Showing papers by "Bernard P. Zeigler published in 2018"

Discrete Event System Specification Framework for Self-Improving Healthcare Service Systems

Abstract: A healthcare service system (HSS) is made of humans and technology where for the foreseeable future, self-improvement will be primarily based on human understanding rather than machine learning. Therefore, for such a system to continually self-improve, it must provide the right data and models to support human decisions on selection of alternatives likely to improve the quality of its services. Our focus in this paper is to show how modeling and simulation (M&S) can help design service infrastructures that introduce coordination and bring into play the conditions for learning and continuous improvement. To do this, we discuss the application of the discrete event system specification (DEVS) formalism within system of systems engineering (SoSE) to develop coordination models for transactions that involve multiple disparate activities of component systems and that need to be selectively sequenced to implement patient-centered coordinated care interventions. We show how such coordination concepts provide a layer to support a proposed information technology for continuous improvement of healthcare as a learning collaborative system of systems (SoS).

Iterative Specification as a Modeling and Simulation Formalism for I/O General Systems

Abstract: This paper aims at exploiting iterative system specification as a modeling formalism including explicitly simulation mechanisms. The systems specified are dynamic with inputs/outputs (I/O) so that they can be coupled in a modular way. The iterative specification consists of a decomposition of the I/O behavior of a system into trajectory segments. We prove that specific decompositions can be combined while ensuring that the overall I/O behavior is correctly represented. While generic in nature, the approach is validated against spiking neuronal modeling applications.

DEVS markov modeling and simulation: formal definition and implementation

Abstract: Markov Modeling is among the most commonly used forms of model expression and Markov concepts of states and state transitions are fully compatible with the DEVS characterization of discrete event systems. Besides their general usefulness, the Markov concepts of stochastic modeling are implicitly at the heart of most forms of discrete event simulation and are a natural basis for the extended and integrated Markov modeling facility discussed in this paper. DEVS Markov models are full-fledged DEVS models and can be coupled with other DEVS components in hierarchical compositions. Due to their explicit transition and time advance structure, DEVS Markov models can be individualized with specific transition probabilities and transition times/rates which can be changed during model execution for dynamic structural change. This paper presents the formal concepts underlying DEVS Markov models and how they are implemented in MS4 Me, also discussing how the facilities differ from other Markov M&S tools.

Closure under coupling: concept, proofs, DEVS recent examples (wip)

Abstract: With the growth in new variants of DEVS, the concept of closure under coupling has reached a level of importance where it stands discussion in its own right. As emphasized in (Zeigler et al 2000), closure under coupling justifies hierarchical construction. Here we show that it also provides assurance that the class under consideration is well-defined and enables checking for the correct functioning of feedback coupled models. Absence of closure is also informative as it begs for characterizing the smallest closed class that includes the given class. This illustrated here as we discuss closure under coupling for several recently introduced subclasses of the DEVS formalism.

A conceptual framework to classify the extensions of DEVS formalism as variants and subclasses

Abstract: The Discrete Event System Specification (DEVS) is a general modeling formalism with sound semantics founded on a system theoretic basis. It can be used as a base for the development of specialized modeling formalisms. Usually, the extensions of DEVS expand the classes of systems models that can be represented in DEVS. However, with a growing number in new variants of DEVS and an increasing number of problems to be solved using discrete simulation techniques, it is necessary to define the relations among different approaches. This paper presents a conceptual modeling perspective applied to DEVS extensions that structure a framework over the traditional modeling and simulation approach. The framework provides a multilevel structure to analyze the features required for each extension type. Two main types of extensions are identified: variants and subclasses. In order to illustrate the proposed guidelines, the Routed DEVS formalism is presented as example of the subclass type.

Designing care pathways using simulation modeling and machine learning

Abstract: The development of care pathways is increasingly becoming an instrumental artefact towards improving the quality of care and cutting costs. This paper presents a framework that incorporates Simulation Modeling along with Machine Learning (ML) for the purpose of designing pathways and evaluating the return on investment of implementation. The study goes through a use case in relation to elderly healthcare in Ireland, with a particular focus on the hip-fracture care scheme. Initially, unsupervised ML is utilized to extract knowledge from the Irish Hip Fracture Database. Data clustering is specifically applied to learn potential insights pertaining to patient characteristics, care-related factors, and outcomes. Subsequently, the data-driven knowledge is utilized within the process of simulation model development. Generally, the framework is conceived to provide a systematic approach for developing healthcare policies that help optimize the quality and cost of care.

The role of approximate morphisms in multiresolution modeling: Can we relax the strict lumpability requirements?

Abstract: To become the core development methodology of major defense-related simulation projects, multiresolution modeling needs more advances in basic theory and practice. In this note, we illustrate an approach to addressing issues relating to approximate morphisms (structure/behavior preservation relations) between pairs of models. Such issues include metrics for departure from strict lumpability, tying approximate satisfaction of structural conditions to the resulting behavioral error, and how to enhance the probability of creating good approximate morphisms by humans and artificial modelers.

Towards a theory of economic value for modeling and simulation: incremental cost of parallel simulation (wip)

Abstract: A useful perspective on the economic value of simulation in engineering comes from considering the role simulation plays in constructing a system. We propose a conceptual model that combines an embryonic concept of the value of an information bit with cost estimation tools used in software engineering. The model provides an economic justification for the common conception that models should be built to address specific questions rather than to comprehensively represent a referent system. Extending these ideas, we offer a model for the incremental value of parallel execution to explain why simulation applications that take advantage of sophisticated parallel discrete event simulation tools form a relatively narrow niche despite the ubiquity of parallel computing hardware. The proposed model enables us to bring value of information criteria into model development and to make predictions about the future of parallel simulation applications.

Service composition and scheduling in cloud-based simulation environment

Abstract: Nowadays, modelling and simulation (M&S) has become an imperative step in every industrial domain. With the increase of simulation resources, tools and models, cloud simulation is proposed as a new simulation mode which targets to integrate distributed simulation resources and implement wider range of flexible simulation analysis. How to correctly incorporate multiple simulation services together to accomplish a multi-disciplinary simulation task and how to tackle multiple tasks requirements are two important concerns. Numerous services with different granularities in cloud simulation can constitute a complex service network. The service composition steps to complete a task in service network are indeterminate. Motivated by these factors, we propose a new service network-based method for service composition and scheduling. In this method, the number of composition steps is uncertain before to be executed. The work in this study can reflect the characteristic of uncertainty of composition paths in the cloud environment.

Systems engineering and simulation: converging toward noble causes

Abstract: This paper highlights the accomplishments and shared vision between the International Council on Systems Engineering (INCOSE) and the Modeling and Simulation community (represented by the Society for Modeling and Simulation, International (SCS), and Simulation Interoperability Standards Organization (SISO, among others). We describe convergence between the model-based systems engineering initiative of the INCOSE community and the model-based simulation developments of the SCS community. The goal is not only to highlight the outstanding accomplishments of our time, but also to emphasize the parallels and relationships. The paper is intended to enhance communications and facilitate the outreach already in motion. Modeling and Simulation (M&S) represents a core capability and need for addressing today’s complex and grand challenges. We suggest a collaboration of INCOSE and SCS, as leaders in the systems and M&S communities, to solve these challenges complicated by multi-dimensional, hierarchical, and uncertain Big Data and propelled by exascale computational platforms.

Discrete Event Modeling and Simulation Aspects to Improve Machine Learning Systems

Abstract: Discrete Event Modeling and Simulation (M&S) and Machine Learning (ML) are two frameworks suited for system modeling which when combined can give powerful tools for system optimization for example. This paper details how discrete event M&S could be integrated into ML concepts and tools in order to improve the design and use of ML frameworks. An overview of different improvements are given and three concerning Reinforcement Learning (RL) are implemented in the framework of the DEVS formalism.