SIMULATION: Transactions of The Society for Modeling and Simulation International

Hu, Xiaolin; Zeigler, Bernard P

doi: 10.1177/0037549711400778pmid: N/A

We present an activity-based framework that links information and energy. The activity-based framework uses a quantization-based approach for modeling information processing and defines weighted activity to model the energy consumption of information processing. We provide a formal description of this framework and use simulation to show how it enables one to study the interaction between information and energy in energy-aware information processing. An existing discrete event system specification (DEVS)-based simulation environment, DEVS-FIRE, is employed to model wireless sensor nodes for detecting and monitoring wildfires. Simulation experimental results confirm the utility of the activity-based framework to support the analysis and design of energy-aware information processing systems.

Qiu, Fasheng; Hu, Xiaolin

doi: 10.1177/0037549711435950pmid: N/A

This paper proposes a spatial activity-based modeling approach and applies it to pedestrian crowd behavior simulations. Compared to conventional pedestrian crowd models where pedestrians make movement decisions in a time-based manner, the spatial activity-based pedestrian crowd model allows a pedestrian to make decisions only when needed, that is, when there is significant change in the pedestrian’s spatial position, as defined by a threshold called ‘space resolution’. To demonstrate the spatial activity-based modeling, we develop an activity-based model for pedestrian crowd simulation in a two-dimensional environment and compare it with a conventional time-based simulation model. Experiment results show that the spatial activity-based model can lead to more efficient simulations when simulating heterogeneous pedestrian crowds.

Li, Ting; Van Vorst, Nathanael; Liu, Jason

doi: 10.1177/0037549712469892pmid: N/A

Traditional discrete-event simulation of large-scale networks at the packet level is computationally expensive. This article presents a fast rate-based transmission control protocol (RTCP) traffic model designed to reduce the time and space complexity for simulating network traffic whilst maintaining good accuracy. A distinct feature of the proposed model is that the transmission control protocol (TCP) congestion control behavior is represented using analytical models that describe the send rate at the traffic source as a function of the round-trip time and the packet loss rate at different phases of a TCP connection. Rather than modeling at the granularity of individual packets visiting the intermediate routers, the model approximates traffic flows as a series of rate windows, each consisting of a number of packets considered to possess the same arrival rate. The model calculates the queuing delays and the packet losses as these rate windows traverse the individual network queues along the flow path. The proposed RTCP model is able to achieve a performance advantage over other TCP models, by integrating analytical solutions and aggregating traffic using rate windows. Empirical results show that the RTCP model can correctly capture the overall TCP behavior and achieve a speedup of more than two orders of magnitude over the corresponding detailed packet-oriented simulation.

Sencer, Asli; Basarir Ozel, Birgul

doi: 10.1177/0037549712470169pmid: N/A

Workforce management is critical in call centers, where thousands of calls are handled by hundreds of agents every day. In a call center, where the call arrival rates tend to fluctuate during the day, the agent allocation plans are required to be planned flexibly and the number of operating call center agents ought to be updated whenever needed, in order to keep the customer satisfaction level over a predefined level. Workforce plans are usually generated by the use of queuing models that are based on Erlang C calculations. However, they have assumptions that oversimplify the real system and jeopardize the validation of the model. At this point, the simulation models, which do not have such restrictive assumptions, are effective in calculating the required number of agents for each time period and measuring the performance of a given shift schedule. In this study, a simulation-based decision support system (DSS) is developed that runs on real-time data for one of the largest call centers in Turkey. The graphical user interfaces (GUIs) are designed in accordance with the man–machine interaction consideration to increase the usability, functionality and effectiveness of the DSS. It is shown that the combination of the advantages of simulation with a flexible and user-friendly DSS environment provides more effective and efficient workforce planning and performance reporting in call centers.

Schamai, Wladimir; Fritzson, Peter; Paredis, Chris JJ

doi: 10.1177/0037549712470296pmid: N/A

ModelicaML is a UML profile that enables modeling and simulation of systems and their dynamic behavior. ModelicaML combines the power of the OMG UML standardized graphical notation for systems and software modeling, and the simulation power of Modelica. This addresses the increasing need for precise and integrated modeling of products containing both software and hardware. This article discusses the usage of executable UML state machines for system modeling, i.e. usage of the same formalism for describing the state-based dynamic behavior of physical system components and software. Moreover, it points out that the usage of Modelica as an action language enables an integrated simulation of continuous-time and reactive/event-based system dynamics. The main purpose of this article is however to highlight issues that are identified regarding the UML specification which are experienced with typical executable implementations of UML state machines. The issues identified are resolved and rationales for the taken design decisions are provided.

Monsef, Hassan; Wu, Bin

doi: 10.1177/0037549712470732pmid: N/A

Improving system efficiency and reliability is motivating countries to design and execute different types of time of use demand response programs. However, certain deficiencies prevent these programs from reaching their goals. Smart meters as a mechanism could help the electric system to reach the highest demand-side management goals which are inaccessible through today’s methods. On the other hand, realization of smart meters in a system would be very costly. In this situation, identifying the most influential buses to implement the infrastructures of a smart grid is of highest importance. In this paper, after a short overview of demand response programs and problems facing them, a smart meter is introduced as a solution to these problems. As a test grid, the IEEE 57-bus network has been chosen to compare the results of the execution of a normal time of use program and real-time pricing program available in a smart grid. In order to execute the mentioned programs in this system, 10 buses have been selected as the most influential buses using a generation shift factor method. The execution of time of use and real-time pricing programs on the selected buses have been simulated using a demand response model. Finally, the time of use program and the real-time pricing program in a smart grid environment have been compared with respect to load shape modification, load factor, price curve, and ‘Expected Power Not Supplied’.

Sarraf Shirazi, Abbas; von Mammen, Sebastian; Jacob, Christian

doi: 10.1177/0037549712470733pmid: N/A

The typically large numbers of interactions in agent-based simulations come at considerable computational costs. In this article, we present an approach to reduce the number of interactions based on behavioural patterns that recur during runtime. We employ machine learning techniques to abstract the behaviour of groups of agents to cut down computational complexity while preserving the inherent flexibility of agent-based models. The learned abstractions, which subsume the underlying model agents’ interactions, are constantly tested for their validity: after all, the dynamics of a system may change over time to such an extent that previously learned patterns would not reoccur. An invalid abstraction is, therefore, removed again from the system. The creation and removal of abstractions continues throughout the course of a simulation in order to ensure an adequate adaptation to the system dynamics. Experimental results on biological agent-based simulations show that our proposed approach can successfully reduce the computational complexity during the simulation while maintaining the freedom of arbitrary interactions.

Khalili, Ali; Abdollahi Azgomi, Mohammad; Jalaly Bidgoly, Amir

doi: 10.1177/0037549712473512pmid: N/A

Discrete-event systems have gained a lot of interest due to their wide range of applications, and discrete-event simulation is a useful method for the performance evaluation of such systems. In this domain, model-based evaluation methods play an important role and there are many formalisms and realistic experiments using these methods. In this paper, we introduce SimGine, a multi-formalism simulation engine for stochastic discrete-event systems based on SDES, which is a unified abstract description for stochastic discrete-event systems. The engine is also capable of rare-event simulation of models using the importance sampling technique, which makes it the first multi-formalism simulation tool with rare-event simulation capability. The XML-based input language of SimGine allows for definition of the required methods. The body of each method is expressed by codes in a high-level programming language and this provides a powerful and flexible approach for defining events with complex behavior. For the simulation of an existing model, a tool for translating models into the SimGine input language should be prepared. SimGine can be used as a stand-alone simulation tool or as a simulation engine in other tools.