Academic articles

The History of Simulation Modeling


During the past half-century simulation has advanced as a tool of choice for operational systems analysis. The advances in technology have stimulated new products and new environments without software standards or methodological commonality. Each new simulation language or product offers its own unique set of features and capabilities. Yet these simulation products are the evolution of research, development, and application. In this paper we interpret the historical development of simulation modeling. In our view simulation modeling is that part of the simulation problem-solving process that focuses on the development of the model. It is the interpretation of a real production (or service) problem in terms of a simulation language capable of performing a simulation of that real-world process. While “interpretation” is in the “eyes of the beholder” (namely us) there are some historical viewpoints and methods that influence the design of the simulation model.

Simulation of maintenance strategies in mechanized tunneling


Mechanized tunneling is one of the most common methods used for underground constructions for infrastructure systems. Since a tunnel boring machine (TBM) represents a non-redundant single machine system, the efficiency of maintenance work highly impacts the overall project performance. The wear and tear of cutting tools is a critical, but mostly unknown process. To plan the maintenance work of cutting tools efficiently, it is necessary to know the current tool conditions and adapt the planned maintenance strategies to the actual status accordingly. In this paper, an existing theoretical empiric surrogate model to describe cutting tool conditions will be used and implemented as a software component within a process simulation tool that manages TBM steering parameters. Further, different maintenance setups for TBM cutting tools are presented and evaluated. To prove the capability of the presented approach, a case study will show the effects that improved maintenance work can have on project performance.

Towards airspace rules for future UAS-based delivery


The growth of the nascent UAS industry will be affected by the airspace coordination rules between drones because these rules can impact business profitability. Few analyses have been reported to support design of commercial UAS operations in low-altitude commercial urban airspace. Analysis of minimum horizontal separation is critical for designing safe and efficient UAS delivery systems. In this paper a constructive simulation model is used to analyze and evaluate proposed UAS airspace traffic. A high density of delivery drones could create a bottleneck in a drone-based supply chain very quickly, especially when a high minimum horizontal separation standard is required. This paper proposes a simple idea on how to organize low-altitude UAS traffic, and evaluates the idea using a simulation model. Additional implications and future work needed in relation to UAS-based delivery are also discussed.

Analysis of future UAS-based delivery


Commercial use of Unmanned Aerial System (UAS) has the potential to reshape the delivery market and to open new business opportunities to small businesses, e.g., local stores, pharmacies, restaurants, as well as to large international and national businesses and government entities, e.g., Amazon, Google, UPS, power companies, and USPS. Simulation models can examine the value added to current business operations, the effects of radical shifts in current operations, and the formation of new types of businesses. This paper presents an envisioned future UAS delivery business operation models and develops a theoretical constructive simulation model. The conducted simulation analysis based on full factorial design estimated causalities between multiple independent and dependent business and policy factors e.g. drone velocity, flying altitude, number of drones, delivery demand, route type, maximum drone fly-time, number of orders completed, time average drone density, order time, drone utilization, and reachability of customers.

From desktop to large-scale Model Exploration with Swift/T


As high-performance computing resources have become increasingly available, new modes of computational processing and experimentation have become possible. This tutorial presents the Extreme-scale Model Exploration with Swift/T (EMEWS) framework for combining existing capabilities for model exploration approaches (e.g., model calibration, metaheuristics, data assimilation) and simulations (or any “black box” application code) with the Swift/T parallel scripting language to run scientific workflows on a variety of computing resources, from desktop to academic clusters to Top 500 level supercomputers. We will present a number of use-cases, starting with a simple agent-based model parameter sweep, and ending with a complex adaptive parameter space exploration workflow coordinating ensembles of distributed simulations. The use-cases are published on a public repository for interested parties to download and run on their own.

An Object-oriented Process Flow Approach to ARGESIM Comparisons "Flexible Assembly System" with AnyLogic


Simulator: AnyLogic is an object-orientated, general-purpose simulator for discrete, continuous and hybrid applications. It supports modelling with UML – RT and the underlying modelling technology is based on Java. Since Version 4.5 AnyLogic provides different advanced libraries as the Enterprise Library which implements often used discrete model object classes like sources, conveyors, and sinks.  Model: As the Comparison addresses the possibility to define and combine submodels, the objectoriented approach of AnyLogic, using the Enterprise Library, seems natural. The model consists of eight stations connected by some conveyors (all predefined in the Enterprise Library). 

An Object-oriented Hybrid Approach to ARGESIM Comparison "Crane and Embedded Control" with AnyLogic


Simulator: AnyLogic is a general-purpose simulation environment for discrete, continuous and hybrid systems. It employs UML-RT structure diagrams for building hierarchical models in object-oriented way and hybrid statecharts for behaviour specification. The generated model is Java and can be extended with user’s Java code. The simulation engine handles discrete events and dynamically changing sets of algebraic-differential equations. It automatically detects “change” (or “state”) events. Debugging and visualization facilities are present.

Supply Chain Management with Anylogic 4.0


Simulator: AnyLogic is a general- purpose simulator for discrete but also for continuous and hybrid applications. The modelling technology of AnyLogic is based on Java so that building simulation models using AnyLogic should be easy for experienced programmers. Model: According to the task of the comparison there are three Active Object Classes. The customer class corresponds to the wholesaler; the wholesaler class corresponds to the distributor class and to the factory class. In addition there is built a Message class that represents the movable goods as well as the orderings in the supply chain.

Creating and Running Mobile Agents with XJ DOME


XJ DOME is a set of tools and techniques for those who wish to speed up development of Distributed COM applications and improve their quality. DOME supports graphical modeling, code generation, simulation, deployment, monitoring and management. The simulation mode enables the developer to simulate the entire distributed application in virtual time on a single machine. After simulation step the application can be deployed onto the target network and managed via DOME Application Viewer. During run-time DOME platform enables the developer to collect and watch statistics, inspect threads and synchronization objects, view logs. DOME platform supports building of mobile agent systems on top of DCOM services. It provides for agent migration and employs DCOM security.