Production energy optimization using low dynamic programming, a decision support tool for sustainable manufacturing

Q. Zhu, F. Lujia, A. Mayyas, M. A. Omar, Y. Al-Hammadi, S. Al Saleh

Research output: Contribution to journalArticlepeer-review

54 Scopus citations


The presented study investigates the application of a Model Predictive Controller, equipped with linear-programming based optimizer, with application to energy management in production environments. The study focuses on an automotive OEM assembly plant that consumes fossil fuel (natural and landfill gas) in addition to electricity drawn from the grid. This manuscript details the optimization structure under two different cost functions; specifically, cost-savings and energy efficiency. The predicted results are in agreement with the current plant consumption and demonstrate the conflicting nature of the two cost models proposed; thusly, highlighting the importance of objective decision making tools, driven by specific performance criteria, in managing the energy and the overall sustainability of production environments. Additionally, the study discusses the role of the co-generation process efficiency on the overall plant energy consumption.

Original languageEnglish (US)
Pages (from-to)178-183
Number of pages6
JournalJournal of Cleaner Production
StatePublished - Oct 15 2015
Externally publishedYes


  • Energy management
  • Energy prediction
  • Manufacturing system
  • Simulation

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Building and Construction
  • General Environmental Science
  • Strategy and Management
  • Industrial and Manufacturing Engineering


Dive into the research topics of 'Production energy optimization using low dynamic programming, a decision support tool for sustainable manufacturing'. Together they form a unique fingerprint.

Cite this