Engineering in Action – Processes
At Ohio University, we say we do it better – and here’s how we can prove it. This collection of case studies supports how we’re creating better teams, better processes, better systems – all leading to better results and better engineers.
Creating Quality Processes
In the world of engineering, being precise is always important. Associate Professor David J. Koonce’s paper focuses on the process for developing accurate cost estimations while minimizing errors though risk analysis and calibration. Read more about this topic and examine a variety of other engineering management related case studies.
A Quality Function Deployment Methodology for Product Development
By Ryan Craig, graduate student presented by David Koonce
Quality Function Deployment (AFD) is an industrial and systems engineering tool used in product development. It was developed by Dr. Yoji Akao in 1966 and is comprises of a number of processes, notably the House of Quality, and Kano’s model. To read more, view this PowerPoint presentation about Quality Function Deployment.
Identifying and Removing Error in Hierarchical Cost Estimates
By David Koonce, R.P. Gandhi, A.N. Nambiar, Robert Judd
Abstract
The ability to estimate the manufacturing cost of a product proves to be critical through all phases of the development cycle. When developing an estimate, the two components of the process, the equations which estimate the cost and the data supplied to those equations may both contribute error to the estimate. The error associated with input data can be identified through risk analysis. Error in the equations can be identified through calibration with historical data. This paper presents a methodology for applying these two error identification techniques in a hierarchical cost estimation tool. Click here to read more about Identifying and Removing Error in Hierarchical Cost Estimates.
A Neural Network Job-Shop Scheduler
By Gary Weckman, Chandrasekhar Ganduri, David Koonce
Abstract
This paper focuses on the development of a neural network (NN) scheduler for scheduling job-shops. In this hybrid intelligent system, genetic algorithms (GA) are used to generate optimal schedules to a known benchmark problem. In each optimal solution, every individually scheduled operation of a job is treated as a decision which contains knowledge. Each decision is modeled as a function of a set of job characteristics (e.g., processing time), which are divided into classes using domain knowledge from common dispatching rules (e.g., shortest processing time). A NN is used to capture the predictive knowledge regarding the assignment of operation’s position in a sequence. The trained NN could successfully replicate the performance of the GA on the benchmark problem. The developed NN scheduler was then tested against the GA, Attribute-Oriented Induction data mining methodology and common dispatching rules on a test set of randomly generated problems. The better performance of the NNscheduler on the test problem set compared to other methods proves the feasibility of NN-based scheduling. The scalability of the NN scheduler on larger problem sizes was also found to be satisfactory in replicating the performance of the GA. Click here to read more about a Neural Network Job-Shop Scheduler.
