Design Method: Deep Operations Research

Yong Li, Xunchen Liu, Shanling Han and Jian‘gang Gao
Additional contact information
Yong Li: Shandong University of Science and Technology, College of Mechanical and Electronic Engineering
Xunchen Liu: Shandong University of Science and Technology, College of Mechanical and Electronic Engineering
Shanling Han: Shandong University of Science and Technology, College of Mechanical and Electronic Engineering
Jian‘gang Gao: Ocean University of China, Faculty of Information Science and Engineering

Chapter Chapter 5 in Intelligent Manufacturing Engineering, 2026, pp 393-473 from Springer

Abstract: Abstract What is the product of the IME system?—Guidelines. How does the system produce guidelines?—Deep Operations Research (DOR). The value of the IME system lies in uncovering the underlying laws and innovative guidelines that govern enterprise operations. Intelligent Manufacturing Engineering: Deep Operations Research is grounded in the DOR framework proposed by the authors. This method employs the quadflow, including quality flow, cash flow, information flow, material flow, and more, to represent the heterogeneous tensors of intelligent system nodes. It constructs the community and hierarchical complex network of the system, develops a graph neural network algorithm tailored for IME to extract features, and identifies the intrinsic relations among elements, thereby innovating the operational criteria of the system. To understand the heterogeneous hierarchical complex networks composed of MES, ERP, APS, CRM, WMS, and PLM of IME system, and realize the deep mining of industrial data around production scheduling optimization, logistics path planning, customer relation optimization, product development, and network security. Master the design method of the IME system with DOR as the underlying logic and PyTorch as the platform.

Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-95-3109-7_5

Ordering information: This item can be ordered from
http://www.springer.com/9789819531097

DOI: 10.1007/978-981-95-3109-7_5

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().