perspectives in this dissertation. First, quality dimensions and measures for process discovery are analyzed on a large scale and compared with each other on the basis of empirical experiments. It is shown that there are important differences between the different quality measures in terms of feasibility, validity and sensitivity. Moreover, the role and meaning of the generalization dimension is unclear. Second, process realism is also tackled from a data point of view. By developing a transparent and extensible tool-set, a framework is offered to analyze process data from different perspectives. From both perspectives, recommendations are made for future research, and a call is made to give the process realism mindset a central place within process mining analyses. In 2020, the PhD dissertation won the “BPM Dissertation Award”, granted to outstanding PhD theses in the field of Business Process Management.
... Techniques from the process mining field, which has grown steadily over the last decades, can be applied to gain insights into these event data [9]. Over the past decade, a lot of attention has been given to the discovery of process models from event logs [57,97,138,139], and subsequently the quality measurement of these models [6,11,12,99,123]. ...
Chapter
In this chapter, we evaluate the use of bupaR in the context of education.
... Recently, also the behavioral influence of actors and resources on the processes they perform has been investigated, specifically from the angle of performance. This research led to integrating queueing models and process models [90] and the detection of complex performance patterns when considering all process executions together [17,47]. Integrated treatment of these perspectives allowed to increase accuracy in process prediction [48,89], inferring otherwise unobservable behavior [30], and allows detecting emergent system-level phenomena of cascades of increased workload and processing delays [94]. ...
Preprint
Full-text available
Augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems that draws upon trustworthy AI technology. An ABPMS enhances the execution of business processes with the aim of making these processes more adaptable, proactive, explainable, and context-sensitive. This manifesto presents a vision for ABPMSs and discusses research challenges that need to be surmounted to realize this vision. To this end, we define the concept of ABPMS, we outline the lifecycle of processes within an ABPMS, we discuss core characteristics of an ABPMS, and we derive a set of challenges to realize systems with these characteristics.
... Halonen et al. [98] Comprehensive full life-cycle multi-method approach to data-driven service reconfiguration. Cycles of redesign and optimisation of resource allocation in a queueing network model informed experimental pilot studies to assess realistic working practices Senderovich et al. [99] Fork/join queueing network derived from administrative logs and schedules and Real Time Location Service (RTLS) data of an outpatient service allows simulation of different central pharmacy service policies. The optimal strategy is modelled to yield a 20% increase in performance Johnson et al. [11] Portfolio of three case studies using models from a fully developed process mining framework (ClearPath method) to implement the NETIMIS health economics discrete event simulation tool [103], illustrating both the difficulties and the potential of this type of application. ...
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Obtaining time dependent results for many server queues is, under general structural assumptions, a hard problem. This paper makes an attempt to approximate stochastically the behaviour of a general many server queue by using single server queues as stochastic bounds. We propose three alternative ways of constructing approximating single server queues. The first technique utilizes special classes of service time distributions new better than used, new worse than used, the second is via dividing the service times by the number of servers, and the third is based on a grouping idea of the customers. The first and third techniques yield in fact two bounding queues each, one of which is faster and one slower than the original s-server queue.
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A comprehensive treatment on the use of quantitative modeling for decision making and best practices in the service industries. Making up a significant part of the world economy, the service sector is a rapidly evolving field that is relied on to dictate the public's satisfaction and success in various areas of everyday life, from banking and communications to education and healthcare. Service Science provides managers and students of the service industries with the quantitative skills necessary to model key decisions and performance metrics associated with services, including the management of resources, distribution of goods and services to customers, and the analysis and design of queueing systems. The book begins with a brief introduction to the service sector followed by an introduction to optimization and queueing modeling, providing the methodological background needed to analyze service systems. Subsequent chapters present specific topics within service operations management, including: • Location modeling and districting • Resource allocation problems • Short- and long-term workforce management • Priority services, call center design, and customer scheduling • Inventory modeling • Vehicle routing. The author's own specialized software packages for location modeling, network optimization, and time-dependent queueing are utilized throughout the book, showing readers how to solve a variety of problems associated with service industries. These programs are freely available on the book's related web site along with detailed appendices and online spreadsheets that accompany the book's "How to Do It in Excel" sections, allowing readers to work hands-on with the presented techniques. Extensively class-tested to ensure a comprehensive presentation, Service Science is an excellent book for industrial engineering and management courses on service operations at the upper-undergraduate and graduate levels. The book also serves as a reference for researchers in the fields of business, management science, operations research, engineering, and economics. This book was named the 2010 Joint Publishers Book of the Year by the Institute of Industrial Engineers.
Article
Fork/join (F/J) networks can be used to model parallel processing computer systems.and manufacturing systems. In this paper, we present some fundamental equivalence properties that hold for F/J networks with blocking. Two networks that are equivalent may appear different, but their behavior is closely related. Their throughputs are the same and there is a simple relationship between their average buffer levels. We study a model of F/J networks where processing times are exponentially distributed. We prove a theorem that provides a test for equivalence and illustrate it with several examples.
Conference Paper
Process Mining attempts to reconstruct the workflow of a business process from logs of activities. This task is quite important in business scenarios where there is not a well understood and structured definition of the business process performed by workers. Activities logs are thus mined in the attempt to reconstruct the actual business process. In this paper, we propose the generalization of a popular process mining algorithm, named Heuristics Miner, to time intervals. We show that the possibility to use, when available, time interval information for the per- formed activities allows the algorithm to produce better workflow models.
Conference Paper
Treating instants of time as primitive not only is conceptually implausible but also has encountered grave practical difficulties. A satisfactory theory of time seems to be one which is based on the common-sense idea that events or periods are the primitive enties of time while instants are constructed from them. In this paper we present one such common-sense theory of time. We start from a structure of events, construct instants out of the events, and then show that these instants have the properties we normally expect of them. Views discussed here include the view of Allen and Hayes, and that of Russell.
Book
The more challenging case of transient analysis of Markov chains is investigated in Chapter 5. The chapter introduces symbolic solutions in simple cases such as small or very regular state spaces. In general, numerical techniques are more suitable and are therefore covered in detail. Uniformization and some variants thereof are introduced as the method of choice for transient analysis in most cases. Particular emphasis is given to stiffness tolerant uniformization that can be of practical relevance in many modeling scenarios where relatively rare and fast events occur concurrently. As an alternative a method for aggregation/disaggregation of stiff Markov chains is introduced for a computation of approximate transient state probabilities. The method is based on a distinction of fast recurrent and fast transient sets of states that can be aggregated with relatively small error. All steps are illustrated by a detailed example model of server breakdown and repair. In addition to numerical results an error analysis is provided as well.
Article
In this paper the class of acyclic fork-join queuing networks that arise in various applications, including parallel processing and flexible manufacturing are studied. In such queuing networks, a fork describes the simultaneous creation of several new customers, which are sent to different queues. The corresponding join occurs when the services of all these new customers are completed. The evolution equations that govern the behavior of such networks are derived. From this, the stability conditions are obtained and upper and lower bounds on the network response times are developed. These bounds are based on various stochastic ordering principles and on the notion of association of random variables.