Multi-objective optimisation of reliable product-plant network configuration
Abstract Ensuring manufacturing reliability is key to satisfying product orders when production plants are subject to disruptions. Reliability of a supply network is closely related to the redundancy of products as production in disrupted plants can be replaced by alternative plants. However the ben...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2018-01-01
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| Series: | Applied Network Science |
| Online Access: | http://link.springer.com/article/10.1007/s41109-017-0058-8 |
| _version_ | 1818966779655880704 |
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| author | Alexandra Brintrup Alena Puchkova |
| author_facet | Alexandra Brintrup Alena Puchkova |
| author_sort | Alexandra Brintrup |
| collection | DOAJ |
| description | Abstract Ensuring manufacturing reliability is key to satisfying product orders when production plants are subject to disruptions. Reliability of a supply network is closely related to the redundancy of products as production in disrupted plants can be replaced by alternative plants. However the benefits of incorporating redundancy must be balanced against the costs of doing so. Models in literature are highly case specific and do not consider complex network structures and redundant distributions of products over suppliers, that are evident in empirical literature. In this paper we first develop a simple generic measure for evaluating the reliability of a network of plants in a given product-plant configuration. Second, we frame the problem as a multi-objective evolutionary optimisation model to show that such a measure can be used to optimise the cost-reliability trade off. The model has been applied to a producer’s automotive light and lamp production network using three popular genetic algorithms designed for multi-objective problems, namely, NSGA2, SPEA2 and PAES. Using the model in conjunction with genetic algorithms we were able to find trade off solutions successfully. NSGA2 has achieved the best results in terms of Pareto front spread. Algorithms differed considerably in their performance, meaning that the choice of algorithm has significant impact in the resulting search space exploration. |
| first_indexed | 2024-12-20T13:38:20Z |
| format | Article |
| id | doaj.art-b99b7c031d0f48b3897b62f702a8b4b4 |
| institution | Directory Open Access Journal |
| issn | 2364-8228 |
| language | English |
| last_indexed | 2024-12-20T13:38:20Z |
| publishDate | 2018-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Applied Network Science |
| spelling | doaj.art-b99b7c031d0f48b3897b62f702a8b4b42022-12-21T19:38:53ZengSpringerOpenApplied Network Science2364-82282018-01-013111910.1007/s41109-017-0058-8Multi-objective optimisation of reliable product-plant network configurationAlexandra Brintrup0Alena Puchkova1Department of Engineering, Institute for Manufacturing, University of CambridgeDepartment of Engineering, Institute for Manufacturing, University of CambridgeAbstract Ensuring manufacturing reliability is key to satisfying product orders when production plants are subject to disruptions. Reliability of a supply network is closely related to the redundancy of products as production in disrupted plants can be replaced by alternative plants. However the benefits of incorporating redundancy must be balanced against the costs of doing so. Models in literature are highly case specific and do not consider complex network structures and redundant distributions of products over suppliers, that are evident in empirical literature. In this paper we first develop a simple generic measure for evaluating the reliability of a network of plants in a given product-plant configuration. Second, we frame the problem as a multi-objective evolutionary optimisation model to show that such a measure can be used to optimise the cost-reliability trade off. The model has been applied to a producer’s automotive light and lamp production network using three popular genetic algorithms designed for multi-objective problems, namely, NSGA2, SPEA2 and PAES. Using the model in conjunction with genetic algorithms we were able to find trade off solutions successfully. NSGA2 has achieved the best results in terms of Pareto front spread. Algorithms differed considerably in their performance, meaning that the choice of algorithm has significant impact in the resulting search space exploration.http://link.springer.com/article/10.1007/s41109-017-0058-8 |
| spellingShingle | Alexandra Brintrup Alena Puchkova Multi-objective optimisation of reliable product-plant network configuration Applied Network Science |
| title | Multi-objective optimisation of reliable product-plant network configuration |
| title_full | Multi-objective optimisation of reliable product-plant network configuration |
| title_fullStr | Multi-objective optimisation of reliable product-plant network configuration |
| title_full_unstemmed | Multi-objective optimisation of reliable product-plant network configuration |
| title_short | Multi-objective optimisation of reliable product-plant network configuration |
| title_sort | multi objective optimisation of reliable product plant network configuration |
| url | http://link.springer.com/article/10.1007/s41109-017-0058-8 |
| work_keys_str_mv | AT alexandrabrintrup multiobjectiveoptimisationofreliableproductplantnetworkconfiguration AT alenapuchkova multiobjectiveoptimisationofreliableproductplantnetworkconfiguration |