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Applied Sciences
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Modeling, Simulation, Operation and Control of Discrete Event Systems
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Modeling, Simulation, Operation and Control of Discrete Event Systems

A special issue of Applied Sciences (ISSN 2076-3417).

Deadline for manuscript submissions: closed (31 August 2017) | Viewed by 103153

Special Issue Editors

Prof. Dr. Zhiwu Li

E-Mail Website
Guest Editor
Institute of Systems Engineering, Macau University of Science and Technology, Taipa, Macao
Interests: discrete event system; petri net theory and application; control and scheduling of production systems; data mining and granular computing
Special Issues, Collections and Topics in MDPI journals
Dr. Mengchu Zhou

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA
Interests: Petri nets; intelligent automation; Internet of Things; big data; web services; intelligent
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Naiqi Wu

E-Mail Website
Guest Editor
Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and Systems, Macau University of Science and Technology, Macao 999078, China
Interests: intelligent manufacturing; discrete event systems, and petri net theory and applications; production planning, scheduling and control; intelligent logistics and transportation; energy systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yisheng Huang

E-Mail Website
Guest Editor
Department of Electrical Engineering, National IIan University, Yilan City, Taiwan
Interests: petri nets and discrete event systems in transportation; traffic flow modeling and control; communication and control for railroad networks; intelligent transportation systems

Special Issue Information

Dear Colleagues,

Information and computer technologies provide the spur to the burgeoning man-made and highly automated systems where discrete events are a dominant trait, take place frequently, and play an essential role in their operation and management. Nowadays, discrete event dynamic systems, as a natural abstraction of various contemporary technological applications, include intelligent urban traffic systems, automated flexible manufacturing systems, computer networks, communication protocols, logistic systems, monitoring and control of large buildings, scientific and business workflows, distributed databases, and concurrent software systems. The modeling, simulation, operation, and control of discrete event systems are the primary issues to be investigated. It is of paramount significance and importance to develop novel formal frameworks, analysis techniques, design tools, testing methods, and systematic control and optimization procedures for these kinds of man-made, highly complex systems; this is critical for their development and survivability. This Special Issue aims to address the present challenging crux of discrete event systems, such as supervisory control, deadlock analysis, optimal scheduling, resource management, performance evaluation, system identification, and fault diagnosis.

Prof. Dr. Mengchu Zhou
Prof. Dr. Naiqi Wu
Prof. Dr. Yisheng Huang
Prof. Dr. Zhiwu Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Modeling, simulation, and control of discrete event systems
  • Supervisory control theory and implementation techniques
  • Petri net theory and applications
  • Deadlock prevention, avoidance, and recovery
  • Performance analysis of discrete event systems
  • Deadlock-free scheduling and real-time control
  • Identification, fault diagnosis and opacity enforcement of discrete event systems
  • Petri nets and discrete event systems for smart cites and intelligent transportation
  • Scheduling and optimization of discrete and hybrid systems
  • Energy-efficient supply chain management and event-triggered control

Published Papers (20 papers)

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Editorial

Jump to: Research

4 pages, 174 KiB  
Editorial
Special Issue on Modeling, Simulation, Operation and Control of Discrete Event Systems
by Zhiwu Li, Mengchu Zhou, Naiqi Wu and Yi-sheng Huang
Appl. Sci. 2018, 8(2), 202; https://0-doi-org.brum.beds.ac.uk/10.3390/app8020202 - 30 Jan 2018
Cited by 4 | Viewed by 2692
Abstract
Information and computer technologies provide the spur to burgeoning man-made, highly automated systems.[...] Full article
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)

Research

Jump to: Editorial

834 KiB  
Article
Input–Output Finite Time Stabilization of Time-Varying Impulsive Positive Hybrid Systems under MDADT
by Lihong Yao and Junmin Li
Appl. Sci. 2017, 7(11), 1187; https://0-doi-org.brum.beds.ac.uk/10.3390/app7111187 - 17 Nov 2017
Cited by 7 | Viewed by 3396
Abstract
Time-varying impulsive positive hybrid systems based on finite state machines (FSMs) are considered in this paper, and the concept of input–output finite time stability (IO-FTS) is extended for this type of hybrid system. The IO-FTS analysis of the single linear time-varying system is [...] Read more.
Time-varying impulsive positive hybrid systems based on finite state machines (FSMs) are considered in this paper, and the concept of input–output finite time stability (IO-FTS) is extended for this type of hybrid system. The IO-FTS analysis of the single linear time-varying system is given first. Then, the sufficient conditions of IO-FTS for hybrid systems are proposed via the mode-dependent average dwell time (MDADT) technique. Moreover, the output feedback controller which can stabilize the non-autonomous hybrid systems is derived, and the obtained results are presented in a linear programming form. Finally, a numerical example is provided to show the theoretical results. Full article
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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1973 KiB  
Article
A Lookahead Behavior Model for Multi-Agent Hybrid Simulation
by Mei Yang, Yong Peng, Ru-Sheng Ju, Xiao Xu, Quan-Jun Yin and Ke-Di Huang
Appl. Sci. 2017, 7(10), 1095; https://0-doi-org.brum.beds.ac.uk/10.3390/app7101095 - 24 Oct 2017
Cited by 3 | Viewed by 4055
Abstract
In the military field, multi-agent simulation (MAS) plays an important role in studying wars statistically. For a military simulation system, which involves large-scale entities and generates a very large number of interactions during the runtime, the issue of how to improve the running [...] Read more.
In the military field, multi-agent simulation (MAS) plays an important role in studying wars statistically. For a military simulation system, which involves large-scale entities and generates a very large number of interactions during the runtime, the issue of how to improve the running efficiency is of great concern for researchers. Current solutions mainly use hybrid simulation to gain fewer updates and synchronizations, where some important continuous models are maintained implicitly to keep the system dynamics, and partial resynchronization (PR) is chosen as the preferable state update mechanism. However, problems, such as resynchronization interval selection and cyclic dependency, remain unsolved in PR, which easily lead to low update efficiency and infinite looping of the state update process. To address these problems, this paper proposes a lookahead behavior model (LBM) to implement a PR-based hybrid simulation. In LBM, a minimal safe time window is used to predict the interactions between implicit models, upon which the resynchronization interval can be efficiently determined. Moreover, the LBM gives an estimated state value in the lookahead process so as to break the state-dependent cycle. The simulation results show that, compared with traditional mechanisms, LBM requires fewer updates and synchronizations. Full article
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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1370 KiB  
Article
Heuristic Method for Decision-Making in Common Scheduling Problems
by Edyta Kucharska
Appl. Sci. 2017, 7(10), 1073; https://0-doi-org.brum.beds.ac.uk/10.3390/app7101073 - 17 Oct 2017
Cited by 6 | Viewed by 3796
Abstract
The aim of the paper is to present a heuristic method for decision-making regarding an NP-hard scheduling problem with limitations related to tasks and the resources dependent on the current state of the process. The presented approach is based on the algebraic-logical meta-model [...] Read more.
The aim of the paper is to present a heuristic method for decision-making regarding an NP-hard scheduling problem with limitations related to tasks and the resources dependent on the current state of the process. The presented approach is based on the algebraic-logical meta-model (ALMM), which enables making collective decisions in successive process stages, not separately for individual objects or executors. Moreover, taking into account the limitations of the problem, it involves constructing only an acceptable solution and significantly reduces the amount of calculations. A general algorithm based on the presented method is composed of the following elements: preliminary analysis of the problem, techniques for the choice of decision at a given state, the pruning non-perspective trajectory, selection technique of the initial state for the trajectory final part, and the trajectory generation parameters modification. The paper includes applications of the presented approach to scheduling problems on unrelated parallel machines with a deadline and machine setup time dependent on the process state, where the relationship between tasks is defined by the graph. The article also presents the results of computational experiments. Full article
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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6473 KiB  
Article
Accelerated Simulation of Discrete Event Dynamic Systems via a Multi-Fidelity Modeling Framework
by Seon Han Choi, Kyung-Min Seo and Tag Gon Kim
Appl. Sci. 2017, 7(10), 1056; https://0-doi-org.brum.beds.ac.uk/10.3390/app7101056 - 13 Oct 2017
Cited by 15 | Viewed by 6879
Abstract
Simulation analysis has been performed for simulation experiments of all possible input combinations as a “what-if” analysis, which causes the simulation to be extremely time-consuming. To resolve this problem, this paper proposes a multi-fidelity modeling framework for enhancing simulation speed while minimizing simulation [...] Read more.
Simulation analysis has been performed for simulation experiments of all possible input combinations as a “what-if” analysis, which causes the simulation to be extremely time-consuming. To resolve this problem, this paper proposes a multi-fidelity modeling framework for enhancing simulation speed while minimizing simulation accuracy loss. A target system for this framework is a discrete event dynamic system. The dynamic property of the system facilitates the development of variable fidelity models for the target system due to its high computational cost; and the discrete event property allows for determining when to change the fidelity within a simulation scenario. For formal representation, the paper defines several key concepts such as an interest region, a fidelity change condition, and a selection model. These concepts are integrated into the framework to allow for the achievement of a condition-based disjunction of high- and low-fidelity simulations within a scenario. The proposed framework is applied to two case studies: unmanned underwater and urban transportation vehicles. The results show that simulation speed increases at least 1.21 times with a 5% accuracy loss. We expect that the proposed framework will resolve a computationally expensive problem in the simulation analysis of discrete event dynamic systems. Full article
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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404 KiB  
Article
Dynamic Scheduling of a Semiconductor Production Line Based on a Composite Rule Set
by Yumin Ma, Fei Qiao, Fu Zhao and John W. Sutherland
Appl. Sci. 2017, 7(10), 1052; https://0-doi-org.brum.beds.ac.uk/10.3390/app7101052 - 13 Oct 2017
Cited by 24 | Viewed by 4743
Abstract
Various factors and constraints should be considered when developing a manufacturing production schedule, and such a schedule is often based on rules. This paper develops a composite dispatching rule based on heuristic rules that comprehensively consider various factors in a semiconductor production line. [...] Read more.
Various factors and constraints should be considered when developing a manufacturing production schedule, and such a schedule is often based on rules. This paper develops a composite dispatching rule based on heuristic rules that comprehensively consider various factors in a semiconductor production line. The composite rule is obtained by exploring various states of a semiconductor production line (machine status, queue size, etc.), where such indicators as makespan and equipment efficiency are used to judge performance. A model of the response surface, as a function of key variables, is then developed to find the optimized parameters of a composite rule for various production states. Furthermore, dynamic scheduling of semiconductor manufacturing is studied based on support vector regression (SVR). This approach dynamically obtains a composite dispatching rule (i.e., parameters of the composite dispatching rule) that can be used to optimize production performance according to real-time production line state. Following optimization, the proposed dynamic scheduling approach is tested in a real semiconductor production line to validate the effectiveness of the proposed composite rule set. Full article
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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749 KiB  
Article
Deterministic and Robust Optimization Approach for Single Artillery Unit Fire Scheduling Problem
by Yong Baek Choi, Suk Ho Jin and Kyung Sup Kim
Appl. Sci. 2017, 7(10), 1038; https://0-doi-org.brum.beds.ac.uk/10.3390/app7101038 - 11 Oct 2017
Cited by 3 | Viewed by 3245
Abstract
In this study, deterministic and robust optimization models for single artillery unit fire scheduling are developed to minimize the total enemy threat to friendly forces by considering the enemy target threat level, enemy target destruction time, and target firing preparation time simultaneously. Many [...] Read more.
In this study, deterministic and robust optimization models for single artillery unit fire scheduling are developed to minimize the total enemy threat to friendly forces by considering the enemy target threat level, enemy target destruction time, and target firing preparation time simultaneously. Many factors in war environments are uncertain. In particular, it is difficult to evaluate the threat levels of enemy targets definitively. We consider the threat level of an enemy target to be an uncertain parameter and propose a robust optimization model that minimizes the total enemy threat to friendly forces. The robust optimization model represents a semi-infinite problem that has infinitely many constraints. Therefore, we reformulate the robust optimization model into a tractable robust counterpart formulation with a finite number of constraints. In the robust counterpart formulation with cardinality-constrained uncertainty, the conservativeness and robustness of the solution can be adjusted with an uncertainty degree, Γ. Further, numerical experiments are conducted to verify that the robust counterpart formulation with cardinality-constrained uncertainty can be made equivalent to the deterministic optimization model and the robust counterpart formulation with box uncertainty by setting Γ accordingly. Full article
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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957 KiB  
Article
Strategies to Automatically Derive a Process Model from a Configurable Process Model Based on Event Data
by Mauricio Arriagada-Benítez, Marcos Sepúlveda, Jorge Munoz-Gama and Joos C. A. M. Buijs
Appl. Sci. 2017, 7(10), 1023; https://0-doi-org.brum.beds.ac.uk/10.3390/app7101023 - 04 Oct 2017
Cited by 22 | Viewed by 4167
Abstract
Configurable process models are frequently used to represent business workflows and other discrete event systems among different branches of large organizations: they unify commonalities shared by all branches and describe their differences, at the same time. The configuration of such models is usually [...] Read more.
Configurable process models are frequently used to represent business workflows and other discrete event systems among different branches of large organizations: they unify commonalities shared by all branches and describe their differences, at the same time. The configuration of such models is usually done manually, which is challenging. On the one hand, when the number of configurable nodes in the configurable process model grows, the size of the search space increases exponentially. On the other hand, the person performing the configuration may lack the holistic perspective to make the right choice for all configurable nodes at the same time, since choices influence each other. Nowadays, information systems that support the execution of business processes create event data reflecting how processes are performed. In this article, we propose three strategies (based on exhaustive search, genetic algorithms and a greedy heuristic) that use event data to automatically derive a process model from a configurable process model that better represents the characteristics of the process in a specific branch. These strategies have been implemented in our proposed framework and tested in both business-like event logs as recorded in a higher educational enterprise resource planning system and a real case scenario involving a set of Dutch municipalities. Full article
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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2870 KiB  
Article
Decision Support Simulation Method for Process Improvement of Intermittent Production Systems
by Péter Tamás
Appl. Sci. 2017, 7(9), 950; https://0-doi-org.brum.beds.ac.uk/10.3390/app7090950 - 15 Sep 2017
Cited by 14 | Viewed by 6292
Abstract
Nowadays production system processes are undergoing sweeping changes. The trends include an increase in the number of product variants to be produced, as well as the reduction of the production’s lead time. These trends were induced by new devices of the industry’s 4.0, [...] Read more.
Nowadays production system processes are undergoing sweeping changes. The trends include an increase in the number of product variants to be produced, as well as the reduction of the production’s lead time. These trends were induced by new devices of the industry’s 4.0, namely the Internet of Things and cyber physical systems. The companies have been applying intermittent production systems (job production, batch production) because of the increase in the number of product variants. Consequently, increasing the efficiency of these systems has become especially important. The aim of development in the long term—not achievable in many cases—is the realization of unique production, with mass production’s productivity and lower cost. The improvement of complex production systems can be realized efficiently only through simulation modeling. A standardized simulation method for intermittent production systems has not been elaborated so far. In this paper, I introduce a simulation method for system improvement and present its application possibilities and a practical example. Full article
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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3630 KiB  
Article
Reliability-Based and Cost-Oriented Product Optimization Integrating Fuzzy Reasoning Petri Nets, Interval Expert Evaluation and Cultural-Based DMOPSO Using Crowding Distance Sorting
by Zhaoxi Hong, Yixiong Feng, Zhongkai Li, Guangdong Tian and Jianrong Tan
Appl. Sci. 2017, 7(8), 791; https://0-doi-org.brum.beds.ac.uk/10.3390/app7080791 - 04 Aug 2017
Cited by 16 | Viewed by 4155
Abstract
In reliability-based and cost-oriented product optimization, the target product reliability is apportioned to subsystems or components to achieve the maximum reliability and minimum cost. Main challenges to conducting such optimization design lie in how to simultaneously consider subsystem division, uncertain evaluation provided by [...] Read more.
In reliability-based and cost-oriented product optimization, the target product reliability is apportioned to subsystems or components to achieve the maximum reliability and minimum cost. Main challenges to conducting such optimization design lie in how to simultaneously consider subsystem division, uncertain evaluation provided by experts for essential factors, and dynamic propagation of product failure. To overcome these problems, a reliability-based and cost-oriented product optimization method integrating fuzzy reasoning Petri net (FRPN), interval expert evaluation and cultural-based dynamic multi-objective particle swarm optimization (DMOPSO) using crowding distance sorting is proposed in this paper. Subsystem division is performed based on failure decoupling, and then subsystem weights are calculated with FRPN reflecting dynamic and uncertain failure propagation, as well as interval expert evaluation considering six essential factors. A mathematical model of reliability-based and cost-oriented product optimization is established, and the cultural-based DMOPSO with crowding distance sorting is utilized to obtain the optimized design scheme. The efficiency and effectiveness of the proposed method are demonstrated by the numerical example of the optimization design for a computer numerically controlled (CNC) machine tool. Full article
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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3598 KiB  
Article
An Improved Dispatching Method (a-HPDB) for Automated Material Handling System with Active Rolling Belt for 450 mm Wafer Fabrication
by Chia-Nan Wang, Hsien-Pin Hsu and Van-Vinh Tran
Appl. Sci. 2017, 7(8), 780; https://0-doi-org.brum.beds.ac.uk/10.3390/app7080780 - 31 Jul 2017
Cited by 5 | Viewed by 4728
Abstract
The semiconductor industry is facing the transition from 300 mm to 450 mm wafer fabrication. Due to the increased size and weight, 450 mm wafers will pose unprecedented challenges on semiconductor wafer fabrication. To better handle and transport 450 mm wafers, an advanced [...] Read more.
The semiconductor industry is facing the transition from 300 mm to 450 mm wafer fabrication. Due to the increased size and weight, 450 mm wafers will pose unprecedented challenges on semiconductor wafer fabrication. To better handle and transport 450 mm wafers, an advanced Automated Material Handling System (AMHS) is definitely required. Though conveyor-based AMHS is expected to be suitable for 450 mm wafer fabrication, still it faces two main problems, traffic-jam problem and lot-prioritization. To address the two problems, in this research we have proposed an improved dispatching method, termed Heuristic Preemptive Dispatching Method using Activated Roller Belt (a-HPDB). We have developed some effective rules for the a-HPDB based on Activated Roller Belt (ARB). In addition, we have conducted experiments to investigate its effectiveness. Compared with the HPDB and R-HPD, two dispatching rules proposed in previous studies, our experimental results showed the a-HPDB had a better performance in terms of average lot delivery time (ALDT). For hot lots and normal lots, the a-HPDB had advantages of 4.14% and 8.92% over the HPDB and advantages of 4.89% and 8.52% over R-HPD, respectively. Full article
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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4650 KiB  
Article
Performance Analysis of a Forecasting Relocation Model for One-Way Carsharing
by Ganjar Alfian, Jongtae Rhee, Muhammad Fazal Ijaz, Muhammad Syafrudin and Norma Latif Fitriyani
Appl. Sci. 2017, 7(6), 598; https://0-doi-org.brum.beds.ac.uk/10.3390/app7060598 - 09 Jun 2017
Cited by 18 | Viewed by 5946
Abstract
A carsharing service can be seen as a transport alternative between private and public transport that enables a group of people to share vehicles based at certain stations. The advanced carsharing service, one-way carsharing, enables customers to return the car to another station. [...] Read more.
A carsharing service can be seen as a transport alternative between private and public transport that enables a group of people to share vehicles based at certain stations. The advanced carsharing service, one-way carsharing, enables customers to return the car to another station. However, one-way implementation generates an imbalanced distribution of cars in each station. Thus, this paper proposes forecasting relocation to solve car distribution imbalances for one-way carsharing services. A discrete event simulation model was developed to help evaluate the proposed model performance. A real case dataset was used to find the best simulation result. The results provide a clear insight into the impact of forecasting relocation on high system utilization and the reservation acceptance ratio compared to traditional relocation methods. Full article
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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2470 KiB  
Article
Scheduling of Crude Oil Operations in Refinery without Sufficient Charging Tanks Using Petri Nets
by Yan An, NaiQi Wu, Chi Tin Hon and ZhiWu Li
Appl. Sci. 2017, 7(6), 564; https://0-doi-org.brum.beds.ac.uk/10.3390/app7060564 - 30 May 2017
Cited by 7 | Viewed by 4997
Abstract
A short-term schedule for crude oil operations in a refinery should define and sequence the activities in detail. Each activity involves both discrete-event and continuous variables. The combinatorial nature of the scheduling problem makes it difficult to solve. For such a scheduling problem, [...] Read more.
A short-term schedule for crude oil operations in a refinery should define and sequence the activities in detail. Each activity involves both discrete-event and continuous variables. The combinatorial nature of the scheduling problem makes it difficult to solve. For such a scheduling problem, charging tanks are a type of critical resources. If the number of charging tanks is not sufficient, the scheduling problem is further complicated. This work conducts a study on the scheduling problem of crude oil operations without sufficient charging tanks. In this case, to make a refinery able to operate, a charging tank has to be in simultaneous charging and feeding to a distiller for some time, called simultaneously-charging-and-feeding (SCF) mode, leading to disturbance to the oil distillation in distillers. A hybrid Petri net model is developed to describe the behavior of the system. Then, a scheduling method is proposed to find a schedule such that the SCF mode is minimally used. It is computationally efficient. An industrial case study is given to demonstrate the obtained results. Full article
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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2357 KiB  
Article
A Program Model of Fuzzy Interpreted Petri Net to Control Discrete Event Systems
by Michał Markiewicz and Lesław Gniewek
Appl. Sci. 2017, 7(4), 422; https://0-doi-org.brum.beds.ac.uk/10.3390/app7040422 - 22 Apr 2017
Cited by 9 | Viewed by 4514
Abstract
Using Petri nets (PNs) to control discrete event systems (DES) has many benefits, because of their graphical representations, the possibility of parallel process control, and their formal descriptions. Amongst the different PNs that are applied for this purpose, most have some limitations for [...] Read more.
Using Petri nets (PNs) to control discrete event systems (DES) has many benefits, because of their graphical representations, the possibility of parallel process control, and their formal descriptions. Amongst the different PNs that are applied for this purpose, most have some limitations for visualization. For many of these PNs, another restriction is the length of time between the creation of the control algorithm in the form of a graph and its practical implementation. These two issues can be resolved with one solution called fuzzy interpreted PN (FIPN). This article proposes the use of a program model based on FIPN to control DES and the method for generation of this model using the graphical representation of the net. FIPN offers a better visualization in comparison to discrete PNs and it allows for the quick creation of program code through the application of a simulator called FIPN-SML. This computer tool implements a method that transforms the graphical form of FIPN into Structured Text (ST) language supported by the IEC 61131-3. Full article
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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3133 KiB  
Article
FMS Scheduling under Availability Constraint with Supervisor Based on Timed Petri Nets
by Mohamed Ali Kammoun, Wajih Ezzeddine, Nidhal Rezg and Zied Achour
Appl. Sci. 2017, 7(4), 399; https://0-doi-org.brum.beds.ac.uk/10.3390/app7040399 - 15 Apr 2017
Cited by 12 | Viewed by 4743
Abstract
This paper proposes an optimal solution to large-scale Flexible Manufacturing System (FMS) scheduling problems under availability constraints based on Timed Petri Nets (TPNs). First a decomposition method of TPNs is proposed, then a mathematical model is derived based on their properties. The mathematical [...] Read more.
This paper proposes an optimal solution to large-scale Flexible Manufacturing System (FMS) scheduling problems under availability constraints based on Timed Petri Nets (TPNs). First a decomposition method of TPNs is proposed, then a mathematical model is derived based on their properties. The mathematical model is built to determine the optimal firing sequence of TPN transitions to minimize the total manufacturing time. The resulting firing sequence of TPN transitions is used to generate the manufacturing system supervisor operated by TPN and digital controllers. Several numerical examples and comparative studies are provided in this paper in order to prove the new approach’s efficiency. Full article
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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2283 KiB  
Article
A Timed Colored Petri Net Simulation-Based Self-Adaptive Collaboration Method for Production-Logistics Systems
by Zhengang Guo, Yingfeng Zhang, Xibin Zhao and Xiaoyu Song
Appl. Sci. 2017, 7(3), 235; https://0-doi-org.brum.beds.ac.uk/10.3390/app7030235 - 01 Mar 2017
Cited by 41 | Viewed by 7720
Abstract
Complex and customized manufacturing requires a high level of collaboration between production and logistics in a flexible production system. With the widespread use of Internet of Things technology in manufacturing, a great amount of real-time and multi-source manufacturing data and logistics data is [...] Read more.
Complex and customized manufacturing requires a high level of collaboration between production and logistics in a flexible production system. With the widespread use of Internet of Things technology in manufacturing, a great amount of real-time and multi-source manufacturing data and logistics data is created, that can be used to perform production-logistics collaboration. To solve the aforementioned problems, this paper proposes a timed colored Petri net simulation-based self-adaptive collaboration method for Internet of Things-enabled production-logistics systems. The method combines the schedule of token sequences in the timed colored Petri net with real-time status of key production and logistics equipment. The key equipment is made ‘smart’ to actively publish or request logistics tasks. An integrated framework based on a cloud service platform is introduced to provide the basis for self-adaptive collaboration of production-logistics systems. A simulation experiment is conducted by using colored Petri nets (CPN) Tools to validate the performance and applicability of the proposed method. Computational experiments demonstrate that the proposed method outperforms the event-driven method in terms of reductions of waiting time, makespan, and electricity consumption. This proposed method is also applicable to other manufacturing systems to implement production-logistics collaboration. Full article
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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918 KiB  
Article
Modeling and Solving the Three Seaside Operational Problems Using an Object-Oriented and Timed Predicate/Transition Net
by Hsien-Pin Hsu, Chia-Nan Wang, Chien-Chang Chou, Ying Lee and Yuan-Feng Wen
Appl. Sci. 2017, 7(3), 218; https://0-doi-org.brum.beds.ac.uk/10.3390/app7030218 - 24 Feb 2017
Cited by 12 | Viewed by 3959
Abstract
Container terminals (CTs) play an essential role in the global transportation system. To deal with growing container shipments, a CT needs to better solve the three essential seaside operational problems; berth allocation problem (BAP), quay crane assignment problem (QCAP), and quay crane scheduling [...] Read more.
Container terminals (CTs) play an essential role in the global transportation system. To deal with growing container shipments, a CT needs to better solve the three essential seaside operational problems; berth allocation problem (BAP), quay crane assignment problem (QCAP), and quay crane scheduling problem (QCSP), which affect the performance of a CT considerably. In past studies, the three seaside operational problems have often been solved individually or partially, which is likely to result in poor overall system performance. However, solving the three seaside operational problems simultaneously is in fact a very complicated task. In this research, we dealt with the three seaside operational problems at the same time by using a novel high-level Petri net, termed an Object-Oriented and Timed Predicate/Transition Net (OOTPr/Tr net). After defining the three seaside operational problems formally, we integrated them as a three-level framework that was further transformed into an OOTPr/Tr net model. Then, using the Prolog programming language, we implemented this model as a simulation tool to find the best solution based on the various combinations of heuristic rules used. Full article
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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Article
Modeling, Simulation, and Performance Analysis of Decoy State Enabled Quantum Key Distribution Systems
by Logan O. Mailloux, Michael R. Grimaila, Douglas D. Hodson, Ryan Engle, Colin McLaughlin and Gerald Baumgartner
Appl. Sci. 2017, 7(2), 212; https://0-doi-org.brum.beds.ac.uk/10.3390/app7020212 - 22 Feb 2017
Cited by 6 | Viewed by 7390
Abstract
Quantum Key Distribution (QKD) systems exploit the laws of quantum mechanics to generate secure keying material for cryptographic purposes. To date, several commercially viable decoy state enabled QKD systems have been successfully demonstrated and show promise for high-security applications such as banking, government, [...] Read more.
Quantum Key Distribution (QKD) systems exploit the laws of quantum mechanics to generate secure keying material for cryptographic purposes. To date, several commercially viable decoy state enabled QKD systems have been successfully demonstrated and show promise for high-security applications such as banking, government, and military environments. In this work, a detailed performance analysis of decoy state enabled QKD systems is conducted through model and simulation of several common decoy state configurations. The results of this study uniquely demonstrate that the decoy state protocol can ensure Photon Number Splitting (PNS) attacks are detected with high confidence, while maximizing the system’s quantum throughput at no additional cost. Additionally, implementation security guidance is provided for QKD system developers and users. Full article
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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5680 KiB  
Article
Manufacturing Scheduling Using Colored Petri Nets and Reinforcement Learning
by Maria Drakaki and Panagiotis Tzionas
Appl. Sci. 2017, 7(2), 136; https://0-doi-org.brum.beds.ac.uk/10.3390/app7020136 - 03 Feb 2017
Cited by 42 | Viewed by 8986
Abstract
Agent-based intelligent manufacturing control systems are capable to efficiently respond and adapt to environmental changes. Manufacturing system adaptation and evolution can be addressed with learning mechanisms that increase the intelligence of agents. In this paper a manufacturing scheduling method is presented based on [...] Read more.
Agent-based intelligent manufacturing control systems are capable to efficiently respond and adapt to environmental changes. Manufacturing system adaptation and evolution can be addressed with learning mechanisms that increase the intelligence of agents. In this paper a manufacturing scheduling method is presented based on Timed Colored Petri Nets (CTPNs) and reinforcement learning (RL). CTPNs model the manufacturing system and implement the scheduling. In the search for an optimal solution a scheduling agent uses RL and in particular the Q-learning algorithm. A warehouse order-picking scheduling is presented as a case study to illustrate the method. The proposed scheduling method is compared to existing methods. Simulation and state space results are used to evaluate performance and identify system properties. Full article
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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3811 KiB  
Article
A Genetic Regulatory Network-Based Method for Dynamic Hybrid Flow Shop Scheduling with Uncertain Processing Times
by Youlong Lv, Jie Zhang and Wei Qin
Appl. Sci. 2017, 7(1), 23; https://0-doi-org.brum.beds.ac.uk/10.3390/app7010023 - 04 Jan 2017
Cited by 9 | Viewed by 4899
Abstract
The hybrid flow shop is a typical discrete manufacturing system. A novel method is proposed to solve the shop scheduling problem featured with uncertain processing times. The rolling horizon strategy is adopted to evaluate the difference between a predictive plan and the actual [...] Read more.
The hybrid flow shop is a typical discrete manufacturing system. A novel method is proposed to solve the shop scheduling problem featured with uncertain processing times. The rolling horizon strategy is adopted to evaluate the difference between a predictive plan and the actual production process in terms of job delivery time. The genetic regulatory network-based rescheduling algorithm revises the remaining plan if the difference is beyond a specific tolerance. In this algorithm, decision variables within the rolling horizon are represented by genes in the network. The constraints and certain rescheduling rules are described by regulation equations between genes. The rescheduling solutions are generated from expression procedures of gene states, in which the regulation equations convert some genes to the expressed state and determine decision variable values according to gene states. Based on above representations, the objective of minimizing makespan is realized by optimizing regulatory parameters in regulation equations. The effectiveness of this network-based method over other ones is demonstrated through a series of benchmark tests and an application case collected from a printed circuit board assembly shop. Full article
(This article belongs to the Special Issue Modeling, Simulation, Operation and Control of Discrete Event Systems)
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