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

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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.

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Acknowledgments

The authors thank Abdullah Alrazgan, a graduate student from George Mason University, for his effort on SPAF compiler development. The work represented here was partially funded through cooperative agreement #70NANB12H277 between George Mason University and NIST.

Conflict of interest No approval or endorsement of any commercial product by the National Institute of Standards and Technology is intended or implied. Certain commercial software systems are identified in this paper to facilitate understanding. Such identification does not imply that these software systems are necessarily the best available for the purpose.

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Authors and Affiliations

  1. Systems Integration Division, Engineering Laboratory, National Institute of Standards and Technology, 100 Bureau Drive, MS 8260, Gaithersburg, MD, 20899-8260, USA

    Seung-Jun Shin, Duck Bong Kim, Guodong Shao & David Lechevalier

  2. Department of Computer Science, George Mason University, 4400 University Drive, MS 4A5, Fairfax, VA, 22030-4444, USA

    Alexander Brodsky

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  1. Seung-Jun Shin
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  2. Duck Bong Kim
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Correspondence to Duck Bong Kim.

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Shin, SJ., Kim, D.B., Shao, G. et al. Developing a decision support system for improving sustainability performance of manufacturing processes. J Intell Manuf 28, 1421–1440 (2017). https://doi.org/10.1007/s10845-015-1059-z

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