Intelligent Manufacturing Engineering: Deep Operations Research

Author:   Yong Li ,  Xunchen Liu ,  Shanling Han ,  Jian‘gang Gao
Publisher:   Springer Verlag, Singapore
ISBN:  

9789819531080


Pages:   463
Publication Date:   06 February 2026
Format:   Hardback
Availability:   Not yet available   Availability explained
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Intelligent Manufacturing Engineering: Deep Operations Research


Overview

This book introduces the core framework of Intelligent Manufacturing Engineering—an emerging STEM discipline that integrates mechanical engineering, operations research, and AI. It defines the field through three key pillars: relationships as its essence, Deep Operations Research as its logic, and a new guidelines as its outcome. As the first monograph on Intelligent Manufacturing Engineering, the book offers intuitive logic, concise algorithms, and accessible mathematical reasoning. It is ideal for students in engineering, mathematics, and computer science, and serves as a valuable reference for professionals engaged in intelligent manufacturing system R&D.

Full Product Details

Author:   Yong Li ,  Xunchen Liu ,  Shanling Han ,  Jian‘gang Gao
Publisher:   Springer Verlag, Singapore
Imprint:   Springer Verlag, Singapore
ISBN:  

9789819531080


ISBN 10:   981953108
Pages:   463
Publication Date:   06 February 2026
Audience:   General/trade ,  General
Format:   Hardback
Publisher's Status:   Forthcoming
Availability:   Not yet available   Availability explained
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

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Author Information

Professor Yong Li, Ph.D, from Shandong University of Science and Technology in China, possesses expertise in mathematics, mechanics, polymer science, intelligent manufacturing, and related fields. As the CEO & CTO of a listed company, he has successfully established a tire intelligent manufacturing factory. Transitioning from industry to academia, by delving into the ""black box"" of enterprise product development and conducting an in-depth analysis of underlying issues, he has proposed the Deep Operations Research methodologies, which form the underlying logic of the book. As the first or corresponding author, he has authored 28 academic papers. Professor Xunchen Liu, obtained PhD in University of Alberta in Edmonton, Canada in 2013, worked as a lecture in the School of Mechanical Engineering Shanghai Jiao Tong University from 2015 to 2022. Joined Shandong University of Science and Technology in 2023 as Professor and PhD supervisor in the College of Mechanical and Electrical Engineering. Main research interest includes optical diagnostics and aero-engine combustion. Professor Shanling Han, Ph.D, from Shandong University of Science and Technology in China. He is the Doctoral Supervisor, Qingdao West Coast New Area “Outstanding Young Talent”, Shandong Province “Outstanding Graduate Student Supervisor”, and the Deputy Director of Qingdao Precision Nondestructive Intelligent Inspection Technology and Equipment Manufacturing Engineering Research Center. He has published more than 100 papers in academic journals and conferences, among which more than 70 papers have been retrieved by SCI, 3 academic books, 5 invention patents, more than 30 utility model patents, 8 computer software copyrights. He has been awarded the first prize of technical invention in Chinese universities, the second prize of Shandong Province Scientific and Technological Progress, the first prize of Shandong Province Teaching Achievements, the first prize of Shandong Province Graduate Education Teaching Achievements, and other teaching and scientific research awards. His current research areas include advanced design and intelligent manufacturing of machinery, lightweight material forming and joining process, additive manufacturing (3D printing), automotive safety and lightweight technology, and AI-based defect detection technology. Engineer JianGang Gao, a PhD candidate at Ocean University of China, serves as a member of the National Standardization Committee and Director of Digital Operations at Tercelo Tire Group Co., Ltd. With long-term dedication to the research and application of intelligent manufacturing technologies in the tire industry, he is strongly committed to the deep integration of industry, academia, and research. He actively facilitates effective collaboration between academic research and industrial practice. Having led the implementation of numerous industrial big data and digital twin technology projects, he has also contributed to the development of over 20 international and national industry standards, significantly advancing technological innovation and standardization in the field.    

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