Gerald T. Mackulak

Stochastic simulation of risk factor potential effects for software development risk management

Abstract One of the proposed purposes for software process simulation is the management of software development risks, usually discussed within the category of project planning/management. However, modeling and simulation primarily for the purpose of software development risk management has been quite limited. This paper describes an approach to modeling risk factors and simulating their effects as a means of supporting certain software development risk management activities. The effects of six common and significant software development risk factors were studied. A base model was then produced for stochastically simulating the effects of the selected factors. This simulator is a tool designed specifically for the risk management activities of assessment, mitigation, contingency planning, and intervention.

A simulation-based experiment for comparing AMHS performance in a semiconductor fabrication facility

As the cost and complexity of constructing a semiconductor fabrication facility increases, responsive tools are needed for designing and planning its operations. Discrete-event simulation paired with design of experiments is an effective combination. This article demonstrates how simulation in combination with design of experiments is used to compare the intrabay layout of two automated material handling systems. The difference in stocker robot utilization, number of vehicle moves per hour, and average delivery time for the two intrabay layouts will be compared using a fractional factorial experimental design. The study demonstrates that the distributed storage option is preferable for maximizing manufacturing performance. The solution procedure has general applicability as a tutorial for practitioners.

Understanding the effects of requirements volatility in software engineering by using analytical modeling and software process simulation

This paper introduces an executable system dynamics simulation model developed to help project managers comprehend the complex impacts related to requirements volatility on a software development project. The simulator extends previous research and adds research results from an empirical survey, including over 50 new parameters derived from the associated survey data, to a base model. The paper discusses detailed results from two cases that show significant cost, schedule, and quality impacts as a result of requirements volatility. The simulator can be used as an effective tool to demonstrate the complex set of factor relationships and effects related to requirements volatility.

D-Optimal Sequential Experiments for Generating a Simulation-Based Cycle Time-Throughput Curve

A cycle time-throughput curve quantifies the relationship of average cycle time to throughput rates in a manufacturing system. Moreover, it indicates the asymptotic capacity of a system. Such a curve is used to characterize system performance over a range of start rates. Simulation is a fundamental method for generating such curves since simulation can handle the complexity of real systems with acceptable precision and accuracy. A simulation-based cycle time-throughput curve requires a large amount of simulation output data; the precision and accuracy of a simulated curve may be poor if there is insufficient simulation data. To overcome these problems, sequential simulation experiments based on a nonlinear D-optimal design are suggested. Using the nonlinear shape of the curve, such a design pinpointsp starting design points, and then sequentially ranks the remainingn --p candidate design points, wheren is the total number of possible design points being considered. A model of a semiconductor wafer fabrication facility is used to validate the approach. The sequences of experimental runs generated can be used as references for simulation experimenters.

Effective simulation model reuse: a case study for AMHS modeling

The application of simulation as a performance estimation tool in automated material handling system design is well documented, as is the amount of time required to build, debug, and analyze a typical AMHS simulation model. In the rapid growth, high technology industry, it is infeasible to continually employ sufficient staff to create unique models of all of the possible scenarios that require examination. Typically, the model is not complete before the project requirements have been modified. An alternative to unique model creation is to reuse an existing generic model. Generic models are similar to a group of software tools called simulators: software packages that contain a pre-programmed model. Investigation has indicated that a special purpose reusable generic model, designed to address the set of issues faced by a specific commercial entity, is efficient and necessary for fast model turnaround. If correctly developed, the generic model could be reused, thereby reducing model building time as well as increasing simulation accuracy. The paper discusses the use of such a model and illustrates the improvement to model build cycle time.

Ascertaining Important Features For Industrial Simulation Environments

Recent years have witnessed the development and commercial release of multiple simulation tools, environments, and intelligent simulators. Each release seems to contain additional advanced features designed to simplify simulation use and increase the productivity of model builders. But to date, no one has addressed feature definition from the viewpoint of a simulation practitioner. This paper discusses our efforts to identify and prioritize simulation features deemed most desirable from the practitioner viewpoint. A series of three questionnaires was developed and administered to a group of qualified simulation practitioners. With results that are of interest to simulation users, researchers, and simulation software developers, the survey responses reveal not only what practitioners feel are the most important features of presently available commercial packages, but also identify important areas for future development.

Efficient cycle time-throughput curve generation using a fixed sample size procedure

The cycle time-throughput curve is one of the most important analytical tools used to assess operating policies in manufacturing systems. Unfortunately, the generation of this curve is complicated and time consuming when generation is based upon extensive simulation analysis. This paper presents a simulation-based, fixed sample size strategy for generating a cycle time-throughput curve with minimal mean square error that mitigates the typical problems associated with a simulation-based cycle time-throughput curve. The strategy comprises two components, the sampling method and sampling weights. A queuing network of five workstations in series is used for validation of the approach. Results indicate that the sampling method using antithetic variates is effective in reducing the variance as well as bias of a cycle time-throughput curve. Furthermore, this method is robust to the sample size. Given a sufficiently large sample, the combination of common random numbers and antithetic variates is preferred. A reduction in the sample size and complexity of the system increases the significance of the sampling weights.

Levels of Capacity and Material Handling System Modeling for Factory Integration Decision Making in Semiconductor Wafer Fabs

As the costs of building a new wafer fab increase, a detailed simulation model representing the production operations, the tools, the automated material handling systems (AMHS), and the tool-AMHS interactions is needed for accurately planning the capacity of these facilities. The problem is that it currently takes too long to build, experiment, and analyze a sufficiently detailed model of a fab. The key for building accurate and computationally efficient fab models is to decide on the right amount of model details, specifically those details representing the equipment capacity and the AMHS. This paper identifies a method for classifying a fab model by the level of capacity detail, the level of AMHS detail, or the level of capacity/AMHS detail. Within the capacity/ AMHS modeling level, our method further differentiates between detailed integrated capacity/AMHS models and abstract coupled capacity/AMHS models. The proposed classification method serves as the basis of a framework that helps users select the system components to be modeled within a desired level of detail. This research also provides a review of past-published literature summarizing the work done at each of the proposed fab modeling levels. A case study comparing the performance between an integrated capacity/AMHS model and a coupled capacity/AMHS model is presented. The study demonstrates that the coupled model generates cycle time estimates that are not statistically different than those generated by the integrated model. This paper also shows that the coupled model can improve CPU time by approximately 98% in relation to the integrated model.

A discrete event simulation model simplification technique

Cycle time-throughput curves (CT-TH), which plot the average cycle time versus start rate for a given product mix, are often used to support decisions made in manufacturing settings, such as the impact of proposed changes in start rate on mean cycle time. Discrete event simulation is often used to generate estimations of cycle time at a significant number of traffic intensities (start rates). However, simulation often requires long run lengths and extensive output analysis. In most manufacturing environments, the time and/or budget available for such simulations is limited. As demands for faster and more accurate results are required, alternative approaches to improving simulation efficiency must be investigated. This research seeks to develop a procedure for simplifying a detailed model into a fast (abstract) simulation model that achieves a statistically indistinguishable level of accuracy and precision. This technique has particular application in the simulation of semiconductor manufacturing facilities

Operational modeling and simulation of an inter-bay AMHS in semiconductor wafer fabrication

This paper studies the operational logic in an inter-bay automated material handling system (AMHS) in semiconductor wafer fabrication. This system consists of stockers located in a two-floor layout. Automated moving devices transfer lots between stockers within the same floor (intra-floor lot transfer) or between different floors (inter-floor lot transfer). Intra-floor lot-transferring transports use a two-rail one-directional system, whereas inter-floor lot-transferring transports use lifters. The decision problem consists of selecting rails and lifters that minimize average lot-delivery time. Several operation rules to deliver lots from source stocker to destination stocker are proposed and their performance is evaluated by discrete event simulation.