Decision Support for Sustainable Manufacturing using Decision Guidance Query Language

Published

July 19, 2011

Author(s)

Guodong Shao, Deogratias Kibira, Alexander Brodsky , Nathan Egge

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

Pub Type

Journals

Keywords

sustainable manufacturing, sustainable machining, decision guidance management system, decision guidance query language, life cycle assessment, optimization

Citation

Shao, G. , Kibira, D. , Brodsky, A. and , N. (2011), Decision Support for Sustainable Manufacturing using Decision Guidance Query Language, International Journal of Sustainable Engineering (Accessed December 11, 2024)