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Production and maintenance tasks apply for access to the same resources. Maintenance-related machine downtime reduces productivity, but the costs incurred due to unplanned machine failures often outweigh the costs associated with predictive maintenance. Costs incurred due to unplanned machine failure include corrective maintenance, reworks, delays in deliveries, breaks in the work of employees and machines. Therefore, scheduling of production and maintenance tasks should be considered jointly. The problem of generating a predictive schedule with given constrains is considered. The objective of the paper is to develop a scheduling method that reflects the operation of the production system and nature of disturbances. The original value of the paper is the development of the method of a basic schedule generation with the application of the Ant Colony Optimisation. A predictive schedule is built by planning the technical inspection of the machine at time of the predicted failure-free time. The numerical simulations are performed for job/flow shop systems.
Iwona Paprocka; Damian Krenczyk; Anna Burduk. The Method of Production Scheduling with Uncertainties Using the Ants Colony Optimisation. Applied Sciences 2020, 11, 171 .
AMA StyleIwona Paprocka, Damian Krenczyk, Anna Burduk. The Method of Production Scheduling with Uncertainties Using the Ants Colony Optimisation. Applied Sciences. 2020; 11 (1):171.
Chicago/Turabian StyleIwona Paprocka; Damian Krenczyk; Anna Burduk. 2020. "The Method of Production Scheduling with Uncertainties Using the Ants Colony Optimisation." Applied Sciences 11, no. 1: 171.
The paper proposes a method to solve the mixed-model assembly line sequencing problem based on the Simulated Annealing Optimization algorithm. Achieving full line synchronization, by creating the appropriate model version sequence, becomes increasingly difficult at current levels of product complexity. The method of generating the candidate sequence by repeatedly swapping two random positions depending on the current temperature value was used. The search area is relatively large in the early phase of the algorithm. In addition, the conditions for resetting the temperature indicator if the local point candidate solutions are not improved have been added. It was also necessary to create a search objective function, taking into account specific aspects related to the mix-model sequencing problem. The proposed approach is based on binary coding of the input sequence and a suitably modified method of determining the boundaries of the search area. This increases the chance to avoid local optima trapping.
Damian Krenczyk; Karol Dziki. A Simulated Annealing Based Method for Sequencing Problem in Mixed Model Assembly Lines. Advances in Intelligent Systems and Computing 2020, 331 -341.
AMA StyleDamian Krenczyk, Karol Dziki. A Simulated Annealing Based Method for Sequencing Problem in Mixed Model Assembly Lines. Advances in Intelligent Systems and Computing. 2020; ():331-341.
Chicago/Turabian StyleDamian Krenczyk; Karol Dziki. 2020. "A Simulated Annealing Based Method for Sequencing Problem in Mixed Model Assembly Lines." Advances in Intelligent Systems and Computing , no. : 331-341.
This article examines the problem of balancing modern assembly lines for large-size products typical for the automotive industry. The model of a multimanned assembly line balancing problem is proposed, in which assembly tasks are assigned to specific areas closely related to the place of the performance thereof on the vehicle. The model takes into account additional location restrictions. The proposed approach fits in with current trends aiming at minimizing the space occupied by assembly lines along with reducing the number of stations. A hybrid method is proposed combining modified known heuristics of task assignments to workstations with bounding and backtracking techniques in order to determine the assignment of workers to tasks.
Damian Krenczyk; Karol Dziki. Heuristic and Backtracking Algorithm for Multimanned Assembly Line Balancing Problem with Location Constraints. Cybernetics and Systems 2020, 51, 698 -713.
AMA StyleDamian Krenczyk, Karol Dziki. Heuristic and Backtracking Algorithm for Multimanned Assembly Line Balancing Problem with Location Constraints. Cybernetics and Systems. 2020; 51 (7):698-713.
Chicago/Turabian StyleDamian Krenczyk; Karol Dziki. 2020. "Heuristic and Backtracking Algorithm for Multimanned Assembly Line Balancing Problem with Location Constraints." Cybernetics and Systems 51, no. 7: 698-713.
Reggie Davidrajuh; Damian Krenczyk. Extending GPenSIM for Model Checking on Petri Nets. International journal of simulation: systems, science & technology 2020, 1 .
AMA StyleReggie Davidrajuh, Damian Krenczyk. Extending GPenSIM for Model Checking on Petri Nets. International journal of simulation: systems, science & technology. 2020; ():1.
Chicago/Turabian StyleReggie Davidrajuh; Damian Krenczyk. 2020. "Extending GPenSIM for Model Checking on Petri Nets." International journal of simulation: systems, science & technology , no. : 1.
Large-scale manufacturing systems involve hardware and software that are highly interconnected and complex. Unexpected failures in these systems can cause material damages and can risk human lives too. The definite way of avoiding unexpected failures is to make a model of the system and to perform model verification and validation on it. Petri nets are a highly effective way of modelling discrete-event systems. Model checking is the terminology that is used for model verification on Petri Nets. General-purpose Petri Net Simulator (GPenSIM) is a tool for modelling, simulation, performance evaluation, and control of discrete-event systems (GPenSIM: a general purpose Petri net simulator, http://www.davidrajuh.net/gpensim, 2019, [15]). GPenSIM is developed by one of the authors of this chapter. This chapter explores the potentials of incorporating the model checking functions to GPenSIM. In this chapter, the problem of model checking is presented. The chapter introduces Activity-Oriented Petri Nets (AOPN) and GPenSIM for model checking of cyclic production systems.
Reggie Davidrajuh; Bozena Skolud; Damian Krenczyk. Incorporating Automatic Model Checking into GPenSIM. Developments in Advanced Control and Intelligent Automation for Complex Systems 2019, 175 -187.
AMA StyleReggie Davidrajuh, Bozena Skolud, Damian Krenczyk. Incorporating Automatic Model Checking into GPenSIM. Developments in Advanced Control and Intelligent Automation for Complex Systems. 2019; ():175-187.
Chicago/Turabian StyleReggie Davidrajuh; Bozena Skolud; Damian Krenczyk. 2019. "Incorporating Automatic Model Checking into GPenSIM." Developments in Advanced Control and Intelligent Automation for Complex Systems , no. : 175-187.
K Dziki; Damian Krenczyk. Mixed-model assembly line balancing problem with tasks assignment. IOP Conference Series: Materials Science and Engineering 2019, 591, 1 .
AMA StyleK Dziki, Damian Krenczyk. Mixed-model assembly line balancing problem with tasks assignment. IOP Conference Series: Materials Science and Engineering. 2019; 591 ():1.
Chicago/Turabian StyleK Dziki; Damian Krenczyk. 2019. "Mixed-model assembly line balancing problem with tasks assignment." IOP Conference Series: Materials Science and Engineering 591, no. : 1.
Many decisions that must be made during the production process mean that limited human perception is not able to meet the growing requirements of keeping the parameters and constantly striving to increase the efficiency of current production lines. The main challenge is also the continuous increase of awareness about the process and the possibility of its modernization. This forces the expansion of the production with new elements, which are not directly related to the production line itself. And this in turn forces the expansion of knowledge, for example, cooperation with new elements. The theoretical knowledge that each employee must have from every issue going to be very general without going deeper into details. Simultaneous control of all mutual elements with the same coincidence becomes impossible. Traditional methods of failure analysis and finding reasons for its occurrence are inefficient and ineffective. This paper is attempting to create a system topology for all subsystems. A comprehensive production management system, its efficiency and failure predictive system will be discussed. The system should integrate and correlate many different databases, which are conducted according to different standards. This causes a necessity of choosing a method for seeking solutions for problems in such a large stored database. “Big data” which is popular today, is using neural networks which not always is the best choice. Especially when we don’t have enough knowledge about technology and connections inside the process. Maximum use of expert knowledge, experience of employees, data acquisition and usage of unfiltered data will be presented in this paper.
Krzysztof Niemiec; Damian Krenczyk. Multi-domain, Advisory Computing System in Continuous Manufacturing Processes. Advances in Intelligent Systems and Computing 2019, 376 -385.
AMA StyleKrzysztof Niemiec, Damian Krenczyk. Multi-domain, Advisory Computing System in Continuous Manufacturing Processes. Advances in Intelligent Systems and Computing. 2019; ():376-385.
Chicago/Turabian StyleKrzysztof Niemiec; Damian Krenczyk. 2019. "Multi-domain, Advisory Computing System in Continuous Manufacturing Processes." Advances in Intelligent Systems and Computing , no. : 376-385.
Mass customization production is the next stage in the development of production systems that combines an individual approach to the client needs and benefits of mass production. This approach forces manufacturers to seek new, more effective methods of production flow planning, in particular methods for solving the assembly line balancing problem. The traditional approaches and methods proposed for solving balancing problems require adaptation to new constraints associated with the increasingly widespread introduction of multi-manned and spatially divided assembly workstations. This requires considering additional location restrictions and a more complex allocation of tasks in contrast to restricted only by technological precedencies and time constraints for Simple Assembly Line Balancing Problem. The paper presents a proposal for solving the problem of line balancing with location constraints using new hybrid heuristic algorithm, which is a combination of a modified RPW algorithm and a local search of task sequence on assembly stations zones. Moreover, the concepts of smoothness and efficiency is referred to two separate areas: stations and employees. Experimental results for the literature case of a 30 tasks problem indicate the effectiveness of the proposed approach in practice.
Damian Krenczyk; Karol Dziki. A Hybrid Heuristic Algorithm for Multi-manned Assembly Line Balancing Problem with Location Constraints. Advances in Intelligent Systems and Computing 2019, 333 -343.
AMA StyleDamian Krenczyk, Karol Dziki. A Hybrid Heuristic Algorithm for Multi-manned Assembly Line Balancing Problem with Location Constraints. Advances in Intelligent Systems and Computing. 2019; ():333-343.
Chicago/Turabian StyleDamian Krenczyk; Karol Dziki. 2019. "A Hybrid Heuristic Algorithm for Multi-manned Assembly Line Balancing Problem with Location Constraints." Advances in Intelligent Systems and Computing , no. : 333-343.
The goal of this paper is to compare the two simulation environments of FlexSim and GPenSIM, for manufacturing simulations. A discrete-event system in material handling is taken as a case study, and this system is modeled and simulated with FlexSim and GPenSIM. The motivation of this paper is to share the experiences from this simulation study. Firstly, this paper introduces the simulation tools the FlexSim and the GPenSIM. Secondly, a case study is presented that involves an elevator as a part of a shuttle-based storage and retrieval system in a modern manufacturing facility. Since the goal and scope of this paper is limited to comparing the two simulation environments, this paper uses simple elevator algorithms (the Standard Elevator Algorithm). The experience received from the experiment are analyzed under three categories such as easiness of using, the possibility for integration with other tools and techniques, and the suitability for manufacturing simulation. The experience received from the experiment show that FlexSim being a commercial software is far easier to use and offer a dedicated environment for manufacturing simulation. Whereas, GPenSIM provides a general platform for modeling any discrete-event systems, and provide easy integration with the other tools that are available on the MATLAB platform.
Damian Krenczyk; Reggie Davidrajuh; Bozena Skolud. Comparing Two Methodologies for Modeling and Simulation of Discrete-Event Based Automated Warehouses Systems. Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020) 2019, 161 -175.
AMA StyleDamian Krenczyk, Reggie Davidrajuh, Bozena Skolud. Comparing Two Methodologies for Modeling and Simulation of Discrete-Event Based Automated Warehouses Systems. Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020). 2019; ():161-175.
Chicago/Turabian StyleDamian Krenczyk; Reggie Davidrajuh; Bozena Skolud. 2019. "Comparing Two Methodologies for Modeling and Simulation of Discrete-Event Based Automated Warehouses Systems." Proceedings of the 2nd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2020) , no. : 161-175.
Nowadays, the manufacturing processes become more complex and difficult to analyze. Therefore, the computer simulation is widely used for modeling of manufacturing systems that can include human resources and industrial robots. In the article, the human- and robot-related factors are described, and the methodology of modeling and simulation of human operators and industrial robots is presented. It is based on Overall Equipment Effectiveness (OEE) factors and includes planned availability and failures, work performance, and product quality. An example of industrial press line with modeling and simulation of human resources and industrial robots in FlexSim is presented that allows for better representation and understanding of the real production process.
Grzegorz Gołda; Adrian Kampa; Damian Krenczyk. The Methodology of Modeling and Simulation of Human Resources and Industrial Robots in FlexSim. Happy City - How to Plan and Create the Best Livable Area for the People 2019, 87 -99.
AMA StyleGrzegorz Gołda, Adrian Kampa, Damian Krenczyk. The Methodology of Modeling and Simulation of Human Resources and Industrial Robots in FlexSim. Happy City - How to Plan and Create the Best Livable Area for the People. 2019; ():87-99.
Chicago/Turabian StyleGrzegorz Gołda; Adrian Kampa; Damian Krenczyk. 2019. "The Methodology of Modeling and Simulation of Human Resources and Industrial Robots in FlexSim." Happy City - How to Plan and Create the Best Livable Area for the People , no. : 87-99.
Reggie Davidrajuh; Damian Krenczyk; Bozena Skolud. Finding Clusters in Petri Nets. An approach based on GPenSIM. Modeling, Identification and Control: A Norwegian Research Bulletin 2019, 40, 1 -10.
AMA StyleReggie Davidrajuh, Damian Krenczyk, Bozena Skolud. Finding Clusters in Petri Nets. An approach based on GPenSIM. Modeling, Identification and Control: A Norwegian Research Bulletin. 2019; 40 (1):1-10.
Chicago/Turabian StyleReggie Davidrajuh; Damian Krenczyk; Bozena Skolud. 2019. "Finding Clusters in Petri Nets. An approach based on GPenSIM." Modeling, Identification and Control: A Norwegian Research Bulletin 40, no. 1: 1-10.
Requirements set by clients regarding, on the one hand, the highest quality of services provided, on the other hand, shorter and shorter order completion dates, cause that enterprises are often unable to cope with them. A production load or limited production resources also means that enterprises are no longer able to accept a new order for their production plan. The following problem is outlined here - send the customer away empty-handed, or try to execute the order, but with a much later execution date and expose to the cost of contractual penalties. Therefore, solutions are sought, which can be particularly advantageous for producers in the small and medium-sized enterprises (SME) sector, for whom this is an increasingly frequent problem - sometimes means "to be or not to be" on the market. One of the possibilities for enterprises is to use the concept of a virtual manufacturing enterprises, which mainly consists in sharing the free production capacity of each producer within the virtual network A network that has been established even between individual enterprises that are geographically dispersed. By establishing a group of several companies cooperating at the same time, one should find such a thread of agreement that it would be possible to cooperate, and also to achieve a common goal, which is to make a final product for the recipient. If it is necessary to make a final product that meets the assumed criteria, it is necessary not only to define the level of cooperation, but also there exists much need for a mechanism to manage and control information flow among manufacturers. Therefore, it is necessary to have information on production issues, such as the quantity and quality of spare production capacities, their access time, additional resources, semi-finished products, and since these are geographically dispersed enterprises, the location of each enterprise is also important. In addition, the network itself is established on the basis of information contained in the production order, so you can adjust the number of participants to your needs, and assign tasks accordingly. All executive actions are taken on the basis of the proposed algorithm, which describes the entire decision-making process, how to collect information, where to keep them, and above all, who should be responsible for providing information.
M Olender; D Krenczyk. Manage and control information flow in virtual manufacturing enterprises. IOP Conference Series: Materials Science and Engineering 2018, 400, 022041 .
AMA StyleM Olender, D Krenczyk. Manage and control information flow in virtual manufacturing enterprises. IOP Conference Series: Materials Science and Engineering. 2018; 400 (2):022041.
Chicago/Turabian StyleM Olender; D Krenczyk. 2018. "Manage and control information flow in virtual manufacturing enterprises." IOP Conference Series: Materials Science and Engineering 400, no. 2: 022041.
In the paper results of the design optimisation process for the self-locking moving device using CAD software are shown. As the object of the design optimisation the plier barrier subassembly was selected. The decision about selecting of the plier barrier subassembly as the optimisation object was made taking into account its importance for the device operating and safety. The optimisation process was carried out in Autodesk Inventor software. As a result, a new modified design of the plier barrier was developed. The design was next used for manufacturing of the self-locking moving device prototype. The prototype device was subjected to load mechanical tests in order to check its mechanical strength and the plier barrier clamping force effectiveness. The results of the mechanical tests in the content of the paper are presented in whole. The carried out experiments proved the device correctness of operation. Having these done, the self-locking moving device data sheet was prepared. Taking into account fact that according to The Machinery Directive, Directive 2006/42/EC the self-locking moving device is a machine, it was necessary to perform technical risk assessment. Summarizing up, thanks to performed design optimisation, it was possible to raise the safety level of the device operation, increase the device moving speed and to decrease the overall production costs by simplifying the device design.
C Grabowik; K Kalinowski; D Krenczyk; Iwona Paprocka; W Kempa; K Juszczak. The design optimisation of the self-locking moving device using CAD software. IOP Conference Series: Materials Science and Engineering 2018, 400, 022034 .
AMA StyleC Grabowik, K Kalinowski, D Krenczyk, Iwona Paprocka, W Kempa, K Juszczak. The design optimisation of the self-locking moving device using CAD software. IOP Conference Series: Materials Science and Engineering. 2018; 400 (2):022034.
Chicago/Turabian StyleC Grabowik; K Kalinowski; D Krenczyk; Iwona Paprocka; W Kempa; K Juszczak. 2018. "The design optimisation of the self-locking moving device using CAD software." IOP Conference Series: Materials Science and Engineering 400, no. 2: 022034.
The development of production technologies, customer-centric philosophies and, above all, increased uncertainty around today's market, make companies look for solutions that would support their functioning on the market. Keeping up with changes requires, among other things, proper planning of production processes and high investments in innovative technological solutions. For this reason, it is increasingly difficult to make a modern product alone. That is why enterprises from the small and medium enterprises sector are looking for solutions that would enable to continue production without the need to have all the specialized production resources dedicated to individual products. Therefore, companies outsource the choice, configuration and or manufacture components that they are not able to do themselves. Another option is to join the virtual manufacturing network, where manufacturers temporarily share their spare capacity. Such cooperation is possible even for individual enterprises that are geographically dispersed. The proposed solution of generation of possible production routes (based on game theory) also takes into account the time and costs required to transport intermediates between manufacturers (alternative routes). The obtained results are the lead time and costs of manufacturing processes.
D Krenczyk; M Olender. Production flow planning method applied to virtual manufacturing enterprises. IOP Conference Series: Materials Science and Engineering 2018, 400, 022036 .
AMA StyleD Krenczyk, M Olender. Production flow planning method applied to virtual manufacturing enterprises. IOP Conference Series: Materials Science and Engineering. 2018; 400 (2):022036.
Chicago/Turabian StyleD Krenczyk; M Olender. 2018. "Production flow planning method applied to virtual manufacturing enterprises." IOP Conference Series: Materials Science and Engineering 400, no. 2: 022036.
The paper presents the problem of scheduling in flexible manufacturing systems with additional resources consideration. In the developed model, the basic resources e.g. and additional resources - staff, tools, etc. - are assigned to resource groups. The allocation of operations to particular resources is carried out through these groups - by indicating the number of resources needed from a given group. At the scheduling stage, depending on the expected values of beginning and ending of the operation, the most advantageous resource for allocation is selected. Forward and backward scheduling strategies and serial and parallel schedule generation schemes are discussed in this context.
K Kalinowski; D Krenczyk; Iwona Paprocka; W Kempa; C Grabowik. Schedule generation schemes for flexible manufacturing systems with additional resources. IOP Conference Series: Materials Science and Engineering 2018, 400, 062016 .
AMA StyleK Kalinowski, D Krenczyk, Iwona Paprocka, W Kempa, C Grabowik. Schedule generation schemes for flexible manufacturing systems with additional resources. IOP Conference Series: Materials Science and Engineering. 2018; 400 (6):062016.
Chicago/Turabian StyleK Kalinowski; D Krenczyk; Iwona Paprocka; W Kempa; C Grabowik. 2018. "Schedule generation schemes for flexible manufacturing systems with additional resources." IOP Conference Series: Materials Science and Engineering 400, no. 6: 062016.
Since long time it's known that every enterprise or factory, to keep position on the market must constantly increase productivity, improve the quality of its product or introduce new features and functions or reduce costs production and after that decrease the price of the final product. Strong slogans: faster, better, cheaper they do not always go hand in hand. Today no one even dreaming about a spectacular jump by a dozen or several dozen percent. We are currently "fighting" by several percent and in some cases for percentage fractions that will allow you to stay ahead of the competition. The paper will attempt to identify the suitable parameters used to assess the quality and efficiency of production. Methods of measuring quality values and ways of reacting for their change. Their mutual dependencies and ranges of changes. Which parameters have a critical impact on the product and its suitability for sale.
K Niemiec; D Krenczyk. Selected quality indicators and methods of their measurement. IOP Conference Series: Materials Science and Engineering 2018, 400, 022039 .
AMA StyleK Niemiec, D Krenczyk. Selected quality indicators and methods of their measurement. IOP Conference Series: Materials Science and Engineering. 2018; 400 (2):022039.
Chicago/Turabian StyleK Niemiec; D Krenczyk. 2018. "Selected quality indicators and methods of their measurement." IOP Conference Series: Materials Science and Engineering 400, no. 2: 022039.
The growing popularity of IT tools and interest in the use of modelling and simulation tools in the era of modern manufacturing concepts development, such as the industry 4.0 implies increasing use of computer applications in management and manufacturing. In the problem addressed in this paper, an operation management tools and DES systems integration based on data-driven semi-automatic simulation models generation method is presented. The presented approach allows the use of simulation and visualization for rapid verification of production flow plans. The concept of semi-automated model generator based on data-driven is applied. The approach has been illustrated through examples of practical implementation of the proposed method using FlexSim simulation software.
D Krenczyk; W M Kempa; K Kalinowski; C Grabowik; Iwona Paprocka. Integration of manufacturing operations management tools and discrete event simulation. IOP Conference Series: Materials Science and Engineering 2018, 400, 022037 .
AMA StyleD Krenczyk, W M Kempa, K Kalinowski, C Grabowik, Iwona Paprocka. Integration of manufacturing operations management tools and discrete event simulation. IOP Conference Series: Materials Science and Engineering. 2018; 400 (2):022037.
Chicago/Turabian StyleD Krenczyk; W M Kempa; K Kalinowski; C Grabowik; Iwona Paprocka. 2018. "Integration of manufacturing operations management tools and discrete event simulation." IOP Conference Series: Materials Science and Engineering 400, no. 2: 022037.
Lately, a great deal of effort has been spent developing methods to generate robust schedules. The Multi-Objective Immune Algorithm (MOIA) is one of the methods dealing with an uncertainty [1]. The basic schedules, obtained by the MOIA, are modified using the rule of the Minimal Impact of Disturbed Operation on the Schedule (MIDOS) in order to generate predictive schedules. If the effect of a disruption is too large, in order to generate reactive schedules, the rule of the Minimal Impact of Rescheduled Operation on the Schedule (MIROS) is applied. This paper is the continuation of the searching process for an algorithm which achieves good quality basic schedules that influence on the quality of predictive and reactive schedules. In [2,3], the two immune algorithms are compared, the MOIA and Clonal Selection Algorithm (CSA) when investigating the influence of basic schedules on the obtainment of stable and robust schedules with the application of the MIDOS and MIROS. The two algorithms are applied for a multi criteria job shop scheduling problem. In this paper, the genetic algorithm (GA) is applied for the same scheduling problem and the achieved schedules are compared.
I Paprocka; C Grabowik; W M Kempa; D Krenczyk; K Kalinowski. The influence of algorithms for basic-schedule generation on the performance of predictive and reactive schedules. IOP Conference Series: Materials Science and Engineering 2018, 400, 022042 .
AMA StyleI Paprocka, C Grabowik, W M Kempa, D Krenczyk, K Kalinowski. The influence of algorithms for basic-schedule generation on the performance of predictive and reactive schedules. IOP Conference Series: Materials Science and Engineering. 2018; 400 (2):022042.
Chicago/Turabian StyleI Paprocka; C Grabowik; W M Kempa; D Krenczyk; K Kalinowski. 2018. "The influence of algorithms for basic-schedule generation on the performance of predictive and reactive schedules." IOP Conference Series: Materials Science and Engineering 400, no. 2: 022042.
Petri nets are a useful tool for the modeling and performance evaluation of discrete event systems. Literature reveals that the Petri Net models of real-world discrete event systems are most frequently event graphs (a subclass of Petri nets). Literature also reveals that there are some simple methods for the performance evaluation of event graphs. The general-purpose Petri Net simulator (GPenSIM) is a new simulator that runs on the MATLAB platform. GPenSIM provides a Petri net language, with which Petri net classes and extensions can be developed. GPenSIM also provides functions for performance analysis. Since real-world discrete event systems usually possess a large number of resources, the Petri net models of these systems tend to become huge. Activity-Oriented Petri Nets (AOPN) is an approach that reduces the size of the Petri nets. In addition to the simulator functions, GPenSIM also realizes the AOPN approach on the MATLAB platform. Thus, AOPN is an integral part of GPenSIM. As a running example, a flexible manufacturing system is firstly modeled as an event graph, and then the size of the model is reduced with the AOPN approach. The advantages of GPenSIM and AOPN are discussed in this paper.
Reggie Davidrajuh; Bozena Skolud; Damian Krenczyk. Performance Evaluation of Discrete Event Systems with GPenSIM. Computers 2018, 7, 8 .
AMA StyleReggie Davidrajuh, Bozena Skolud, Damian Krenczyk. Performance Evaluation of Discrete Event Systems with GPenSIM. Computers. 2018; 7 (1):8.
Chicago/Turabian StyleReggie Davidrajuh; Bozena Skolud; Damian Krenczyk. 2018. "Performance Evaluation of Discrete Event Systems with GPenSIM." Computers 7, no. 1: 8.
Performance evaluation of a manufacturing system is to find out some useful information about the behavior of the system. Usually, Petri nets are very useful tool for modeling and performance evaluation of manufacturing systems. Literature review reveals that the Petri net models of manufacturing systems are most frequently event graphs, a special type of Petri nets; in addition, they are strongly connected event graphs. Literature review also reveals that there are some simple yet powerful methods for performance evaluation of strongly connected event graphs. General-purpose Petri net simulator (GPenSIM) is a new tool for modeling, simulation, and performance analysis of discrete event systems. This paper is about implementation and application of event graphs in GPenSIM, for performance analysis of real-world manufacturing systems.
Reggie Davidrajuh; Bozena Skolud; Damian Krenczyk. GPenSIM for Performance Evaluation of Event Graphs. Recent Advances in Computational Mechanics and Simulations 2017, 289 -299.
AMA StyleReggie Davidrajuh, Bozena Skolud, Damian Krenczyk. GPenSIM for Performance Evaluation of Event Graphs. Recent Advances in Computational Mechanics and Simulations. 2017; ():289-299.
Chicago/Turabian StyleReggie Davidrajuh; Bozena Skolud; Damian Krenczyk. 2017. "GPenSIM for Performance Evaluation of Event Graphs." Recent Advances in Computational Mechanics and Simulations , no. : 289-299.