Academic articles

There has been an increased interest in resilient supplier selection in recent years, much of it focusing on forecasting the disruption probabilities. The results of this study advance our understanding about how and when machine learning and simulation can be combined to create digital supply chain twins, and through these twins improve resilience. The proposed data-driven decision-making model for resilient supplier selection can be further exploited for design of risk mitigation strategies in supply chain disruption management models, redesigning the supplier base or investing in most important and risky suppliers.

The purpose of this research is to investigate how electric vehicles can promote energy transition and how blockchain can facilitate the decentralisation of future energy systems.

With the slow but steady rollout of smart meters and advancements in internet of things (IoT) technology, and with the help of agent-based modeling, the results from this study will prove the worth of blockchain’s inclusion in the smart grids of the future.

In December 2019, there was a case of viral pneumonia in Wuhan. After confirming that the pathogen of this disease is a new coronavirus, the World Health Organization (WHO) confirmed and named it 2019-nCoV. The pneumonia caused by this pathogen infection is called a novel corona virus pneumonia.

To better understand the mode of transmission of 2019-nCoV among the population and the effects of control measures, the study was conducted using agent-based modeling (ABM) to simulate an interactive environment over a certain space-time range. The study simulates the trend of 2019-nCoV infection at different levels of close contact in order to provide relevant information and references.

Many developed countries of the world such as Japan, Korea, Singapore, Germany etc., are facing the issue of decreasing birth rates and increasing aged populations. One of the potential solutions for this problem is liberal immigration and refugee laws. Korea has stringent immigration laws and most of the immigration into the country is temporary in nature. However, we have witnessed exodus from Middle East countries to many European countries. Such a phenomenon could have lasting impacts on the host country. Following this cue, we built a demographic simulator for modeling the rapid influx of people seeking refuge in Korea. In this particular simulation test case, we observed the change in demographic distribution of Korea.

Increasing occurrence of natural disturbances such as windstorms and high snow cover as well as uncer-tainty according to queuing and lead times, bottlenecks, utilization, stock level, wagon and truck availability and machine breakdowns lead to supply chain risks and seasonal irregularities in wood harvest and transport. Innovative multimodal systems via rail terminals offer the potential to increase buffer capacity and reduce greenhouse gas emissions. Therefore, a train terminal is included in a new virtual environment spanning the whole wood supply chain and enabling manager involvement in testing, analysis and evalua-tion of a complex multimodal transport system. The simulation model facilitates carrying out experiments and scenario designs for strategy comparisons in workshops with supply chain managers and provides in-tuitive decision support by animation and a KPI-cockpit. Adapting collaborative supply chain control strat-egies in participatory simulation enhances the development of advanced risk management and therefore improves supply chain resilience, efficiency and sustainability.

This case study considers the simulation of a coal lading port in order to determine which extensions are needed based on expected capacity demands. These investigations are executed in cooperation with the German company TAKRAF GmbH which planned and constructed the considered port. Processes at this port are influenced by uncertainties, like the provided coal mix from mines and transportation times from mines to the port or meteorological disturbances. The maximum capacity of the current state of the port was determined at a first step. Components which mainly limit the maximum outcome were identified. Based on these results, different extension scenarios were evaluated.

A worldwide leading company in the cosmetic industry was dealing with great challenges regarding adapting its positioning strategy to the dynamically changing behaviors of the market. The company needed to decide where to invest its marketing budget to optimize its revenue and was using different traditional marketing mix models without any success. A marketing mix model was developed using agent-based modeling to predict the market’s reaction to a given distribution of a certain budget among the different touchpoints available. This innovative model uses market information and consumer information collected from surveys to estimate the company’s sales, level of awareness and level of consideration given its distributed investment. The tool was implemented as part of the marketing plan decision making process, providing the ability to test different scenarios and generate quantitative analysis of its results.

Simulation modelling has long been used as a decision support tool in the mining industry. This is typically done to address issues on the strategic time horizon, with a heavy focus on experimentation and sensitivity analysis. These issues include mining equipment selection, pit optimization, design and operation of the mine-plant interface, testing the robustness of a mine plan and blending.

Mining simulators can be used to forecast production in the short term to test the quality of truck dispatch decisions (allocation of trucks to loaders) and evaluate the value of alternate scheduling rules. It can also be used to produce a forecast of the likelihood of achieving a shift target and allow operators to test what-if options to reduce the risk of production loss or reduce costs by putting excess equipment on standby. Being able to make these decisions with confidence helps to drive improvements in operations efficiency.

We discuss the application of simulation to estimate a nominal, or target, processing times for work stations on a serial assembly line. The expectation is that having different processing times per station per product will increase the throughput of the line, compared to having a constant time for all stations. A demonstration case at ABB Robotics in Sweden will be presented. This is a small part in the “Process Automation for Discrete Manufacturing Excellence” project (PADME) involving five manufacturing industry partners and four research organizations, that aim at adapting Industrie 4.0 strategies and existing state-of-the-art technologies into new configurations, serving as a framework that can be used by similar industries.

In many curricula and degree programs, simulation courses are not required, but these tools and techniques could be beneficial to students preparing for a variety of careers. The current paper describes two examples of simulation education embedded into broader course topics from a development perspective. The examples, from courses generally described in this paper as Cloud Computing and Big Data, offer a recommended approach for exposing students to practical uses of simulation. In the first example, we use simulation techniques to develop a web service emulation response database within a cloud computing environment for software testing. In the second example, simulation techniques provide an approach to generate data sets for learning data analytics techniques.