China
Africa
Explore chapters and articles related to this topic
Comparison of Sensor-Embedded Closed-Loop Supply Chain Systems with Regular Systems
Published in Eren Özceylan, Surendra M. Gupta, Sustainable Production and Logistics, 2021
Mehmet Talha Dulman, Surendra M. Gupta, Tetsuo Yamada
A discrete event simulation was used to model the systems. The discrete event simulation software used in this study was Arena 14.7 (Kelton et al., 2007). Extreme input values were used in testing the outcomes of the models, and the models were verified using this method. The models were validated by plotting some of the performance measures and observing them through simulation runs. The run length of the models was 4,000 days. This was sufficient to cover the expected life span of the products (i.e. seven years for dishwashers and ten years for dryers). In addition to the expected life spans, the run length was sufficient to complete the EOL processes. A total of 64 experiments were carried out for both systems. Additional data was required to run the models, and this is presented in Appendix A.
Simulation Techniques
Published in Harry G. Perros, An Introduction to IoT Analytics, 2021
There are two major categories of simulation depending upon the system that is being modeled: discrete-event and continuous event. Discrete-event simulation is used to model systems whose state takes discrete values. For instance, let us assume that we want to simulate the queueing of IoT requests in a server. The state of this system is discrete, because it is depicted by the number n of requests queueing for service, n = 0, 1, 2, …, which is a discrete variable. Discrete-event simulation is used to model systems such as IoT systems, computer networks, production systems, etc. Continuous-event simulation, on the other hand, is totally different to discrete-event simulation and it is based on differential equations. It is used for systems whose state is a continuous variable, such as the position in the trajectory of a rocket. In this chapter, we deal with discrete-event simulation techniques.
An operational data based framework for longwall shearer performance measurement
Published in Christoph Mueller, Winfred Assibey-Bonsu, Ernest Baafi, Christoph Dauber, Chris Doran, Marek Jerzy Jaszczuk, Oleg Nagovitsyn, Mining Goes Digital, 2019
Discrete event simulation is based on modelling of a system having stochastic, dynamic and discrete processes. Stochastic processes have uncertainties that can be defined by statistical distributions. The statistical structure of the system leads to a variety of outputs in every simulation run. In fact, statistical data analysis and the determination of replication number can be considered as an important part of the simulation study. Dynamic processes define the evolution of the model according to the time whereas in discrete processes, change in the system occurs in discrete points of time (Banks et al. 2010). Models in discrete event simulation predict the outputs of the stochastic processes (Y) according to the system parameters (p) and inputs (x) (Que et al. 2016). () [Y1,Y2,…Yi]=f[p1,p2,…pjk,x1,x1,…xjm]
Cellular manufacturing design 1996–2021: a review and introduction to applications of Industry 4.0
Published in International Journal of Production Research, 2023
Roohollah YounesSinaki, Azadeh Sadeghi, Hadi Mosadegh, Najat Almasarwah, Gursel Suer
To measure potential benefits of implementing industry 4.0 in reconfigurable CMS, digital twin technology can be used. Digital twins build generic simulation models to evaluate the performance of the system. Among different simulation methods, discrete event simulation is capable of investigating different optimisation scenarios obtained from real-time optimisation. Agent-based simulation models evaluate the interaction of holons in digital twins. Figure 5 in Section 3.7.1 indicates only 6% of the reviewed papers used simulation models. By adapting Industry 4.0, applying simulation models is expected to increase. Reviewing the literature indicates optimisation and artificial intelligence algorithms and simulation models are mainly applied for minor modifications such as self-scheduling in manufacturing systems. By the advancement of the mentioned algorithms and development of software and hardware requirements to store and process big data, it is expected that smart decision-making more frequently leads to fundamental changes such as manufacturing layout configuration.
Introducing Lean practices through simulation: A case study in an Italian SME
Published in Quality Management Journal, 2023
Stefano Frecassetti, Bassel Kassem, Kaustav Kundu, Matteo Ferrazzi, Alberto Portioli-Staudacher
The combined use of case studies and simulations has led to the study of these countless benefits. Through the collection of data from a real case, we were able, through the use of Simulation, to enrich and consolidate the analysis, thanks to the countless advantages given by this methodology. Firstly, discrete-event Simulation allows the dynamic analysis of production systems to identify possible improvements in the “AS-IS” state and the possibility of introducing improvements in future "TO-BE" scenarios. We have better understood the system in its initial phase through the Simulation. Moreover, the great advantage of the Simulation is being able to analyze possible solutions for future implementations without disrupting the environment of a company. Thanks to the Simulation, it is possible to validate possible solutions in a time-lapse and avoid investing money in solutions without having the certainty that they could carry concrete advantages. One of the fundamental advantages of Simulation is that it can tolerate much less restrictive modeling assumptions, unlike exact analytical methods that rely on more restrictive assumptions.
Offshore platform for containerized cargo redistribution: a new concept and simulation-based performance study
Published in Maritime Policy & Management, 2021
Xinhu Cao, Shuhong Wang, Chenhao Zhou, Ning Wu
The simulation model is developed by using ARENA, a commercial simulation software for discrete event simulation modelling. The simulation logic is illustrated in Figures 7–9, which can be used as a guide to build an Arena model. In this paper, ‘container ship arrival’ is modelled by a ‘Create’ module, ‘queue in somewhere’ is modelled by a ‘Hold’ module, ‘If condition is met’ utilizes the ‘Decide’ module, ‘filling process’ is modelled by a ‘Process’ module, and ‘Release container and terminate simulation’ is based on a ‘Dispose’ module. Note that as ARENA is following entity driven concept, which means that each entity will execute the process one by one. To control the container flow, multiple ‘Hold’ and ‘Decide’ are needed. As the logics are general, the model can be developed by using other commercial simulation software, such as AutoMod as well.