Finally, the monitoring and control phase inspects process instances for the next iteration of the BP management life cycle. This is why the stages in BP's management lifecycle must be circular, that is, the output of the monitoring and control phase flows back into the discovery, analysis and improvement phases. BPS also helps predict the performance of the system under a number of scenarios determined by the decision maker.
A detailed theoretical discussion of the relationships between softness and probability can be found in [40]. Probability is an indicator of the frequency or probability that an item is in a class. We use the parameter implementation in terms of an interval-valued variable defined as:.
In our approach, to efficiently calculate the interval-valued output of the system, a GA is used. The fitness function is represented by the output KPI, the interval-valued output of the process model. In a GA, a chromosome (or genome) consists of the set of parameters that define a proposed solution to the problem to be solved.
However, a very high crossover rate leads to unpromising regions of the search space being explored.
Architectural Implementation
It is worth noting that the optimization performed by the IBimp system typically lasts only a few generations. The coexistence of multiple GA implementations is an important requirement to allow inspection of how the optimization is performed, rather than its performance. More specifically, the BP designer/analyst (the stick man on the left) first uses a BPMN editor to create a BPMN model (the artifact on the bottom right).
For this purpose, any editor can be used, provided that an XML file in BPMN exchange format can be exported. Once the BPMN model artifact is created, it can be imported into another software product that supports the BPMN exchange XML format. The BPMN model is then enriched by the BP Designer/Analyst with two types of parameters, namely JSON parameters and DOM (Document Object Model) range parameters, via a management interface.
More specifically, DOM stands for Document Object Model [48], a cross-platform and language-independent convention for representing and interacting with objects, while JSON stands for JavaScript Object Notation, a lightweight data exchange format. [49] that is used to send such parameters to the Bimp Simulator Engine, a core component of the architecture [50]. More specifically, Figure 11a shows the main interface for setting parameters with a value and range. Further simulation parameters can be set by an advanced user via a plain text configuration file.
More specifically, the Application Controller is in charge of: (i) taking the parameters; (ii) the execution of the core simulation components; and (iii) taking the output and log data. Moreover, the system is able to produce a plot of the optimization process, as shown in the experimental section. The optimization is performed by the genetic algorithm, a core component activated by the application controller.
The genetic algorithm is able to control the Bimp Simulator Engine and instantiate the JSON parameter artifact in order to perform a large number of single value output evaluations, each with different JSON parameters. When the parameters are all single-valued, the application controller ignores the genetic algorithm and the simulation output is calculated only via the Bimp Simulator Engine. Further genetic parameters can be set by an advanced user via plain text configuration files.
Experimental Studies
The inclusive gateway can be used to demonstrate that one branch is always taken, while the other is only taken if the additional insurance is required. It can be noted that there are very long queues, especially at the last activities, waiting for the administration of the additional insurance. Then, an interval-valued simulation was performed using intervals for some tasks and resources, as shown in the right column of Table 5.
To illustrate the second way of using the system, Table 8 shows the values of the parameters that were used by the GA to have the minimum and maximum KPI, i.e. the best individual of both optimization processes. Subsequently, an interval-rated simulation has been carried out, using intervals on some tasks and resources, as expressed in the last column of Table 9. To better examine the sensitivity of the number of emergency rooms, we can inspect the detailed progress of the KPI under the minimization and maximization steps, shown in Tables 12 and 13 respectively.
More specifically, it is worth noting in table 12 that a reduction of about 6 hours in the total duration is due to a reduction of several seconds in the duration of triage and registration, despite the fact that the number of triage rooms has also been reduced. It is worth noting in Table 13 that an increase of about 20 hours in total duration can be attributed to fewer sorting rooms and a small increase in check-in and check-in durations. If not, then the matter ends; otherwise the appraiser enters the correct information into the system.
If not, the matter is terminated; otherwise the assessor approves the provisional estimate of the damage. The problem in this example is to find the most efficient staffing levels for each of the five resource types. This scenario provides a real experience of the application of our interval-valued simulation in the context of a marine container terminal located in the port of Leghorn (Italy).
More specifically, the purpose of the study was to assess the extent to which information technology solutions can improve overall process efficiency. This scenario presents a real-world experience of applying our interval-valued simulation tool in the context of the acquisition process of a leading auto and truck parts company. More specifically, the goal of the study was to improve the overall efficiency of the purchase order process.
It can be noted that the maximum KPI can mainly be attributed to a long duration of the tasks “Chief Financial Officer approval” and “materials and delivery notes approval”. Duration of the relevant tasks, in the minimum KPI, as-is and maximum KPI models.
Conclusions and Future Works
Cimino and Gigliola Vaglini are both responsible for the concept of the article, the results presented and the writing. Business Process Management: 10th International Conference, BPM 2012, Tallinn, Estonia, September proceedings; Lecture Notes in Computer Science, Part 7481; Springer: Berlin/Heidelberg, Germany, 2012. In Business Process Management; Lectures in Computer Science; Springer: Berlin/Heidelberg, Germany, 2006; Part 4102, pg.
Managing Business Process Flows: Principles of Operations Management, 3rd ed.; Pearson: Upper Saddle River, NJ, USA, 2012. In Business Process Management; Lecture notes in computer science; Springer: Berlin/Heidelberg, Germany, 2012; Volume 7481, p. In Handbook on Business Process Management; International handbooks on information systems; Springer: Berlin/Heidelberg, Germany, 2010; p.
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I Proceedings of the International Workshop on Business Process Intelligence (BPI), Brisbane, Australien, 24. september 2007; s. I Proceedings of the 2nd IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'93), San Francisco, CA, USA, 28. marts-1. april 1993; Bind 2, s. I Proceedings of the 2011 International Conference on Security and Intelligent Information Systems (SIIS’11), Warszawa, Polen 13.-14. juni 2011; Springer: Berlin/Heidelberg, Tyskland, 2012; pp.
