Thomas J. Schriber

Inside discrete-event simulation software: how it works and why it matters

This paper provides simulation practitioners and consumers with a grounding in how discrete-event simulation software works. Topics include discrete-event systems; entities, resources, control elements and operations; simulation runs; entity states; entity lists; and their management. The implementations of these generic ideas in AutoMod, SLX, ExtendSim, and Simio are described. The paper concludes with several examples of “why it matters” for modelers to know how their simulation software works, including discussion of AutoMod, SLX, ExtendSim, Simio, Arena, ProModel, and GPSS/H.

A conceptual framework for research in the analysis of simulation output

An important part of a simulation study is the analysis of simulation output for the purpose of making inferences about properties of the process being simulated. Fundamental to much of this analysis is the building of a confidence interval on the mean value of a key output variable of interest. This procedure is complicated by the possible presence of serial correlation in the output, which makes it difficult to estimate the variability in the estimator of the process mean. Although some research has been devoted to estimating this variability, additional research must be directed toward developing reliable confidence interval procedures and making the practitioners selection of an appropriate procedure relatively straightforward. This paper provides a conceptual framework for research in the analysis of simulation output, focusing in particular on confidence interval methodology. The characteristics of confidence interval procedures, theoretical output processes, and processes of practical interest to simulation practitioners are discussed, and theirrelationships with one another spelled out. Standard measures of effectiveness applicable to proposed confidence interval procedures are introduced, and the application of these measures to two previously suggested confidence interval procedures is illustrated.

Towards a Web based simulation environment

Existing Simulation and Animation (S&A) software tools are typically platform dependent and do not particularly lend themselves to cooperative work within either the Internet or an Intranet. This paper describes: • Basic approaches toward achieving Web compliance for S&A software; and • Specific components for potential use in an open, platform independent simulation environment for the Internet or for corporate Intranets. Requirements for a Web Based Simulation Environment (WBSE) are also discussed.

Perspectives on simulation using GPSS

GPSS (General Purpose Simulation System), a simulation modeling language used to build computer models for discrete-event simulations, is overviewed. The approach taken in GPSS to model a one-line, one-server system is explained, implementation details are provided, and results are discussed. References and suggestions for further study are given.

Machine utilizations achieved using balanced FMS production ratios in a simulated setting

Stecke [21] has developed mathematical programming approaches for determining, from a set of part type requirements, the production ratios (part types to be produced next, and their proportions) which maximize overall machine utilizations by balancing machine workloads in a flexible manufacturing system (FMS). These mathematical programming (MP) approaches are aggregate in the sense that they do not take into account such things as contention for transportation resources, travel time for work-in-process, contention for machines, finite buffer space, and dispatching rules. In the current study, the sensitivity of machine utilizations to these aggregations is investigated through simulation modeling. For the situation examined, it is found that achieved machine utilizations are a strong function of some of the factors ignored in the MP methodology, ranging from 9.1% to 22.9% less than those theoretically attainable under the mathematical programming assumptions. The 9.1% degradation results from modeling with nonzero work-in-process travel times (i.e. 2 minutes per transfer) and using only central work-in-process buffers. Resource levels (e.g. the number of automated guided vehicles; the amount of work-in-process; the number of slack buffers) needed to limit the degradation to 9.1% correspond to FMS operating conditions which are feasible in practice.

A GPSS/H model for a hypothetical flexible manufacturing system

A GPSS/H model is presented for a hypothetical flexible manufacturing system. The FMS consists of six machines composed of three machine types, manufactures three types of parts, and uses automatic guided vehicles (AGVs) to transport inprocess parts between appropriate machines and wait spaces in the system. Three logical modules have been designed for the model, with copies of these modules then being appropriately distributed and interfaced throughout the model and tailored to achieve overall representation of the specific FMS. The same technique can be used by others to build analogous or extended GPSS/H models for other specific FMSs in which AGVs are used as transporters. Simulations can then be performed with such models to research FMS design and control alternatives.

Using mathematical programming and simulation to study FMS machine utilizations

Mathematical programming can be used to determine, from a set of part-type orders, an input stream composition which maximizes machine utilizations in an FMS (flexible manufacturing system) composed of specified machining resources. Simulation can then be used to estimate the degradation in these utilizations due to their dependency on the following factors ignored in the mathematical programming solution: (1) secondary FMS resources (e.g. pallets and fixtures; loading and unloading stations; buffers; type and capacity of equipment for transferring work-in-process); (2) geometric considerations (e.g., location of loading and unloading stations, machines, and buffers; routes for transfer of work-in-process); (3) secondary time requirements (e.g., transfer times; palletizing and depalletizing times; fixturing, defixturing, and refixturing times); (4) operating procedures (e.g., quantity of work-in-process; dispatching rules; part input sequence); (5) operating discontinuities (e.g., machine breakdowns; scheduled machine maintenance; machine substitution; breakdowns and/or maintenance of equipment for transferring work-in-process); and (6) secondary job characteristics (e.g., the sequence in which parts use machines; fixturing and refixturing requirements; due dates; lateness penalties). This paper presents an example illustrating the sequential use of mathematical programming and simulation to: (1) determine the level of selected secondary FMS resources required to maximize machine utilizations when transfer times are realistic; (2) estimate the degradation in machine utilizations when there are inadequate secondary resources; and (3) determine the sensitivity of machine utilizations to selected operating procedures.

Various ways academics teach simulation: are they all appropriate?

This panel discusses goals and educational strategies for teaching simulation in academia. Clearly, there is considerable material to cover in a single course or a sequence thereof in, say, an undergraduate program. The issue is how to motivate and empower students to analyze complex problems correctly and to prevent the pitfall of misusing the concept.

Assessment of simulation models based on trace-file analysis: a metamodeling approach

Many important characteristics of simulation models, including queuing models, can be investigated by the use of metamodels. Problems in qualitative analysis such as analyzing model dynamics and coming to a careful understanding of model behavior can be dealt with this way. Metamodels can provide precise results even for quantitative analysis tasks, such as those involving the movement of dynamic model elements. This paper describes the use of a type of metamodeling to support the assessment of simulation models based on the analysis of trace files produced at the time of model execution. Because of the simple structure of these trace files, a simulation model can create them easily. The analysis and interpretation of trace files that is described here is independent of the simulation language used to create the original model. The tools presented in this article can be used for these purposes: to construct generic model structures at the metamodel level and then animate aspects of model behavior in terms of these structures; to build a graphic display indicating which dynamic model elements moved at which times between which points in the model, and in which real-time order in cases of time ties; to determine when (and if) user-specified model conditions come about; and to develop statistical information that might not have been planned for in the design of the original model. Future plans call for making these tools available in a World Wide Web environment to support assessment of simulation models.

Use of an external optimizing algorithm with a GPSS model

Although GPSS enjoys many advantages as a language for modeling discrete, non-deterministic systems, it suffers from several disadvantages. GPSS is interpretive, resulting in relatively slow execution times. In addition, the input/output and computational capabilities in the language are limited. Disadvantages like these can be lessened or overcome by use of the GPSS HELP Block, which makes it possible to interface an executing GPSS model with one or more FORTRAN subroutines. As an instructive example of HELP Block utility, this paper shows how a GPSS model has been mated with a FORTRAN subroutine implementing the univariate searching strategy. The result is a GPSS-FORTRAN combination which automates the search for the optimal way to configure a system.