Freestyle text data such as surveys, complaint transcripts, customer ratings, or maintenance squawks can provide critical information for quality engineering. Exploratory text data analysis (ETDA) is proposed here as a special case of exploratory data analysis (EDA) for quality improvement problems with freestyle text data. The EDTA method seeks to extract useful information from the text data to identify hypotheses for additional exploration relating to key inputs or outputs. The proposed four steps of ETDA are: (1) preprocessing of text data, (2) text data analysis and display, (3) salient feature identification, and (4) salient feature interpretation. Five examples illustrate the methods.
For years, there have been tremendous endeavors to reduce makespan in an attempt to decrease the production expenses. This investigation aims to develop a scenario-based robust optimization approach for a real-world flow shop with any number of batch processing machines. The study assumes there are some uncertainties associated with processing times as well as size of jobs. Each machine can process multiple jobs simultaneously as long as the machines’ capacities are not violated. In order to verify this developed model and to evaluate the performance of the proposed robust model, a number of test problems are prepared and a commercial optimization solver is adopted to solve these test problems. For the purpose of validating the results, the robust model and mean-value model are carried out by simulation, which confirmed the proposed model.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.