• Real-Time Optimization of Factory Operations

Improve Processes in Real-Time by Integration 
of Production Scheduling with Automation Logic

 
As products become more diversified and customized, chemical manufacturing and discrete parts manufacturing are moving toward multi-product facilities that utilize shared resources according to a demand profile. These facilities' performances depend on high quality production planning and scheduling, as well as the accurate implementation of these plans in the manufacturing process. At times, human intervention is required to rework plans and schedules in order to keep them feasible.

This course will present a new methodology for addressing the integration of production planning and scheduling with the discrete logic of the process automation system, thereby closing a capability gap in achieving real-time optimization of factory operations. This new methodology, termed Manufacturing Execution Optimization (MEO), is the result of a collaborative effort involving The Dow Chemical Company, the University of Michigan, the University of Wisconsin, Siemens Corporation, and Kent Displays Inc., under funding from the Digital Manufacturing and Design Innovation Institute (DMDII).
 

Program Agenda

 
Day 1: Overview, Integration of Scheduling & Automation Logic, Simulation

Morning: (3 hours)

  • Course description; presentation of test problem: Lafortune
  • Primer on chemical production scheduling and factory operations: Maravelias and Wassick
  • Simulation of factory operations using SIMIT: Nandola

Afternoon: (3 hours)

  • Real-time optimization of schedules in factory operations: Maravelias
  • Automation logic and its integration with real-time scheduling: Rawlings and Lafortune
Day 2: Implementation of Real-Time Optimization

Morning: (3 hours)

  • Demonstration of integrated approach on case study using software tools: Team
  • Implementation of dynamic real-time optimization of full-scale factory operations: Dow’s experience: Lin and Wassick

Afternoon: (up to 2 hours; end by 3pm)

  • Discussion, more Q&A, wrap up: Team

Learning Objectives

The integrated MEO methodology for real-time optimization that will be taught is composed of:

  • a scheduling optimization model enhanced to consider automation logic
  • a delay monitoring module that monitors the feasibility or lack thereof of the current schedule under the constraints of the automation logic and triggers, as necessary, schedule re-optimization in real time

The course will present the various steps of the integrated MEO methodology, along with demonstration of software tools that implement its key elements. In addition, the course will present a detailed simulation environment for chemical processes in plant operations, employing the tool SIMIT of Siemens Corp., that mimics both process dynamics and automation logic and can be used for high-fidelity analysis of system performance. To make the course as self-contained as possible, some fundamentals on chemical production scheduling and on automation logic in process control systems will also be introduced.

 

Instructors

 
 
Stéphane Lafortune
Stéphane Lafortune
 
Bao Lin
Bao Lin
 
Christos Maravelias
Christos Maravelias
 
Nareshkumar Nandola
Nareshkumar Nandola
 
Blake Rawlings
Blake Rawlings
 
John Wassick
John Wassick