Modeling and design of material recovery facilities : genetic algorithm approach
Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2015.
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| Other Authors: | |
| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2015
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| Online Access: | http://hdl.handle.net/1721.1/98715 |
| _version_ | 1811086285712916480 |
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| author | Testa, Mariapaola |
| author2 | Stephen C. Graves. |
| author_facet | Stephen C. Graves. Testa, Mariapaola |
| author_sort | Testa, Mariapaola |
| collection | MIT |
| description | Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2015. |
| first_indexed | 2024-09-23T13:23:45Z |
| format | Thesis |
| id | mit-1721.1/98715 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T13:23:45Z |
| publishDate | 2015 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/987152019-04-10T15:06:59Z Modeling and design of material recovery facilities : genetic algorithm approach Material recovery facilities : genetic algorithm approach Testa, Mariapaola Stephen C. Graves. Massachusetts Institute of Technology. Operations Research Center. Massachusetts Institute of Technology. Operations Research Center. Operations Research Center. Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2015. Cataloged from PDF version of thesis. Includes bibliographical references (pages 187-193). In the Organisation for Economic Co-operation and Development (OECD) area, the production of numerical solid waste (MI\SW) increased by 32% between 1990 and 2011, exceeding 660 million tonnes in 2011; the world-wide production of waste is estimated to grow further due to increasing GDP in developing economies. Given this scenario, effective treatment and recovery of wastes becomes a priority. In developed countries, MSW is usually sent to materials recovery facilities (MRFs), which use mechanical and manual sorting units to extract valuable components. In this work, we define a network flow model to represent a MRF that sorts wastes using multi-output units with recirculating streams. For each material in the system, we define a matrix to describe the sorting process. We then formulate a genetic algorithm (GA) that generates alternative configurations of a MRF having a given set of sorting units with known separation parameters and selects those with highest profit and efficiency. The GA incorporates a heuristic for personnel allocation to manual units. We code the algorithm in Java and apply it to an existing MRF. The results show a 33.4% improvement in profit and a 1.7% improvement in efficiency with respect to the current configuration without hand sorting; and a 6.7% improvement in profit and a 3.9% improvement il efficiency, with respect to the current configuration with hand sorting. by Mariapaola Testa. S.M. 2015-09-17T19:06:59Z 2015-09-17T19:06:59Z 2015 2015 Thesis http://hdl.handle.net/1721.1/98715 920691527 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 193 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Operations Research Center. Testa, Mariapaola Modeling and design of material recovery facilities : genetic algorithm approach |
| title | Modeling and design of material recovery facilities : genetic algorithm approach |
| title_full | Modeling and design of material recovery facilities : genetic algorithm approach |
| title_fullStr | Modeling and design of material recovery facilities : genetic algorithm approach |
| title_full_unstemmed | Modeling and design of material recovery facilities : genetic algorithm approach |
| title_short | Modeling and design of material recovery facilities : genetic algorithm approach |
| title_sort | modeling and design of material recovery facilities genetic algorithm approach |
| topic | Operations Research Center. |
| url | http://hdl.handle.net/1721.1/98715 |
| work_keys_str_mv | AT testamariapaola modelinganddesignofmaterialrecoveryfacilitiesgeneticalgorithmapproach AT testamariapaola materialrecoveryfacilitiesgeneticalgorithmapproach |