Robert E. Shannon

Introduction to the art and science of simulation

This introductory tutorial presents an overview of the process of conducting a simulation study of any discrete system. The basic viewpoint is that conducting such a study requires both art and science. Some of the issues addressed are how to get started, the steps to be followed, the issues to be faced at each step, the potential pitfalls occurring at each step, and the most common causes of failures.

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.

Knowledge based simulation techniques for manufacturing

The art and science of simulating complex manufacturing systems is rapidly changing. A great deal of attention is being devoted to the possibilities of bringing artificial intelligence (AI) and expert systems (ES) technology into simulation methodology. Such systems will hopefully allow models to be quickly developed, validated and run with as much of the necessary expertise as possible built into the software. This paper addresses: (a) the motivation and need for developing such systems, (b) the nature of such systems, (c) the potential benefits of this technology over existing approaches and (d) the current state-of-the-art as it applies to simulation.

Introduction to simulation

The purpose of this tutorial is to introduce the basic concepts of discrete simulation as a decision making aide. The advantages and disadvantages of simulation are discussed as well as the basic steps in the simulation process. Factors to be considered as well as pitfalls to avoid are presented. 1 WHAT IS SIMULATION Simulation is one of the most powerful analysis tools available to those responsible for the design and operation of complex processes or systems. In an increasingly competitive world, simulation has become a very powerful tool for the planning, design, and control of complex systems. No longer regarded as the approach of “last resort” it is today viewed as an indispensable problem solving methodology for engineers, designers and managers. To simulate, according to Webster’s Collegiate Dictionary, is “to feign, to obtain the essence of, without the reality.” Thus according to Schriber [198’71, “Simulation involves the modeling of a process or system in such a way that the model mimics the response of the actual system to events that take place over time.” We will define Simulation as the process of designing a model of a real system and conducting expen”ments with this model for the purpose of either understanding the behavior of the system and/or evaluating various strate~-es for the operation of the system. We consider simulation to include both the construction of the model and the experimental use of the model for studying a problem. Thus, we ean think of simulation modeling as an experimental and applied methodology which seeks tcx * Describe the behavior of systems. * Construct theories or hypothesis that account for the observed behavior. * Use the model to predict future behavior, that is, the effects that will be produced by changes in the system or in its method of operation. 2 WHAT CAN BE SIMULATED ? In every research study on the utility and use of operations research techniques [Shannon et al 1981, Ford et al 1987], simulation has always turned up either first or second. The reason is its great versatility, flexibtity and power. When considering the question of what kinds of systems can be simulated, the answer is that almost any type has or can be studied. The broad range of applications of this methodology is almost impossible to classify. Rather than try to give an exhaustive list, we will simply try to point out some representative areas of previous applications. COMPUTER SYSTEMS hardware components, software systems, networks of hardware, data base structure and management, information processing, reliability of hardware and software, etc.. MANUFACTURING material handling systems, assembly lines, automated production facilities, automated storage facilities, inventory control systems, reliability and maintenance studies, plant layout, machine design, etc.. BUSINESS stock and commodhy analysis, pricing policy, marketing strategies, acquisition studies, cash flow analysis, forecasting, transportation alternatives, manpower planning etc.. GOVERNMENT military weapons and their use, mititary tactic-s, population forecasting, kmd use, health are delivery, f~e protection, police services, criminal justice, roadway design, traffic control, sanitation

A predictive neural network modelling system for manufacturing process parameters

A methodology to predict the occurrence of out-of-control process conditions in a composite board manufacturing facility was developed using neural network theory. Multi-variable regression and time series analysis techniques were applied to analyse the data set for comparison and informational purposes. Regression models were developed to mode] specific process parameters and could account for only 25% of the variation in those parameters. When analysed as a time series, the data stream was non-stationary in the variance and transformations failed to achieve stationarity. Back-propagation neural networks were successfully trained to represent the process parameters. Inputs to the network consisted of data representing the current process condition along with historical data on relevant parameters, including temperature, moisture content, and bulk density. The training data set was graphically analysed to demonstrate the type of response surface successfully modelled. The trained neural networks were able...

Knowledge conceptualization tool

Knowledge acquisition is one of the most important and problematic aspects of developing knowledge-based systems. Many automated tools have been introduced in the past, however, manual techniques are still heavily used. Interviewing is one of the most commonly used manual techniques for a knowledge acquisition process, and few automated support tools exist to help knowledge engineers enhance their performance. The paper presents a knowledge conceptualization tool (KCT) in which the knowledge engineer can effectively retrieve, structure, and formalize knowledge components, so that the resulting knowledge base is accurate and complete. The KCT uses information retrieval technique to facilitate conceptualization, which is one of the human intensive activities of knowledge acquisition. Two information retrieval techniques employing best-match strategies are used: vector space model and probabilistic ranking principle model. A prototype of the KCT was implemented to demonstrate the concept. The results from KCT are compared with the outputs from a manual knowledge acquisition process in terms of amount of information retrieved and the process time spent. An analysis of the results shows that the process time to retrieve knowledge components (e.g., facts, rules, protocols, and uncertainty) of KCT is about half that of the manual process, and the number of knowledge components retrieved from knowledge acquisition activities is four times more than that retrieved through a manual process.

Automatic programming of AGVS simulation models

This paper presents a knowledge based modeling system that allows a manufacturing engineer who has very limited knowledge of simulation methodology to quickly and correctly, develop and run a simulation model of an automated guided vehicle system (AGVS). The modeling system is capable of guiding and assisting the engineer with a level of “expertise” comparable to a trained simulation specialist. The modeling support program is an automatic programming system, written in Turbo-Prolog which generates the computer code for the required model and experiment in the SIMAN simulation language.

A sensitivity analysis of a back-propagation neural network for manufacturing process parameters

Back-propagation neural networks that represent specific process parameters in a composite board manufacturing process were analyzed to determine their sensitivity to network design and to the values of the learning parameters used in the back-propagation algorithm. The effects of the number of hidden layers, the number of nodes in a hidden layer, and the values of the learning rate and momentum factor were studied. Three network modification strategies were applied to evaluate their effect on the predictive capability of the network. The convergence criteria were tightened, the number of hidden nodes and hidden layers was increased. These modifications did not improve the predictive capability of the composite board networks.

A model for reengineering legacy expert systems to object-oriented architecture

Abstract The migration of existing systems to object-oriented technology is becoming increasingly important. In this paper, a reengineering model is presented. The goal of the model is to provide a comprehensive method to reengineer non object-oriented systems into object-oriented architecture. The model consists of three main processes: Reverse engineering, merging, and object-oriented development. Reverse engineering extracts requirements and knowledge from an existing software system and redocuments the system. In the merging process, recovered requirements and knowledge from the reverse engineering process are merged with new requirements and knowledge. The merging process removes redundancy, checks for inconsistency, and detects incompleteness. In the object-oriented development, a reengineered system is developed using an object-oriented software development method. This research demonstrates that successful reengineering to object-oriented architecture can be achieved by using requirements and knowledge from the original system as a basis for developing the object-oriented system. The model proved to be useful where a paradigm shift is needed. The effectiveness of the model was demonstrated by converting a legacy non object-oriented software system (implemented in C) into an object-oriented system (implemented in Smalltalk). A set of tools was built to support the model. We also identify guidelines that facilitate the transformation of legacy software systems into object-oriented systems.

Expert simulation system based on a relational database

Describes the design and early implementation of a life-cycle oriented simulation environment based on a commercial relational database. The modules and database that make up the environment are discussed. The rationale for such a system and the current state-of-the-art are presented.<<ETX>>