Discrete Event System Simulation Research Articles

Blood supply chain operation considering lifetime and transshipment under uncertain environment

According to the characteristics of blood inventory control problem under the condition of blood shortage, the dynamic decision-making problem of blood supply chain is investigated in this paper. Firstly, based on the recursive equation, the state transition equations of two categories of blood demand under two inventory issue strategies (FIFO and LIFO) are given. The mathematical expressions of key indexes such as blood outdating and blood shortage are obtained. A blood collection decision-making method based on EWA (Estimated Withdrawal & Aging) strategy is proposed. Then, an optimal model of blood transshipment problem is established with the goal of the shortest transshipment time and the maximum freshness of the transported blood. In addition, an allocation planning model with multiple priority requirements and fairness concerns is established to achieve the best fairness and the minimum shortage. Besides, a discrete event system simulation (DESS) framework is designed according to the characteristics of the model. Finally, the effectiveness of the decision-making method and EWA inventory strategy are verified by numerical simulation. The results show that safety stock, target stock level and fluctuation range of demand have significant impacts on the control effect of blood inventory.

Supply Chain Performance Analysis Using Hybrid Overall Equipment Effectiveness and Discrete Event Simulation (Case Study: Futura Energy Nusantara)

Futura Energy Nusantara is the first manufacturer of lithium-ion battery cell production in Indonesia. Because highly demand, the company must fill the order and increase production Productivity is one of the most fundamental and important determinants of system production. Productivity measurement help identify the problems and find solutions to improve system performance. OEE (Overall Equipment Effectiveness) is one method used for effective time manufacturing systems related to the existence of equipment in the production system. The Element of OEE is Availability Rate (A), Performance Efficiency (P), and Quality Rate (Q). The study aims to test through the simulation production system in Lithium-Ion Batteries (LIB), by considering factor OEE, with discrete event simulation system. As a result, this study is a model to applicable, for the company to improve Productivity

Design of Distributed Discrete-Event Simulation Systems Using Deep Belief Networks

In this research study, we investigate the ability of deep learning neural networks to provide a mapping between features of a parallel distributed discrete-event simulation (PDDES) system (software and hardware) to a time synchronization scheme to optimize speedup performance. We use deep belief networks (DBNs). DBNs, which due to their multiple layers with feature detectors at the lower layers and a supervised scheme at the higher layers, can provide nonlinear mappings. The mapping mechanism works by considering simulation constructs, hardware, and software intricacies such as simulation objects, concurrency, iterations, routines, and messaging rates with a particular importance level based on a cognitive approach. The result of the mapping is a synchronization scheme such as breathing time buckets, breathing time warp, and time warp to optimize speedup. The simulation-optimization technique outlined in this research study is unique. This new methodology could be realized within the current parallel and distributed simulation modeling systems to enhance performance.

Study of emerging air taxi network operation using discrete-event systems simulation approach

This research focuses on understanding the air taxi operations to determine the number of air taxis required to fulfill the demand for urban air mobility in New York City (NYC). We leverage the Define, Measure, Analyze, Design, and Verify (DMADV) framework and integrate it with the systems simulation approach. Upon investigation, we find that all the parameters linearly impact the vehicle utilization, while other measures are robust, specifically with respect to the seating capacity. It is also recommended to operate initially with 70 air taxis in NYC to achieve a trade-off between customer wait time and vehicle utilization. The proposed approach can act as a recommender system for air taxi companies.

Simulation-based planning system for shipbuilding

ABSTRACT To maintain the competitiveness of shipyards in the current, difficult situation, further improvements to technology are necessary. Recently, various production technologies have been developed to advance the shipyard production environment under the influence of the Industry 4.0 toward automation, smart factories, and intelligent planning systems. To contribute to such efforts, we introduce a research case aimed at a simulation-based shipbuilding planning system. Shipbuilding planning processes are reorganized using an integrated planning and scheduling system, and a process-centric discrete event system simulation is used to enhance planning quality. Moreover, the proposed simulation-based planning system is applied to an actual shipbuilding process, proving that it could enhance the quality of production planning through several productivity evaluation indices.

Discrete Event Systems Specifications Modelling and Simulation of Wireless Networking Applications

ABSTRACT We discuss the use of formal discrete-event modelling and simulation for networking applications, in particular, for mobile networks and Wireless Sensor Networks (WSN). We show how one can develop discrete-event model libraries based on the DEVS formalism for mobile networks, allowing tracking the users’ upload status in a given area of coverage, while using a non-cooperative algorithm or Coordinated Multipoint (CoMP) synchronisation. We show a DEVS model library for CoMP-based networks, showing how control messaging can be managed among Evolved Node Base stations (eNBs). We build a case study comparing two CoMP approaches: Joint Processing (JP) and Coordinated Scheduling (CS). Finally, we show how DEVS models can be used to model malware propagation in wireless sensor networks based on epidemic theory.

Adaptive Discrete Event Simulation Systems to Embrace Changes of Requirements Using Event Control Models

During the development or deployment of discrete event simulation systems, many sudden changes in requirements emerge. To embrace the changes rapidly and reduce development costs, attaching reusable, and black-boxed components to existing systems has been regarded as one of the most effective approaches because modifications at the code level are generally costly and risky. However, since this approach requires components that are an exact fit, it may not be easy to avoid the modifications at the source code level actually. Moreover, the required components may not exist. Hence, this paper applies black-box extensibility to simulation systems in order to avoid the modifications. This paper proposes adaptive discrete event simulation systems using event control models for extending and modifying semantics through event-based simulation interface. With the proposed work, the simulation events are modulated, deleted, and generated by the event-oriented control functions in the event control model to embrace the changes of requirements. It can substitute for the modifications at the code level and extend the existing behavioral semantics. As a result, the proposed work provides a new alternative step in the development process with reusable components to avoid modifications at the source code level. The new step will lead to rapid adaptations of existing simulation systems. To support the effectiveness of this approach, this paper will describe applicable examples based on our empirical studies.

Mechanical System Design and Simulation Research of a Demonstration Assembly Line

Most assembly lines use a bottom-up mechanical system design approach. In view of the problems of poor system interchangeability and low product versatility, a battery assembly line was designed based on the modular design method. The linear modules, cylinders and linear guides were used as the basic elements to build the production line modules. Different assembly requirements have improved the performance of the entire production line. The discrete-event system simulation method is used to simulate the quasi-running process of the production line, analyze the bottleneck of the production line, and improve the production efficiency of each module of the production line by changing the optimization method of the loading link and the production sequencing.

Modeling, simulation, estimation and boundedness analysis of discrete event systems

In this paper, a particular state model allowing describing the system evolution in time is proposed. This state model contains two inequalities describing the evolution in time of the system state and input. The system simulation is based on the resolution of this state model. After that, a state estimator is proposed in order to estimate the whole system state and inputs. The state model and the observer are both proposed following count and dater approaches successively. In this work, the considered state is the number of transition firing if a count approach is followed and the dates of firing if the dater approach is considered. It is proved, using an illustrative example, that the proposed observer estimates well the system state by comparing the simulated and the estimated states. A boundedness analysis of the system trajectory is proposed in the case of FCF Petri nets. This analysis is based on an algorithm which gives the bounded transitions knowing the system input. Some particular tables are elaborated to describe the proposed algorithm. Theses tables give the bounded transitions after each iteration of the algorithm.

Integrating fuzzy case-based reasoning and discrete-event simulation to develop a decision support system for part-fixture assignment and fixture flow control

PurposeThis study aims to propose a decision support system (DSS) that performs a decision-based part-fixture assignment and fixture flow control in planned production periods.Design/methodology/approachThe principal approaches were fuzzy case-based reasoning (FCBR) and discrete-event simulation (DES). Besides, the fuzzy analytic hierarchy process (FAHP), an object-oriented (OO) method and a fuzzy weighted Euclidean distance were used to support the decision-making process.FindingsIt shows that integrating FCBR and DES systems is a promising approach to address part-fixture planning problems. The FCBR subsystem proposed various stable numbers of fixtures as scenarios. The DES model analyzed the future performances of these scenarios and identified the best alternative.Research limitations/implicationsThe DSS was tested in laboratory environments using a numerical analysis; however, it was not validated in industrial situations.Originality/valueThe synergy of integrating FCBR and DES systems was not exploited in the past in part-fixture assignment and fixture flow control problems.

NB-DEVS: a hybrid approach for modelling and simulation of imperfect systems

This article presents the NB-DEVS approach for the modelling and simulation of discrete event systems (DES), our objectives are to propose a new formal approach for describing the complex systems involving imperfectly. In our research we propose to integrate the naïve Bayesian network (NB) into DEVS formalism. The association of these two formalisms we have allow to extend the classic DEVS formalism to new domains. This new approach permits to study the system when its behaviour is uncertain. Finally, we simulated the evolution of the production system using our NB-DEVS approach. In simulation, we used several different models to replicate the empirical data. From these simulation iterations, we were able to verify and validate our model.

NB-DEVS: a hybrid approach for modelling and simulation of imperfect systems

This article presents the NB-DEVS approach for the modelling and simulation of discrete event systems (DES), our objectives are to propose a new formal approach for describing the complex systems involving imperfectly. In our research we propose to integrate the naive Bayesian network (NB) into DEVS formalism. The association of these two formalisms we have allow to extend the classic DEVS formalism to new domains. This new approach permits to study the system when its behaviour is uncertain. Finally, we simulated the evolution of the production system using our NB-DEVS approach. In simulation, we used several different models to replicate the empirical data. From these simulation iterations, we were able to verify and validate our model.

Productivity Improvement Through Multi-Objective Simulation Optimization—A Case Study

To maximize profitability and meet customers’ expectations, assembly plants strive to enhance operations and minimize costs. Using discrete-event system simulation and optimization, this paper investigates different scenarios to enhance operations efficiency and presents a decision support tool to aid operational decision making in an air conditioner assembly plant. Specifically, simulation is used to evaluate the system-wide impact of an optimal sequencing rule at a bottleneck station; in addition, a multi-objective simulation optimization problem is solved to enable manufacturers to evaluate their time/cost tradeoffs. Using actual data from a previous month, the simulation study illustrates possible savings throughout the evaluated alternatives. To sustain the study outcomes, we combine mathematical programming and simulation into an integrated decision support tool. Overall, this study illustrates the benefits of the solution approach to a real-life system.

Energy-Oriented Scheduling for Hybrid Flow Shop With Limited Buffers Through Efficient Multi-Objective Optimization

Efficient scheduling benefits productivity promotion, cost savings, and customer satisfaction. In recent years, with a growing concern about the energy price and environmental impact, energy-oriented scheduling is going to be a key issue for sustainable manufacturing. In this paper, we investigate an energy-oriented scheduling problem deriving from the hybrid flow shop with limited buffers. First, we formulate the scheduling problem with a mixed integer linear programming (MILP) model, which considers two objectives including minimizing the total weighted tardiness (TWT) and non-processing energy (NPE). To solve the NP-hard problem in the strong sense, we develop an efficient multi-objective optimization algorithm under the framework of the multi-objective objective evolutionary algorithm based on decomposition (MOEA/D). We devise a job-permutation vector to represent the scheduling solution and cover its search space. Since NPE is a non-regular function, we develop a two-pass decoding procedure composed of a discrete-event system (DES) simulation procedure and a greedily post-shift procedure. Besides, we apply an external archive population (EAP) to guide the algorithm to converge on a Pareto frontier and a local search procedure to enhance the diversity of the population. Finally, we conduct extensive computational experiments to verify the effectiveness of the proposed energy-oriented multi-objective optimization (EOMO) algorithm. The results presented in this paper may be useful for future research on energy-oriented scheduling problems in realistic production systems.

Using a decision tree algorithm to predict the robustness of a transshipment schedule

Maritime ports are complex systems where any decision may affect the interests of many different stakeholders. New technologies are transforming the maritime transport sector, improving economic efficiency, optimizing the systems and operations of logistics management and enhancing connectivity. One of the problems that can benefit from the use of new technologies is finding the best transshipment schedule for incoming vessels.In this paper, we propose using automatic learning techniques to obtain a predictor of the robustness of transshipment schedules proposed by a scheduler. This measure of the goodness of the loading/unloading plan will consider the uncertainty to which processes in ports are subject, due to the weather, personnel skills, and the number of resources available, among other factors. Specifically, in this paper we propose using the so-called ‘decision tree’ algorithm to create a model for predicting the goodness of the proposed transshipment schedule. To obtain the historical data needed to train the machine-learning algorithm, we resorted to discrete event systems simulation, which can be used to test, for the same schedule, various scenarios with different disturbances. This yielded a model that predicts the behavior of the transshipment schedule based on the actual situation at the port.

Creating an Alternative Production Line by Using a Simulation Technique in Duvet Cover Production

In this study, the discrete-event system simulation technique was used in order to create smooth work flow on a duvet cover production line. In accordance with this purpose, a model of the work flow of a duvet cover was created, and input data was collected by means of a time study in order to determine the statistical distribution of all operations using Stat-fit for Simul8 software. The model translation phase was executed in Simul8 Software. Then for the purpose of the verification and validation process, actual system data and simulation model outputs were compared statistically using the normality test and Mann-Whitney non-parametric test in Minitab Software. Once the simulation model of the actual system was properly validated, an alternative model considering fewer operators was generated in order to acquire more output and have a smoother line balance. The alternative model was compared with main one by considering the output rate per operator.

SPECIAL ISSUE: Modelling, Simulation, and Control of Discrete Event Systems

SPECIAL ISSUE: Modelling, Simulation, and Control of Discrete Event Systems

Modeling, Simulation and Verification of Probabilistic Reconfigurable Discrete-Event Systems Under Energy and Memory Constraints

Adaptive probabilistic systems have ability to modify their behaviors to cope with unpredictable significant changes at run-time such as component failures or resources depletion. Reconfiguration is often a major task for systems, i.e., it may violate the memory usage, the required energy and the concerned real-time constraints. It might also make the system’s functions unavailable for some time and make potential harm to human life or large financial investments. Thus, updating a system with any new configuration requires that the post-reconfigurable system fully satisfies the related constraints. Formal modeling and verification could avoid the resources and constraints violation. Thus, the paper proposes a new formalism to specify such systems. We also develop a new visual environment named ZIZO which helps in modeling, constructive simulation and CTL-based verification of adaptive probabilistic systems. The contributions are used to guarantee the resources and correctness of an IPv4 ZeroConf protocol.

Special Issue on Modeling, Simulation, Operation and Control of Discrete Event Systems

Information and computer technologies provide the spur to burgeoning man-made, highly automated systems.[...]

A generic framework for optimizing performance metrics by tuning parameters of clustering protocols in WSNs

Wireless sensor network (WSN) is a key technology trend in emerging internet of things paradigms which are commonly used for application areas such as smart-cities, smart-grids, wearables, and connected health. There is a wealth of literature which considers various cluster-based routing protocols such as LEACH, HEED, and UHEED where these protocols are compared in terms of the network lifetime and/or the total number of packets successfully received by the base station under various operational conditions. While existing studies present various approaches to form WSN clusters in the most efficient way, various parameters are manually-assigned their values such as the radius of the cluster, the number of nodes in the cluster, and the number of clusters that should be formed to reach the base station. The choice of correct parameters is essential for reaching the most efficient configuration, however existing studies do not specify a systematic way for tuning these parameters. In other words, the optimization of cluster-based WSNs through fine tuning of related system parameters is not considered in the existing studies. We believe that presenting a generic approach to tune the parameters of clustering algorithms in order to optimize the performance metrics of WSNs is a significant contribution. In this study a systematic and an efficient method is presented to tune the parameters of clustering and routing protocols. Instead of brute force, or trial and error approaches, simulated annealing and K-beams algorithms are adopted together with discrete event system simulator OMNET++ with Castalia Framework. Results are presented comparatively with brute force approach in order to show the efficiency of the new approach in finding the optimum configuration in terms of energy efficiency as well as the rate of successfully received packets.