Data-Driven Optimization Framework for Next Generation Manufacturing

Manufacturing companies are competing on the basis of price, technical expertise and reliability in meeting due dates. In addition, dynamic market environments require more agility from manufactures in terms of achieving high production flexibility and cost effectiveness. The aim of this project is to provide a decision support system for manufactures to effectively plan and schedule their limited resources to achieve better delivery time and reduced cost in today’s dynamic market environments, even for smaller batch, customized products. The core technologies of this system hinges on two pillars: machine learning and optimization. While the overall system functionalities are delivered through a software deployment, the actual power of optimization under uncertainty is obtained from stochastic programming and robust programming optimization models. Ontologies also play a central role in this project. They enable fluent and consistent flows of data both inside and outside the company.

Faculty Supervisor:

Chun Wang

Student:

Partner:

Promark Electronics

Discipline:

Engineering

Sector:

Manufacturing

University:

Concordia University

Program: