Polymorphic uncertain nonlinear programming approach for maximizing the capacity of V-belt driving
In this paper, a polymorphic uncertain nonlinear programming (PUNP) approach is developed to formulate the problem of maximizing the capacity in a system of V-belt driving with uncertainties. The constructed optimization model is found to consist of a nonlinear objective function and some nonlinear...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Springer New York LLC
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/16852 |
| _version_ | 1848749295491088384 |
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| author | Wan, Z. Zhang, S. Teo, Kok Lay |
| author_facet | Wan, Z. Zhang, S. Teo, Kok Lay |
| author_sort | Wan, Z. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this paper, a polymorphic uncertain nonlinear programming (PUNP) approach is developed to formulate the problem of maximizing the capacity in a system of V-belt driving with uncertainties. The constructed optimization model is found to consist of a nonlinear objective function and some nonlinear constraints with some parameters which are of uncertain nature. These uncertain parameters are interval parameters, random interval parameters, fuzzy parameters or fuzzy interval parameters. To find a robust solution of the problem, a deterministic equivalent formulation (DEF) is established for the polymorphic uncertain nonlinear programming model. For a given satisfaction level, this DEF turns out to be a nonlinear programming involving only interval parameters. A solution method, called a sampling based interactive method, is developed such that a robust solution of the original model with polymorphic uncertainties is obtained by using standard smooth optimization techniques. The proposed method is applied into a real-world design of V-belt driving, and the results indicate that both the PUNP approach and the developed algorithm are useful to the optimization problem with polymorphic uncertainty. |
| first_indexed | 2025-11-14T07:18:40Z |
| format | Journal Article |
| id | curtin-20.500.11937-16852 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:18:40Z |
| publishDate | 2014 |
| publisher | Springer New York LLC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-168522017-09-13T15:42:44Z Polymorphic uncertain nonlinear programming approach for maximizing the capacity of V-belt driving Wan, Z. Zhang, S. Teo, Kok Lay Design optimization Polymorphic uncertainty Algorithm Belt drives Sampling method In this paper, a polymorphic uncertain nonlinear programming (PUNP) approach is developed to formulate the problem of maximizing the capacity in a system of V-belt driving with uncertainties. The constructed optimization model is found to consist of a nonlinear objective function and some nonlinear constraints with some parameters which are of uncertain nature. These uncertain parameters are interval parameters, random interval parameters, fuzzy parameters or fuzzy interval parameters. To find a robust solution of the problem, a deterministic equivalent formulation (DEF) is established for the polymorphic uncertain nonlinear programming model. For a given satisfaction level, this DEF turns out to be a nonlinear programming involving only interval parameters. A solution method, called a sampling based interactive method, is developed such that a robust solution of the original model with polymorphic uncertainties is obtained by using standard smooth optimization techniques. The proposed method is applied into a real-world design of V-belt driving, and the results indicate that both the PUNP approach and the developed algorithm are useful to the optimization problem with polymorphic uncertainty. 2014 Journal Article http://hdl.handle.net/20.500.11937/16852 10.1007/s11081-012-9205-3 Springer New York LLC restricted |
| spellingShingle | Design optimization Polymorphic uncertainty Algorithm Belt drives Sampling method Wan, Z. Zhang, S. Teo, Kok Lay Polymorphic uncertain nonlinear programming approach for maximizing the capacity of V-belt driving |
| title | Polymorphic uncertain nonlinear programming approach for maximizing the capacity of V-belt driving |
| title_full | Polymorphic uncertain nonlinear programming approach for maximizing the capacity of V-belt driving |
| title_fullStr | Polymorphic uncertain nonlinear programming approach for maximizing the capacity of V-belt driving |
| title_full_unstemmed | Polymorphic uncertain nonlinear programming approach for maximizing the capacity of V-belt driving |
| title_short | Polymorphic uncertain nonlinear programming approach for maximizing the capacity of V-belt driving |
| title_sort | polymorphic uncertain nonlinear programming approach for maximizing the capacity of v-belt driving |
| topic | Design optimization Polymorphic uncertainty Algorithm Belt drives Sampling method |
| url | http://hdl.handle.net/20.500.11937/16852 |