Multiobjective Geometric Programming Problem Under Uncertainty
Multiobjective geometric programming (MOGP) is a powerful optimization technique widely used for solving a variety of nonlinear optimization problems and engineering problems. Generally, the parameters of a multiobjective geometric programming (MOGP) models are assumed to be deterministic and fixed....
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| Published in: | Operations research and decisions Vol. 27; no. no. 4; pp. 85 - 109 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Wrocław University of Science and Technology
01.01.2017
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| ISSN: | 2081-8858, 2391-6060 |
| Online Access: | Get full text |
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| Summary: | Multiobjective geometric programming (MOGP) is a powerful optimization technique widely used for solving a variety of nonlinear optimization problems and engineering problems. Generally, the parameters of a multiobjective geometric programming (MOGP) models are assumed to be deterministic and fixed. However, the values observed for the parameters in real-world MOGP problems are often imprecise and subject to fluctuations. Therefore, we use MOGP within an uncertainty based framework and propose a MOGP model whose coefficients are uncertain in nature. We assume the uncertain variables (UVs) to have linear, normal or zigzag uncertainty distributions and show that the corresponding uncertain chance-constrained multiobjective geometric programming (UCCMOGP) problems can be transformed into conventional MOGP problems to calculate the objective values. The paper develops a procedure to solve a UCCMOGP problem using an MOGP technique based on a weighted-sum method. The efficacy of this procedure is demonstrated by some numerical examples. (original abstract) |
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| ISSN: | 2081-8858 2391-6060 |
| DOI: | 10.5277/ord170405 |