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Publication Citation: Decision Support for Sustainable Manufacturing using Decision Guidance Query Language

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Author(s): Guodong Shao; Deogratias Kibira; Alexander Brodsky; Nathan Egge;
Title: Decision Support for Sustainable Manufacturing using Decision Guidance Query Language
Published: July 19, 2011
Abstract: Sustainability has become a very significant research topic as it impacts many different manufacturing industries. Therefore, the technologies for monitoring, analyzing, evaluating, and optimizing the sustainability performance indicators for manufacturing processes and systems are very critical for decision makers on the shop floor. This paper introduces a Decision Guidance Management System (DGMS) that provides actionable recommendations through quantitative analysis of the sustainability measures of manufacturing processes and systems based on Life Cycle Assessment (LCA). The system determines decision preferences using dynamically collected data and decision makers‰ responses, taking into account the prevailing constraints. Optimal decisions can be derived using mathematical and constraint programming. By using Decision Guidance Query Language (DGQL), this methodology allows users to make optimal decisions without an extensive mathematical or operations research background. Knowledge of relational databases is sufficient for a user to formulate the optimization problem and obtain optimal solutions. The methodology is demonstrated with a machining operation case study, in which a list of sustainability metrics for machining are identified, and sustainability modeling methods are proposed. Important sustainable machining performance measures are optimized, resulting in actionable recommendations. [Journal URL: http://www.tandfonline.com/doi/abs/10.1080/19397038.2011.574741#preview ]
Citation: International Journal of Sustainable Engineering
Volume: 4
Issue: 3
Pages: pp. 251 - 265
Keywords: sustainable manufacturing; sustainable machining; decision guidance management system; decision guidance query language; life cycle assessment; optimization
Research Areas: Sustainable Manufacturing, Energy, Databases