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Developing a decision support system for improving sustainability performance of manufacturing processes

Published

Author(s)

Seungjun Shin, Duck B. Kim, Guodong Shao, Alexander Brodsky, David J. Lechevalier

Abstract

It is difficult to formulate and solve optimization problems for sustainability performance in manufacturing. The main reasons for this are: 1) optimization problems are typically complex and involve manufacturing and sustainability aspects, 2) these problems require diversity of manufacturing data, 3) optimization modeling and solving tasks require specialized expertise and programming skills, 4) the use of a different optimization application requires re-modeling of optimization problems even for the same problem, and 5) these optimization models are not decomposed nor reusable. This paper presents the development of a Decision Support System (DSS) that enables manufacturers to formulate optimization problems at multiple manufacturing levels, to represent various manufacturing data, to create compatible and reusable models and to derive easily optimal solutions for improving sustainability performance. We have implemented a DSS prototype system and applied this system to two case studies. The case studies demonstrate how to allocate resources at the production level and how to select process parameters at the unit- process level to achieve minimal energy consumption. The research of this paper will help reduce time and effort for enhancing sustainability performance without heavily relying on optimization expertise.
Citation
Journal of Intelligent Manufacturing

Keywords

Sustainable manufacturing, process optimization, decision supporting system, sustainable process analytics formalism, energy consumption

Citation

Shin, S. , Kim, D. , Shao, G. , Brodsky, A. and Lechevalier, D. (2015), Developing a decision support system for improving sustainability performance of manufacturing processes, Journal of Intelligent Manufacturing, [online], https://doi.org/10.1007/s10845-015-1059-z (Accessed February 22, 2024)
Created March 3, 2015, Updated November 10, 2018