![anylogic agent based modeling tutorial anylogic agent based modeling tutorial](https://i.ytimg.com/vi/Wv_XIcoZKfo/maxresdefault.jpg)
Completing these tasks quickly required the vehicles to cooperate and share information about the environment.Īll the vehicles had sensors that could detect the environment, gather information about the things around them, and share their knowledge with other agents. The vehicles detected obstacles, found, and refueled capsules in the area. It was the easiest way to represent multiple interacting virtual vehicles with a variety of capabilities and have them all operate simultaneously. To assess the performance of automated vehicles and to evaluate algorithms and task sharing between the vehicles, SwRI engineers decided to build an agent-based AnyLogic model of vehicles’ operations in an enclosed area with random obstacles. Collaborative map of the area based on agents’ explorations The implementation of such systems can take a lot of time and money, so the engineers at SwRI decided to use simulation modeling to explore the possibilities of autonomous vehicles. This technology would primarily be used by military forces for the transportation of supplies to the fields of operation, demining, reconnaissance operations, and many other areas where humans can be replaced by machines for their own safety. According to this idea, vehicles would communicate in a distributed manner with each other, share information about their current location and environment, and make decisions on further actions based on this information themselves. These automated systems no longer need human drivers to control them on their missions, whether it is reconnaissance, hauling, or simply transportation.īut SwRI’s engineers didn’t want to stop there and decided to make autonomous vehicles free, not only from the driver, but also from a control center.
![anylogic agent based modeling tutorial anylogic agent based modeling tutorial](https://anylogic.help/anylogic/system-dynamics/images/housingSector.png)
![anylogic agent based modeling tutorial anylogic agent based modeling tutorial](https://www.anylogic.com/upload/medialibrary/3dc/3dc616d3b446abd2b0fb12c0960f0e26.jpg)
SwRI has been working in this field since 2006 and has designed systems for a semi-truck, Ford Explorer, many military platforms, and a wide variety of unmanned aerial vehicles (UAVs), commonly known as drones.
![anylogic agent based modeling tutorial anylogic agent based modeling tutorial](https://www.anylogic.com/upload/medialibrary/c28/c281a151d659da0fa3d8fc72222dc4b1.jpg)
One of the institute’s research areas is automated driving systems. Their contract engineering efforts benefit government, industry, and the public through the application of science and technology. SwRI is also a leader in fuel and energy efficiency, geosciences, turbomachinery, and energy storage. AnyLogic is based on Java and the Eclipse framework that make it possess of outstanding open and compatibility, and its script language is Java too, which brings sufficient flexibility and enables the user to capture the complexity and heterogeneity of business, economic and social systems at any desirable level of detail.Southwest Research Institute (SwRI) has gained worldwide attention by leading NASA missions such as the New Horizons mission to Pluto and the Juno mission to Jupiter. We simulate the container terminal handling and scheduling system on an advanced dynamic simulation platform AnyLogic 6.5.0. Finally a series of singlevessel simulations on handling and transportation are designed, implemented, performed, evaluated and analyzed, which validate the feasibility and creditability of the systematic methodology effectively. A new agile, efficient and robust compound modeling and scheduling methodology for CTLS is obtained consequently. In this paper, the handling, stacking and transportation in CTLS are regarded as a kind of generalized computing and compared with the working in general computer systems, whereupon the Harvard architecture and AnyLogic agent-based computing paradigm are fused to model the operational processing of CTLS, and the kernel thoughts in computer organization, architecture and operating system are introduced into CTLS to support and evaluate container terminal planning, scheduling and decision-making. As the highly complex logistics system, container terminal logistics systems (CTLS) play an increasingly important role in modern international logistics, and therefore their scheduling and decision-making process of much significance to the operation and competitiveness of harbors.