Bigish Data Analytics and Manufacturing Applications

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Our forthcoming book on data analytics for supply chain optimization: 

Allen, T. T. and Liu, E. (accepted in preparation), Introduction to Machine Learning for Supply Chain Analytics Ultimate Intelligence, Python, Cyber Security, with Industrial Datasets, Springer Verlag: New York.

Our scheduling methods use both conventional branch and bound and hybrid meta-heuristics:

Roychowdhury, S., Allen, T. T., and Allen, N. B. (2017). A Genetic Algorithm with an Earliest Due Date Encoding for Scheduling Automotive Stamping Operations. Computers & Industrial Engineering, 105, 201–209.

Big data analytics often relates to addressing terabytes of data and using parallelization approaches to do what might be considered basic calculations. Bigish data analytics (our word) relates to using much larger data than was previously considered for relatively powerful modeling and optimization activities.

Currently, related research involves speeding up and otherwise improve sequential Kriging optimization and subject matter expert refined topic (SMERT) models. Both methods relate to human-in-the-loop "machine learning". In particular, SMERT models relate to view that (some) experts can be trusted to influence models in desirable ways.

White Paper on Bigish Analytics

Simulation Model

Discrete Event and FEM Simulation Optimization

Computer simulation is a well used practice in engineering. We develop models that sometimes look very realistic visually with virtual people and machines making things or accomplishing tasks (like voting). 

When the model is made and it has some degree of trustworthiness with validation, it makes sense to try out a lot of "what if" studies. Design of experiments and simulation optimization are names of areas in which methods are generated for efficient what if experimentation and recommended setting generation. In some cases, they offer the guarantee that all possible what ifs within a set have effectively been tried.

Some of my students and I developed methods building on work by Schonlau and others which offer pretty efficient way to find desirable settings without too much experimentation. 

Sui, Z., Milam, D., and Allen T. T. (2015). A Visual Monitoring Technique Based on Importance Score and Twitter Feeds." INFORMS Social Media Analytics Student Paper Competition Finalist.

click here to download file

Sequential Kriging Optimization

This data was cited in relation to the following publications.
Allen, T. T., Xiong, H., and Afful-Dadzie, A. (2016). A Directed Topic Model Applied to Call Center Improvement. Applied Stochastic Models in Business and Industry, 32(1), 57-73.
Allen, T. T. and H. Xiong (2012). Pareto charting using multifield freestyle text data applied to Toyota Camry user reviews. Applied Stochastic Models in Business and Industry, 28 (2), 152-163.

click here to download call center data

click here to download Camry report data

click here to download Iraq analysis requiring directed modeling

This data contains a collection from multiple sources. If you use it, please cite all of the following:
Ribardo C. and Allen, T. T. (2003). An Alternative Desirability Function for Achieving ‘Six Sigma’ Quality. Quality and Reliability Engineering International, 19, 227-240
Allen, T. T., R. W. Richardson, D. Tagliabue, and G. Maul (2002). Statistical Process Design for Robotic GMA Welding of Sheet Metal. The Welding Journal, 81, 5, 69s-77s
Allen, T. T., W. Ittiwattana, R. W. Richardson, and G. Maul (2001). A Method for Robust Process Design Based on Direct Minimization of Expected Loss Applied to Arc Welding. The Journal of Manufacturing Systems, 20, 5, 329-348 (

click here to download large arc welding dataset

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