A modular hybrid simulation framework for complex manufacturing system design

For complex manufacturing systems, the current hybrid Agent-Based Modelling and Discrete Event Simulation (ABM–DES) frameworks are limited to component and system levels of representation and present a degree of static complexity to study optimal resource planning. To address these limitations, a modular hybrid simulation framework for complex manufacturing system design is presented. A manufacturing system with highly regulated and manual handling processes, composed of multiple repeating modules, is considered. In this framework, the concept of modular hybrid ABM–DES technique is introduced to demonstrate a novel simulation method using a dynamic system of parallel multi-agent discrete events. In this context, to create a modular model, the stochastic finite dynamical system is extended to allow the description of discrete event states inside the agent for manufacturing repeating modules (meso level). Moreover, dynamic complexity regarding uncertain processing time and resources is considered. This framework guides the user step-by-step through the system design and modular hybrid model. A real case study in the cell and gene therapy industry is conducted to test the validity of the framework. The simulation results are compared against the data from the studied case; excellent agreement with 1.038% error margin is found in terms of the company performance. The optimal resource planning and the uncertainty of the processing time for manufacturing phases (exo level), in the presence of dynamic complexity is calculated

A Framework for Megascale Agent Based Model Simulations on Graphics Processing Units

Agent-based modeling is a technique for modeling dynamic systems from the bottom up. Individual elements of the system are represented computationally as agents. The system-level behaviors emerge from the micro-level interactions of the agents. Contemporary state-of-the-art agent-based modeling toolkits are essentially discrete-event simulators designed to execute serially on the Central Processing Unit (CPU). They simulate Agent-Based Models (ABMs) by executing agent actions one at a time. In addition to imposing an un-natural execution order, these toolkits have limited scalability. In this article, we investigate data-parallel computer architectures such as Graphics Processing Units (GPUs) to simulate large scale ABMs. We have developed a series of efficient, data parallel algorithms for handling environment updates, various agent interactions, agent death and replication, and gathering statistics. We present three fundamental innovations that provide unprecedented scalability. The first is a novel stochastic memory allocator which enables parallel agent replication in O(1) average time. The second is a technique for resolving precedence constraints for agent actions in parallel. The third is a method that uses specialized graphics hardware, to gather and process statistical measures. These techniques have been implemented on a modern day GPU resulting in a substantial performance increase. We believe that our system is the first ever completely GPU based agent simulation framework. Although GPUs are the focus of our current implementations, our techniques can easily be adapted to other data-parallel architectures. We have benchmarked our framework against contemporary toolkits using two popular ABMs, namely, SugarScape and StupidModel.GPGPU, Agent Based Modeling, Data Parallel Algorithms, Stochastic Simulations

RAMSES: Reversibility-based agent modeling and simulation environment with speculation-support

This paper presents RAMSES, a framework for easily specifying agent-based discrete event models entailing both environment and agent entities. RAMSES offers parallel execution capabilities based on speculative event processing and an innovative software reversibility technique that copes with state restore in case the run slides along a non-consistent speculative path. Reversibility in RAMSES relies on transparent static software instrumentation, thus allowing the model developer to concentrate on the actual forward-execution logic of the simulation events occurring in the system. An experimental assessment of RAMSES is also presented, which is aimed at determining its run-time effectiveness and its potential for simplifying the development of agent-based models when compared to other (general purpose) speculative frameworks for parallel discrete event simulation

Dynamic Network Security Control Using Software Defined Networking

This thesis develops and implements a process to rapidly respond to host level security events using a host agent, Software Defined Networking and OpenFlow updates, role based flow classes, and Advanced Messaging Queuing Protocol to automatically update configuration of switching devices and block malicious traffic. Results show flow table updates are made for all tested levels in less than 5.27 milliseconds and event completion time increased with treatment level as expected. As the number of events increases from 1,000 to 50,000, the design scales logarithmically caused mainly by message delivery time. Event processing throughput is limited primarily by the message rate of the agent (40 msg./sec.). Additionally, the maximum effective consume rate for the controller indicates this design is capable of supporting up to 380 hosts at one msg./sec. Finally, every event triggered is successfully processed for both experiments resulting in a 100 percent event success rate

Programmability and Performance of Parallel ECS-based Simulation of Multi-Agent Exploration Models

While the traditional objective of parallel/distributed simulation techniques has been mainly in improving performance and making very large models tractable, more recent research trends targeted complementary aspects, such as the “ease of programming”. Along this line, a recent proposal called Event and Cross State (ECS) synchronization, stands as a solution allowing to break the traditional programming rules proper of Parallel Discrete Event Simulation (PDES) systems, where the application code processing a specific event is only allowed to access the state (namely the memory image) of the target simulation object. In fact with ECS, the programmer is allowed to write ANSI-C event-handlers capable of accessing (in either read or write mode) the state of whichever simulation object included in the simulation model. Correct concurrent execution of events, e.g., on top of multi-core machines, is guaranteed by ECS with no intervention by the programmer, who is in practice exposed to a sequential-style programming model where events are processed one at a time, and have the ability to access the current memory image of the whole simulation model, namely the collection of the states of any involved object. This can strongly simplify the development of specific models, e.g., by avoiding the need for passing state information across concurrent objects in the form of events. In this article we investigate on both programmability and performance aspects related to developing/supporting a multi-agent exploration model on top of the ROOT-Sim PDES platform, which supports ECS

Building a Better Pedestrian Flow Model for the Indianapolis Motor Speedway

Undeniable shifts in how public events are conducted with regard to security have occurred since the terrorist attacks on the United States on September 11, 2001. Increased security requirements are a product of the paradigm shift in security for Mega-Event locations. This study examined the Indianapolis Motor Speedway during Mega-Event status events, with specific focus on the 2013, Indianapolis 500 automobile race. The objective was to study the phenomenon of pedestrian flow as it related to entry gate procedures and resulting impacts. This data was then used to compile modeling scenarios employing AnyLogic computer software that allowed for free-agent, variable play to replicate the conditions of the security processing. Through manipulation of agent variables the researcher was able to determine the optimal pedestrian throughput under maximum load conditions. This data was therefore used to identify the processing time standard required in order for security personnel to achieve steady-state flow, which allowed for adequately conducted security checks, and reduction of patron wait times

Optimization of swarm robotic constellation communication for object detection and event recognition

Swarm robotics research describes the study of how a group of relatively simple physically embodied agents can, through their interaction collectively accomplish tasks which are far beyond the capabilities of a single agent. This self organizing but decentralized form of intelligence requires that all members are autonomous and act upon their available information. From this information they are able to decide their behavior and take the appropriate action. A global behavior can then be witnessed that is derived from the local behaviors of each agent. The presented research introduces the novel method for optimizing the communication and the processing of communicated data for the purpose of detecting large scale meta object or event, denoted as meta event, which are unquantifiable through a single robotic agent. The ability of a swarm of robotic agents to cover a relatively large physical environment and their ability to detect changes or anomalies within the environment is especially advantageous for the detection of objects and the recognition of events such as oil spills, hurricanes, and large scale security monitoring. In contrast a single robot, even with much greater capabilities, could not explore or cover multiple areas of the same environment simultaneously. Many previous swarm behaviors have been developed focusing on the rules governing the local agent to agent behaviors of separation, alignment, and cohesion. By effectively optimizing these simple behaviors in coordination, through cooperative and competitive actions based on a chosen local behavior, it is possible to achieve an optimized global emergent behavior of locating a meta object or event. From the local to global relationship an optimized control algorithm was developed following the basic rules of swarm behavior for the purpose of meta event detection and recognition. Results of this optimized control algorithm are presented and compared with other work in the field of swarm robotics

Cohesive token passing algorithm utilizing software agents

The communications domain has utilized the implementation of protocols for a wide spectrum of applications. This encompasses Medium Access Control (MAC) protocols. MAC protocols have been extensively researched from several angles. This encompasses the implementation in the area of Wave Division Multiplexing (WDM) networks and Mobile Adhoc Networks (MANET). The relevance of intelligence in sustaining the pre-requisites for dynamic reconfiguration has gained an integral attention in MANET. Approach: The implementation of Token Ring in MANET can be correlated to its complementary implementation in IP networks. In this paper, the limitation of Token Ring algorithm for IP networks in the context of intelligent processing has been researched extensively. An enhanced Token Ring protocol governed by intelligent processing has been implemented in this paper. The core of the new protocol is based on the circulation mechanism of the token. As opposed to the traditional circulatory mechanism, a software agent is designed to become an intelligent circulatory agent is this research. The developed software agent is utilized to implement prioritized token access subject to the traffic type. Each station is coupled with a software agent who cohesively collaborates to assign the token. Results: The proposed agent and the enhanced Token Ring implementation have been extensively verified through simulation experiments. A complete circulation of the ring is defined upon all nodes being visited at least once. Discrete-event simulation models were developed and deployed for the purpose of performance analysis. The results acquired validated the improved results of the new software agent based implementation. The performance metrics studied were average delay and average buffer utilization. Conclusion: The proposed algorithm has enabled to derive an ideal balance between the complexity of intelligent processing and the versatility of managing the token ring

Surprisal from language models can predict ERPs in processing predicate-argument structures only if enriched by an Agent Preference principle

Language models based on artificial neural networks increasingly capture key aspects of how humans process sentences. Most notably, model-based surprisals predict event-related potentials such as N400 amplitudes during parsing. Assuming that these models represent realistic estimates of human linguistic experience, their success in modelling language processing raises the possibility that the human processing system relies on no other principles than the general architecture of language models and on sufficient linguistic input. Here, we test this hypothesis on N400 effects observed during the processing of verb-final sentences in German, Basque, and Hindi. By stacking Bayesian generalised additive models, we show that, in each language, N400 amplitudes and topographies in the region of the verb are best predicted when model-based surprisals are complemented by an Agent Preference principle that transiently interprets initial role-ambiguous NPs as agents, leading to reanalysis when this interpretation fails. Our findings demonstrate the need for this principle independently of usage frequencies and structural differences between languages. The principle has an unequal force, however. Compared to surprisal, its effect is weakest in German, stronger in Hindi, and still stronger in Basque. This gradient is correlated with the extent to which grammars allow unmarked NPs to be patients, a structural feature that boosts reanalysis effects. We conclude that language models gain more neurobiological plausibility by incorporating an Agent Preference. Conversely, theories of human processing profit from incorporating surprisal estimates in addition to principles like the Agent Preference, which arguably have distinct evolutionary roots