Bibliographic record
Abstract
As an applied science, industrial engineering embraces concepts and theories in such fi elds as mathematics, statistics, social sciences, psychology, economics, management and information technology.It develops tools and techniques to design, plan, operate and control service, manufacturing, and a host of engineering and non-engineering systems.Industrial engineering is concerned with the integrated systems of people, materials, machinery, and logistics, and ensures that these systems operate optimally and effi ciently, saving time, energy, and capital.An industrial engineer, or an engineer equipped with the tools and techniques of industrial engineering is the professional who is responsible for all these tasks.This book aims to expose the reader at an introductory level to the basic concepts of a range of topics in industrial engineering and to demonstrate how and why the application of such concepts are effective.The target audience for this abridged volume encompasses all engineers.In other words, the book is written for motivated individuals from a broad range of engineering disciplines who aim for personal and professional development.They would benefi t from having a foundational book on important principles and tools of industrial engineering.They would be able to apply these principles not only to initiate improvements in their place of work but also to open up a career path to management and positions with a higher level of responsibility and decisionmaking.The level of coverage and the topics included in this book have been distilled from over thirty years of teaching a technical elective course in industrial xiii engineering to non-industrial engineering students in their fi nal year of studies.From direct and indirect feedback from the students on the usefulness of various industrial engineering methods in their work, the course content has evolved to what is now presented as a book.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.621 | 0.459 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".