Discrete Event Simulation Projects Research Articles

Application of Risk Management for Discrete Event Simulation Projects in Healthcare Systems

Despite the recent developments of discrete event simulation (DES) techniques as well as the growing tendency for employing DES in health care quite often many DES projects still fail to implement successfully. According to several studies, the application of project risk management can reduce the incidence of DES project failure in health care however, far too little attention has been paid to this part. In this study, a risk management framework is provided for DES projects in health care order to reduce the occurrence of failure and increase the chance of success in the DES project. This work investigates both managerial and technical risks and tries to identify, evaluate, and control them by using a set of adopted methods. Emergency department seems to be the most popular area of DES projects in health care The proposed framework is applied to an emergency department that serves more than 23000 patients per year. The results of this investigation indicate that the risk management of the DES project not only provides a proactive approach for project managers to implement successfully but also improves scenario ranking process which is one of the main outputs for principal stakeholders.

Data quality problems in discrete event simulation of manufacturing operations

High-quality input data are a necessity for successful discrete event simulation (DES) applications, and there are available methodologies for data collection in DES projects. However, in contrast to standalone projects, using DES as a daily manufacturing engineering tool requires high-quality production data to be constantly available. In fact, there has been a major shift in the application of DES in manufacturing from production system design to daily operations, accompanied by a stream of research on automation of input data management and interoperability between data sources and simulation models. Unfortunately, this research stream rests on the assumption that the collected data are already of high quality, and there is a lack of in-depth understanding of simulation data quality problems from a practitioners’ perspective. Therefore, a multiple-case study within the automotive industry was used to provide empirical descriptions of simulation data quality problems, data production processes, and relations between these processes and simulation data quality problems. These empirical descriptions are necessary to extend the present knowledge on data quality in DES in a practical real-world manufacturing context, which is a prerequisite for developing practical solutions for solving data quality problems such as limited accessibility, lack of data on minor stoppages, and data sources not being designed for simulation. Further, the empirical and theoretical knowledge gained throughout the study was used to propose a set of practical guidelines that can support manufacturing companies in improving data quality in DES.

Scenario analysis of Brazilian soybean exports via discrete event simulation applied to soybean transportation: The case of Mato Grosso State

In 2015, Brazil exported 54.32millionmetrictons of soybeans, which represents 40.2% of the total world exports. Even with lower production costs, Brazil's competitive advantages are diminished due to difficulties with product distribution caused by problems in the logistical network. The objective of this study is to aid in strategic transport decision-making via a discrete event simulation project. The object of study is the logistical export system for soybeans for the state of Mato Grosso, the largest producer and exporter of soybeans in Brazil. The decision-making process is based on route costs and the availability of ports. Four different scenarios were simulated for the export system. At the end of the simulations performed for three months, and beyond simply comparing total costs, the results indicate that there is a need for the development and expansion of, primarily the Port of Vitória and the ports in the north of Brazil, as well as the multimodal logistics flow to these operational ports.

Mining discrete-event simulation projects: A chronological, comprehensive review from 2008 to 2015

Mining discrete-event simulation projects: A chronological, comprehensive review from 2008 to 2015

A review of open source discrete event simulation software for operations research

This article reviews the state of the art in Open Source (OS) Discrete Event Simulation (DES) software used for decision support in Operations Research (OR) focusing on the modelling of manufacturing, services, supply chain management and logistics. DES is arguably one of the most popular OR techniques. Commercial-Off-The-Shelf (COTS) DES software has been around for decades and it has considerably helped in the wider adoption and use of DES, providing the user with tools for modelling, debugging and experimentation. However, such tools often have limitations regarding their aspects such as cost, flexibility and reusability and OS DES seems to be a strong alternative in order to fill those gaps. The scope of our review is to present the state of the art in OS DES and identify future areas of research. The paper concludes with the desired features that an OS DES project should have in order to become a notable competitor against COTS equivalents in the OR domain.

Evaluation of the DESI interface for discrete event simulation input data management automation

Discrete event simulation (DES) is a powerful tool of modelling and analysis used in different disciplines. DES projects rely heavily on data in order to compute different parameters that drive the simulations. DES is usually performed on specialized DES software, which performs the simulations but does not prepare the data to set up the simulations. Professionals and practitioners who use DES agree that the preparation of data for DES projects is a highly manual process and is extremely time-consuming in relationship to the entire duration of DES projects. On average, 31% of DES projects time is devoted to input data management. This article presents an innovative user interface, called DESI, which collects and processes data for DES projects in an automated manner, producing results in real time, in response to intuitive user inputs. This article demonstrates that the DESI interface reduces the time devoted to input data management, addresses current gaps in DES input data management and because it does not rely on the previous existence of data, it can be used in different disciplines where DES modelling is appropriate.

Mapping of Time-Consumption During Input Data Management Activities

The success of a discrete event simulation project relies heavily on input data quality. In order to achieve high quality data a significant amount of time needs to be spent, either due to absence of data or problems with defining and extracting existing data from databases. This paper presents a distribution of the time-consumption for the activities in the input data phase during discrete event simulation projects. The results show where efforts need to be focused to reduce time-consumption and improve quality of input data management.