A target oriented robust optimization model for selection of engineering project portfolio under uncertainty
Budget constraints often force firms to select engineering projects to be implemented from a larger set of alternatives. The conventional approach makes use of 0-1 programming models to identify optimal portfolios for implementation. However, this approach requires deterministic process parameters i...
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| Main Authors: | , , |
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| Format: | text |
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Animo Repository
2017
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| Online Access: | https://animorepository.dlsu.edu.ph/faculty_research/1150 https://animorepository.dlsu.edu.ph/context/faculty_research/article/2149/type/native/viewcontent |
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| Institution: | De La Salle University |
| Summary: | Budget constraints often force firms to select engineering projects to be implemented from a larger set of alternatives. The conventional approach makes use of 0-1 programming models to identify optimal portfolios for implementation. However, this approach requires deterministic process parameters in the model, while in practice, techno-economic parameters are often not precisely known. Thus a target-oriented robust optimization (TORO) model is proposed here, based on a recently developed framework that allows optimization to be done with non-deterministic data defined as intervals. A range of solutions can be generated and then subjected to Monte Carlo simulation to elucidate the trade-off between the system robustness and technical performance level. This methodology is demonstrated with two case studies involving selection of engineering measures to reduce air emissions and safety hazards in industrial plants. Results show that it is possible to identify a robust set of engineering measures that performs well in terms of cost and probability of constraint achievement. Furthermore, TORO provides a better picture on how the system will perform under uncertainty. © 2017 Elsevier B.V. |
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