Multiobjective Optimization Model of Production Planning in Cloud Manufacturing Based on TOPSIS Method with Combined Weights
Combined with the research of mass customization and cloud manufacturing mode, this paper discussed the production planning of mass customization enterprises in the context of cloud manufacturing in detail, analyzed the attribute index of manufacturing resource combination, and given a system consid...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/7503176 |
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| _version_ | 1832552687966617600 |
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| author | Zhiru Li Wei Xu Huibin Shi Qingshan Zhang Fengyi He |
| author_facet | Zhiru Li Wei Xu Huibin Shi Qingshan Zhang Fengyi He |
| author_sort | Zhiru Li |
| collection | DOAJ |
| description | Combined with the research of mass customization and cloud manufacturing mode, this paper discussed the production planning of mass customization enterprises in the context of cloud manufacturing in detail, analyzed the attribute index of manufacturing resource combination, and given a system considering the characteristics of batch production in mass customization and the decentralization of manufacturing resources in cloud manufacturing environment. Then, a multiobjective optimization model has been constructed according to the product delivery date, product cost, and product quality that customers care most about. The Pareto solution set of production plan has been obtained by using NSGA-II algorithm. This paper established a six-tier attribute index system evaluation model for the optimization of production planning scheme set of mass customization enterprises in cloud manufacturing environment. The weight coefficients of attribute indexes were calculated by combining subjective and objective weights with analytic hierarchy process (AHP) and entropy weight method. Finally, the combined weights calculated were applied to the improved TOPSIS method, and the optimal production planning scheme has been obtained by ranking. This paper validated the effectiveness and feasibility of the multiobjective model and NSGA-II algorithm by the example of company A. The Pareto effective solution has been obtained by solving the model. Then the production plan set has been sorted synthetically according to the comprehensive evaluation model, and the optimal production plan has been obtained. |
| format | Article |
| id | doaj-art-13d4742909f24dc3b5768c506f36062b |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-13d4742909f24dc3b5768c506f36062b2025-02-03T05:58:03ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/75031767503176Multiobjective Optimization Model of Production Planning in Cloud Manufacturing Based on TOPSIS Method with Combined WeightsZhiru Li0Wei Xu1Huibin Shi2Qingshan Zhang3Fengyi He4School of Management, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Management, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Management, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Management, Shenyang University of Technology, Shenyang 110870, ChinaSchool of Management, Shenyang University of Technology, Shenyang 110870, ChinaCombined with the research of mass customization and cloud manufacturing mode, this paper discussed the production planning of mass customization enterprises in the context of cloud manufacturing in detail, analyzed the attribute index of manufacturing resource combination, and given a system considering the characteristics of batch production in mass customization and the decentralization of manufacturing resources in cloud manufacturing environment. Then, a multiobjective optimization model has been constructed according to the product delivery date, product cost, and product quality that customers care most about. The Pareto solution set of production plan has been obtained by using NSGA-II algorithm. This paper established a six-tier attribute index system evaluation model for the optimization of production planning scheme set of mass customization enterprises in cloud manufacturing environment. The weight coefficients of attribute indexes were calculated by combining subjective and objective weights with analytic hierarchy process (AHP) and entropy weight method. Finally, the combined weights calculated were applied to the improved TOPSIS method, and the optimal production planning scheme has been obtained by ranking. This paper validated the effectiveness and feasibility of the multiobjective model and NSGA-II algorithm by the example of company A. The Pareto effective solution has been obtained by solving the model. Then the production plan set has been sorted synthetically according to the comprehensive evaluation model, and the optimal production plan has been obtained.http://dx.doi.org/10.1155/2019/7503176 |
| spellingShingle | Zhiru Li Wei Xu Huibin Shi Qingshan Zhang Fengyi He Multiobjective Optimization Model of Production Planning in Cloud Manufacturing Based on TOPSIS Method with Combined Weights Complexity |
| title | Multiobjective Optimization Model of Production Planning in Cloud Manufacturing Based on TOPSIS Method with Combined Weights |
| title_full | Multiobjective Optimization Model of Production Planning in Cloud Manufacturing Based on TOPSIS Method with Combined Weights |
| title_fullStr | Multiobjective Optimization Model of Production Planning in Cloud Manufacturing Based on TOPSIS Method with Combined Weights |
| title_full_unstemmed | Multiobjective Optimization Model of Production Planning in Cloud Manufacturing Based on TOPSIS Method with Combined Weights |
| title_short | Multiobjective Optimization Model of Production Planning in Cloud Manufacturing Based on TOPSIS Method with Combined Weights |
| title_sort | multiobjective optimization model of production planning in cloud manufacturing based on topsis method with combined weights |
| url | http://dx.doi.org/10.1155/2019/7503176 |
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