Developing a decision support system for improving sustainability performance of manufacturing processes
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 dat...
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| Veröffentlicht in: | Journal of intelligent manufacturing 2017-08, Vol.28 (6), p.1421-1440 |
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| container_title | Journal of intelligent manufacturing |
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| creator | Shin, Seung-Jun Kim, Duck Bong Shao, Guodong Brodsky, Alexander Lechevalier, David |
| description | 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. |
| doi_str_mv | 10.1007/s10845-015-1059-z |
| format | Article |
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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.</description><identifier>ISSN: 0956-5515</identifier><identifier>EISSN: 1572-8145</identifier><identifier>DOI: 10.1007/s10845-015-1059-z</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Business and Management ; Case studies ; Control ; Decision support systems ; Energy consumption ; Machines ; Manufacturing ; Manufacturing industry ; Mechatronics ; Modelling ; Optimization ; Process parameters ; Processes ; Production ; Resource allocation ; Robotics ; Studies ; Sustainability</subject><ispartof>Journal of intelligent manufacturing, 2017-08, Vol.28 (6), p.1421-1440</ispartof><rights>Springer Science+Business Media New York (outside the USA) 2015</rights><rights>Journal of Intelligent Manufacturing is a copyright of Springer, 2017.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c369t-4727275a5f27027c439a1f6ae15774913dcad15929f8519e36f8e8b6e44a590b3</citedby><cites>FETCH-LOGICAL-c369t-4727275a5f27027c439a1f6ae15774913dcad15929f8519e36f8e8b6e44a590b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10845-015-1059-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10845-015-1059-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27929,27930,41493,42562,51324</link.rule.ids></links><search><creatorcontrib>Shin, Seung-Jun</creatorcontrib><creatorcontrib>Kim, Duck Bong</creatorcontrib><creatorcontrib>Shao, Guodong</creatorcontrib><creatorcontrib>Brodsky, Alexander</creatorcontrib><creatorcontrib>Lechevalier, David</creatorcontrib><title>Developing a decision support system for improving sustainability performance of manufacturing processes</title><title>Journal of intelligent manufacturing</title><addtitle>J Intell Manuf</addtitle><description>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. 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| subjects | Business and Management Case studies Control Decision support systems Energy consumption Machines Manufacturing Manufacturing industry Mechatronics Modelling Optimization Process parameters Processes Production Resource allocation Robotics Studies Sustainability |
| title | Developing a decision support system for improving sustainability performance of manufacturing processes |
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