scholarly journals Overall Equipment Effectiveness Analysis Using Discrete Event Simulation at Table Tennis Table Manufacturer

2020 ◽  
Vol 19 (2) ◽  
pp. 157
Author(s):  
Yuniaristanto Yuniaristanto ◽  
Iqbal Wahyu Saputra ◽  
Muhammad Hisjam
Keyword(s):  
Production Process ◽  
Discrete Event Simulation ◽  
Production Efficiency ◽  
Discrete Event ◽  
Table Tennis ◽  
Event Simulation ◽  
And Performance ◽  
Performance Rate ◽  
Production Losses ◽  
Improve Production Efficiency

To measure the performance of the production process, an efficiency calculation is performed using the Overall Equipment Effectiveness (OEE) method. OEE can measure various production losses and identify potential developments that can be carried out in a production process. This research is expected to be an input to improve production efficiency. The results of overall equipment effectiveness are then performed using Discrete Event Simulation, which built using STELLA Architect. The result shows that their overall equipment effectiveness scores are below the company goals, and performance rate is their lowest score. These simulation results are expected to be a basis for improvements in the production division, especially at Table Tennis Table Manufacturer.

An artificial neural network approach to discrete-event simulation

1995 ◽  
Vol 9 (1) ◽  
pp. 37-49 ◽  
Author(s):  
Ian Flood ◽  
Kenneth Worley
Keyword(s):  
Neural Network ◽  
Discrete Event Simulation ◽  
Network Architecture ◽  
Discrete Event ◽  
Network Approach ◽  
Neural Network Approach ◽  
Event Simulation ◽  
The Neural Network ◽  
Component Processes ◽  
And Performance

AbstractThis paper proposes and evaluates a neural network-based method for simulating manufacturing processes that exhibit both noncontinuous and stochastic behavior processes more conventionally modeled, using discrete-event simulation algorithms. The incentive for developing the technique is its potential for rapid execution of a simulation through parallel processing, and facilitation of the development and improvement of models particularly where there is limited theory describing the dependence between component processes. A brief introduction is provided to a radial-Gaussian neural network architecture and training process, the system adopted for the work presented in this paper. A description of the basic approach proposed for applying this technology to simulation is then described. This involves the use of a modularized neural network approach to model construction and the prediction of the occurrence of events using information retained from several previous states of the simulation. A class of earth-moving systems, comprising a push-dozer and a fleet of scrapers, is used as the basis for assessing the viability and performance of the proposed approach. A series of experiments show the neural network to be capable of both capturing the characteristic behavior and making an accurate prediction of production rates of scraper-based earth-moving systems. The paper concludes with an indication of some areas for further development and evaluation of the technique.

Applying Discrete Event Simulation (DES) in Healthcare

2015 ◽  
pp. 390-410
Author(s):  
Stavros T. Ponis ◽  
Angelos Delis ◽  
Sotiris P. Gayialis ◽  
Panagiotis Kasimatis ◽  
Joseph Tan
Keyword(s):  
Discrete Event Simulation ◽  
Capacity Planning ◽  
Planning Process ◽  
Discrete Event ◽  
Healthcare Facilities ◽  
Performance Improvements ◽  
Event Simulation ◽  
Health Insurance Provider ◽  
And Performance

This paper highlights the opportunities and challenges of applying Discrete Event Simulation (DES) to support capacity planning of a network of outpatient facilities. Despite an abundance of studies using simulation techniques to examine the operation and performance of outpatient clinics, the problem of capacity allocation and planning of medical services within a network of outpatient healthcare facilities appears to be underexplored. Here, a case study of a health insurance provider that operates a network of six outpatient medical facilities in the US is used to illustrate and explore the synthesizing and adaptive, yet parsimonious nature of using DES methodology for network design and capacity planning. Results of this case study demonstrate that significant performance improvements for the network operator can be achieved with applying DES method to support the network facility capacity planning process.

Modeling and Simulation of IEEE 802.11 Wireless LANs

2012 ◽  
pp. 85-99 ◽  
Author(s):  
Nurul I. Sarkar ◽  
Roger McHaney
Keyword(s):  
Modeling And Simulation ◽  
Ieee 802.11 ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Model Development ◽  
Local Area ◽  
Network Simulator ◽  
Event Simulation ◽  
802.11 Wireless Lans ◽  
And Performance

Stochastic discrete event simulation methodology is becoming increasingly popular among network researchers worldwide in recent years. This popularity results from the availability of various sophisticated and powerful simulation software packages, and also because of the flexibility in model construction and validation offered by simulation. In this chapter, the authors describe their experience in using the network simulator 2 (ns-2), a discrete event simulation package, as an aid to modeling and simulation of the IEEE 802.11 Wireless Local Area Networks (WLANs). This chapter provides an overview of ns-2 focusing on simulation environment, architecture, model development and parameter setting, model validation, output data collection and processing, and simulation execution. The strengths and weaknesses of ns-2 are discussed. This chapter also emphasizes that selecting a good simulator is crucial in modeling and performance analysis of wireless networks.

Improving the Organization of Waste Management Sites

2021 ◽  
Vol 12 (2) ◽  
pp. 58-71
Author(s):  
Elad Harison ◽  
Arkady Cherkassky ◽  
Ofer Barkai
Keyword(s):  
Waste Management ◽  
Population Growth ◽  
Discrete Event Simulation ◽  
Real World ◽  
Discrete Event ◽  
Arrival Times ◽  
Event Simulation ◽  
Broad Scope ◽  
Growth Changes ◽  
And Performance

Waste management (WM) sites operate a broad scope of processes and activities that involve treatment of industrial and private waste and its recycling and landfilling. The paper applies discrete event simulation (DES) methods to assess the performance of different configurations of WM sites, as means for improving their productivity, efficiency, and organization. The authors evaluate the functioning and performance of WM sites under various real-world scenarios, such as different waste arrival times, changes of the volume of waste due to population growth, changes in the shares of different recycled materials due to increasing awareness and public campaigns. The results of the model can serve policymakers, environmentalists, and WM site managers in utilizing their existing facilities to improve both productivity and recycling and thereby reduce waste.

scholarly journals A Capacity Planning through Discrete Event Simulation

Jurnal PASTI ◽  
2020 ◽  
Vol 14 (2) ◽  
pp. 146
Author(s):  
Arief Suharko
Keyword(s):  
Production Process ◽  
Discrete Event Simulation ◽  
Real World ◽  
Capacity Planning ◽  
Discrete Event ◽  
Model Construction ◽  
Strategic Decisions ◽  
Event Simulation ◽  
Run Time ◽  
Critical Resources

The capacity planning serves an important role in strategic decisions involving production facilities. While there are many publications made on capacity planning, most of the models created tend to restrict their applications in real-world due to some initial assumptions being made and/or the run-time execution of the models that may be prohibitive. The objective of this paper is to explore the model construction for in-plant truck movement in a cement company that is based on building a discrete-event simulation one so that the planning may be sufficiently robust while the amount of time for constructing the model and the run-time still serve practical purposes. The model then is used to examine the effects of shifting bottlenecking and thus, allows users to identify critical resources for the production process. The results show that such a model provides the directions and aids for the management to make the strategic decisions.

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