Multi-objective Stochastic Programming to Optimize Number Determination of Manpower Problem in Job Shop Manufacturing Systems
One of the most popular approaches to solve multi-objective stochastic problems is Chance Constrained Programming (CCP). Based on this approach, a deterministic equivalent model is presented for multi-objective stochastic problem and then is optimized by a solver model. In this Paper, both CCP appro...
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| Format: | Article |
| Language: | fas |
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Allameh Tabataba'i University Press
2010-12-01
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| Series: | Muṭāli̒āt-i Mudīriyyat-i Ṣan̒atī |
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| Online Access: | https://jims.atu.ac.ir/article_4476_a863134ff1f15b83a3c01463b93814cf.pdf |
| Summary: | One of the most popular approaches to solve multi-objective stochastic problems is Chance Constrained Programming (CCP). Based on this approach, a deterministic equivalent model is presented for multi-objective stochastic problem and then is optimized by a solver model. In this Paper, both CCP approach and the Global Criterion model are combined and a model will be proposed which is called Chance Constrained Global Criterion (min-max) (CCGC(min- max)). The proposed model is able to present a deterministic equivalent model for multi-objective stochastic problem and thereafter optimizes it. One of the subjects which always planners of manufacturing systems have concerned about, is determining the optimal number of manpower with regard to objectives such as to maximize the output, to minimize the wage cost and to minimize no allowance idle time. Hence to illustrate the proposed model, a multiobjective stochastic problem is presented for to determine the optimal number of manpower in a job shop manufacturing system under uncertainty. The optimization results of this problem using the proposed model and the CCCP(with min-max norm) model under the same conditions, express the proposed model with existence fewer number of variables and constraints compared to the CCCP(min-max) model, presents the wholly same results in fewer number of iterations. |
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| ISSN: | 2251-8029 2476-602X |