SIMULATION: Transactions of The Society for Modeling and Simulation International

Riaz, Faisal; Niazi, Muaz A

doi: 10.1177/0037549717742203pmid: N/A

Amongst collisions, rear-end collisions are the deadliest. Several rear-end collision avoidance solutions have been proposed recently in the literature. A key problem with existing solutions is their dependence on precise mathematical models. However, real world driving is influenced by a number of nonlinear factors. These include road surface conditions, driver reaction time, pedestrian flow, and vehicle dynamics. These factors involve so many different variations that precise mathematical solutions are hard to obtain, if not impossible. This problem with precise control-based rear-end collision avoidance schemes has also previously been addressed using fuzzy logic, but the excessive number of fuzzy rules straightforwardly prejudices their efficiency. Furthermore, such fuzzy logic-based controllers have been proposed without the use of an appropriate modeling technique. One such modeling technique is agent-based modeling. This technique is suitable because it allows for mimicking the functions of an artificial human driver executing fuzzy rules. Keeping in view these limitations, we propose an enhanced emotion enabled cognitive agent (EEEC_Agent)-based controller. The proposed EEEC_Agent helps autonomous vehicles (AVs) avoid rear-end collisions with fewer rules. One key innovation in its design is to use the human emotion of fear. The resultant agent is very efficient and also uses the Ortony–Clore–Collins (OCC) model. The fear generation mechanism of EEEC_Agent is verified through NetLogo simulation. Furthermore, practical validation of EEEC_Agent functions is performed by using a specially built prototype AV platform. Finally, a qualitative comparison with existing state-of-the-art research works reflects that the proposed model outperforms recent research proposals.

Lee, Hyo-Cheol; Lee, Seok-Won

doi: 10.1177/0037549717753880pmid: N/A

Modeling and simulation are methods of validating new systems that are risky to be directly deployed in the real world. During the simulation, the simulation environment continuously changes and simulation objects correspondingly behave according to the changing situations. In general, modeling the behavior for all possible situations is extremely difficult when the rationale is unknown. Therefore, in order to adapt to the changing situation, it is important to recognize the rationale behind the behaviors of the simulation object. However, in many cases, even though the rationale is unknown or difficult to recognize, the simulation requires reasonable behaviors such as a commander’s decision in a war game simulation and a driver’s behavior in rush hours. In this study, we propose a new approach to determine the behavior of simulation objects under changing situations. The proposal is a unified learning approach that integrates two methods, data-driven and knowledge-driven approaches, which allow simulation objects to learn behavioral knowledge from experience as well as from domain experts performing the simulation and reuse verified knowledge. By combining both approaches, we supplement the shortcomings of one method with the strengths of the other. To verify our method, we apply the proposed approach to a military training simulation.

Jones, Thomas C; Hoops, Stefan; Watson, Layne T; Palmisano, Alida; Tyson, John J; Shaffer, Clifford A

doi: 10.1177/0037549717754121pmid: 31303682

The growing size and complexity of molecular network models makes them increasingly difficult to construct and understand. Modifying a model that consists of tens of reactions is no easy task. Attempting the same on a model containing hundreds of reactions can seem nearly impossible. We present the JigCell Model Connector, a software tool that supports large-scale molecular network modeling. Our approach to developing large models is to combine smaller models, making the result easier to comprehend. At the base, the smaller models (called modules) are defined by small collections of reactions. Modules connect together to form larger modules through clearly defined interfaces, called ports. In this work, we enhance the port concept by defining three types of ports. An output port is linked to an internal component that will send a value. An input port is linked to an internal component that will receive a value. An equivalence port is linked to an internal component that will both receive and send values. Not all modules connect together in the same way; therefore, multiple connection options need to exist.

Bokrantz, Jon; Skoogh, Anders; Lämkull, Dan; Hanna, Atieh; Perera, Terrence

doi: 10.1177/0037549717742954pmid: N/A

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.

Maleki, Hamed; Aghazadeh Shabestari, Aydin

doi: 10.1177/0037549717753293pmid: N/A

Continuous improvement in quality is the most important mantra for success in today’s competitive market. Previous studies indicated that although quality is the most important factor in gaining competitive superiority, increasing the level of quality alone cannot meet customers’ needs. One complementary approach to improving the level of quality to meet customers’ expectations is to lower the production costs and price of finished products. The quality circle as a path to achieve greater customer satisfaction is formed to identify the cost of quality and thus reduce this cost, which is a significant cost portion of the entire life cycle of products. This paper presents a case study in an electromotor manufacturing company. First, we build a mathematical model to allocate inspection stations to manufacturing processes and propose a heuristic approach to optimize it. Next, we use Enterprise Dynamic software for simulation. Finally, we compare these methods.

Yarmand, Hamed; Craft, David

doi: 10.1177/0037549718761108pmid: N/A

In radiation therapy, the main challenge is to deliver the dose to the tumor while sparing healthy tissues around the tumor. One important decision to make is the beam configuration. The corresponding mathematical problem, known as beam angle optimization (BAO), is a large-scale problem. We propose three novel heuristic approaches to reduce the computation time and find high-quality treatment plans for BAO. The first heuristic is based on the fact that the beams that are geometrically close to each other (i.e., ‘adjacent’ beams) have similar impacts, and hence are less likely to be used in the optimal configuration simultaneously. Therefore, in this heuristic, referred to as ‘neighbor cuts’, their use is limited. The second heuristic is to eliminate the beams with small contribution to dose delivery in the ideal plan when all candidate beams can be used. Finally, the number of beams is reduced in the third heuristic while ensuring the quality of the plan remains within a pre-specified range. These heuristics can be applied to any formulation for BAO for various external radiation therapy techniques. We evaluate these heuristics by applying them to a mixed integer programming (MIP) formulation of BAO for a phantom liver case and a clinical liver case.