Richard J. Mayer

Expert systems and simulation

Artificial intelligence and expert systems are the latest buzzwords and the hottest topics in the scientific community today Some experts are proclaiming that artificial intelligence (AI) has already emerged as one of the most significant technologies of this cen tury. Proponents are declaring that it will completely revolu tionize management and the way computers are used. If these claims are even half true, then AI is bound to have a profound effect upon the art and science of simulation. This paper will provide an overview of this rapidly evolving field, examine the potential ofAl (and more particularly, expert systems) in simula tion and attempt to explore the probable impact as well as fore cast likely future directions.

The IDEF Family of Languages

The purpose of this contribution is to serve as a clear introduction to the modeling languages of the three most widely used IDEF methods: IDEFO, IDEF1X, and IDEF3. Each language is presented in turn, beginning with a discussion of the underlying “ontology” the language purports to describe, followed by presentations of the syntax of the language — particularly the notion of a model for the language — and the semantical rules that determine how models are to be interpreted. The level of detail should be sufficient to enable the reader both to understand the intended areas of application of the languages and to read and construct simple models of each of the three types.

Using ontologies for simulation modeling

Ontological analysis has been shown to be an effective first step in the construction of robust knowledge based systems. However, the modeling and simulation community has not taken advantage of the benefits of ontology management methods and tools. Moreover, the popularity of semantic technologies and the semantic Web has provided several beneficial opportunities for the modeling and simulation communities of interest. This paper describes the role of ontologies in facilitating simulation modeling. It outlines the technical challenges in distributed simulation modeling and describes how ontology-based methods may be applied to address these challenges. The paper concludes by describing an ontology-based solution framework for simulation modeling and analysis and outlining the benefits of this solution approach

Better management of blood supply-chain with GIS-based analytics

This paper presents a novel application of operations research, data mining and geographic information-systems-based analytics to support decision making in blood supply chain management. This, blood reserve availability assessment, tracking, and management system (BRAMS), research project has been funded by the Office of the Secretary of Defense. (This DoD funded SBIR project is performed by the researchers at Knowledge Based Systems, Inc. (KBSI).) The rapidly increasing demand, criticality of the product, strict storage and handling requirements, and the vastness of the theater of operations, make blood supply-chain management a complex, yet vital problem for the department of defense. In order to address this problem a variety of contemporary analytic techniques are used to analyze inventory and consumption patterns, evaluate supply chain status, monitor performance metrics at different levels of granularity, and detect potential problems and opportunities for improvement. The current implementation of the system is being actively used by 130 mangers at different levels in the supply chain including facilities at Osan Air Force Base in South Korea and Incirlik Air Force Base in Turkey.

Delivering Results: Evolving BPR from Art to Engineering

This chapter presents an approach to Business Process Reengineering (BPR) that is focused on achieving results from the first stages to implementation. The engineering approach presented utilizes an integrated set of methods applied incrementally. This allows BPR practitioners to more realistically approach a project; assess its impact, duration, and required budget; and mitigate the risks of failure. We present the approach as a phased BPR methodology along with methods, proven strategies, and tools we have worked with successfully at each phase. We present motivations for initiating a BPR effort that have been shown to result in successful cases for action. We present rationale for justifying change and a method for building a business case that includes the use of cost benefit analysis in formulating the justification rationale. An approach to planning for a BPR effort is presented that uses the same methods normally applied in the BPR process itself. We cover the issues associated with setting up a BPR project including: forming cross-functional teams, and selecting method and tool technology for the BPR project. A methodology is presented for base-lining the current business situation, identifying the current value delivery system and the processes that support that system along with problem-cause analysis. We describe eight general principles of business process design and conclude with an object-centered technique for new process design. Finally this chapter addresses key issues in the implementation process starting with transition planning activities, model driven information system development, and initiation of a learning system that will carry the results forward in a continuous improvement manner.

Toward a method for acquiring CIM ontologies

Abstract An important requirement for world-class CIM systems is the ability to capture knowledge from multiple disciplines and store it in a form that facilitates re-use, sharing, and extendibility. Taxonomies and glossaries, in and of themselves, will not fully address this requirement, and will need to be supplemented so as to circumscribe the meanings and logical properties of the terms as precisely as possible. We thus perceive the need for ontologies rather than mere taxonomies. An ontology is a description of the kinds of things, both physical and conceptual, that make up a given domain and the relationships among them as represented by the terminology in that domain. This paper describes a scientific method for acquiring, structuring and maintaining CIM ontologies. An ontology capture method is essential to developing practical CIM ontologies because it facilitates the direct capture of CIM knowledge by practitioners within the manufacturing domain. The proposed ontology capture method includes: (...

A methodology for integrating business process and information infrastructure models

Many organizations today undertake Business Process Reengineering (BPR) and information infrastructure (i.e., network hardware, communications, and applications infrastructure) modernization efforts to drastically reduce costs and improve performance. While these efforts would appear to be mutually supportive and complementary in nature, they are rarely conducted jointly. Although it is known that changes to one of these two facets of the organization can produce significant impacts on the other, analysts and decision-makers have had only limited support for predicting the impacts of change to one or both of these two facets of the enterprise. BPR efforts are generally conducted using business process simulation tools. Information infrastructure modernization efforts are generally conducted using network simulation tools. What is lacking is an integrated method and the automated environment for studying the impacts of change to one of these two as pects of the enterprise on the other. This paper presents a simulation-based methodology enabling simultaneous consideration of changes to core business processes and the infrastructure mechanisms that support those processes.

An architecture of a knowledge-based simulation engine

The paper describes the architecture of a knowledge-based simulation engine (KBSE). It summarizes ongoing efforts at Knowledge Based Systems Laboratory, Texas A&M University and Knowledge Based Systems Inc., College Station, TX, in developing knowledge based simulation. The KBSE provides support for: developing a simulation model from a description of the system and the user concern in the form of a question to be answered; and analyzing the simulation output. The support for output analysis includes assistance for statistical analysis, explaining a set of observations, and generating alternative ways to improve performance/solve a problem. The approach used to develop KBSE was to integrate simulation expert knowledge with domain knowledge, commonsense reasoning techniques, and qualitative and quantitative simulation techniques. The Phase I KBSE automates the generation of simulation models from system descriptions. Phase II provides intelligent support for simulation output analysis. The paper presents a summary of the artificial intelligence (AI) based algorithms and heuristics developed in Phase I.

A model-based approach for component simulation development

The increasing complexity of systems has enhanced the use of simulation as a decision-support tool. Often, simulation is the only scientific methodology available to practitioners for the analysis of complex systems. However, only a small fraction of the practical benefits of simulation modeling and analysis have reached the potentially large user community because of the relatively high requirement of time, effort and cost needed to build and successfully use simulation models. We describe a model-based approach that seeks to address these problems via the implementation of MODELSIM, a comprehensive modeling and analysis architecture that includes: application of the IDEF3 and IDEF5 methods for simulation modeling and analysis specification; automatic generation of executable component-based simulations from IDEF-based descriptive models; and reusable libraries of modeling components to facilitate rapid configuration of models as needed over extended periods of time.

Situations and Processes

Typical methods for representing business engineering and manufacturing processes represent process information by means of rather restricted often graphical languages These languages are often fine as far as they go, but for many purposes—information sharing, in particular—much more precise, detailed representations of enterprise processes are required In this paper, we develop an approach to the rigor ous representation of process information based on situation theory We begin with an informal account of the semantic categories of the ap proach including situations, infons types activities, and processes, as well as the central relations that can hold between them A frame work—known as ST based roughly on the Knowledge Interchange Format (KIF) is introduced for expressing information in these terms The use of ST is then illustrated in detail by means of a series of examples Finally the formal semantics for ST is sketched and the language and basic logic of ST is formally defined