Digital Twins (DTs) are intended to be utilized for a wide range of applications, promising benefits like visualization, monitoring, simulation and control of a physical system. Not only the development of a DT for a production facility is a time-consuming task, but also to keep the virtual counterpart up to date in the use phase. In this work, the implementation of an industrial-scale DT of an automotive supplier production site based on a Discrete-Event Simulation (DES) model with self-optimization capabilities for easier maintainability and increased simulation accuracy is presented.

Engineered products have economic, environmental, and social impacts, which comprise the major dimensions of sustainability. This paper seeks to explore interactions between design parameters when social impacts are incorporated into the concept development phase of the systems design process. Social impact evaluation is increasing in importance similar to what has happened in recent years with environmental impact consideration in the design of engineered products. Concurrently, research into new airship design has increased. Airships have yet to be reintroduced at a large scale or for a range of applications in society. Although airships have the potential for positive environmental and economic impacts, the social impacts are still rarely considered. This paper presents a case study of the hypothetical introduction of airships in the Amazon region of Brazil to help local farmers transport their produce to market. It explores the design space in terms of both engineering parameters and social impacts using a discrete-event simulation to model the system. The social impacts are found to be dependent not only on the social factors and airship design parameters but also on the farmer-airship system, suggesting that socio-technical systems design will benefit from integrated social impact metric analysis.

Boundaries between products and services vanish and companies increasingly offer hybrid solutions known as Product Service Systems (PSS). Thereby, a holistic view that includes both the product and service parts must be taken. This paper presents a discrete-event simulation of a PSS as a method for the specification of the product part. Besides product and service, the application scenario and the decision-making are also modeled. Based on the results for a case study, the customer requirements could be guaranteed as the essential product specifications were optimized to minimize total costs.

This 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.

Objectives: The aim of this study was to estimate thresholds for production volume, durability, and cost of care for the cost-effective adoption of liver organ replacement technologies (ORTs).

Methods: We constructed a discrete-event simulation model of the liver allocation system in the United States. The model was calibrated against UNOS data (1994–2000). Into this model, we introduced ORTs with varying durability (time to failure), cost of care, and production volume. Primary outputs of interest were time to 5 percent reduction in the waiting list and time to 5 percent increase in expected transplant volume.

Results: Model output for both calibration and validation phases closely matched published data: waiting list length (±2 percent), number of transplants (±2 percent), deaths while waiting (±5 percent), and time to transplant (±11 percent). Reducing the waiting list was dependent on both ORT durability and production volume. The longer the durability, the less production volume needed to reduce the waiting list and vice versa. However, below 250 ORT/year, durability needed to be >2 years for any significant change to be seen in the waiting list. For base-case costs, all ORT production volume and durability scenarios result in more transplants per year at less total cost of care/patient than the current system. ORTs remain cost saving until manufacturing costs are >5 times base-case costs, production is less 500 ORT/year, and durability <6 months.

Conclusions: Although there remain many technical challenges to overcome, as long as ORTs can meet these threshold criteria, they have the potential of transforming the world of end-stage liver disease.