Service Family Design Optimization Considering a Multi-Server Queue
Service firms not only need to develop differentiated services to meet the requirements of customers with various preferences, but also have to improve service flexibility and the efficiency of the service system. A service family is a strategy by which different modules are configured, based on the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9382985/ |
| _version_ | 1830198354242437120 |
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| author | Zhuotong Miao Xinggang Luo Zhongliang Zhang Qing Zhou |
| author_facet | Zhuotong Miao Xinggang Luo Zhongliang Zhang Qing Zhou |
| author_sort | Zhuotong Miao |
| collection | DOAJ |
| description | Service firms not only need to develop differentiated services to meet the requirements of customers with various preferences, but also have to improve service flexibility and the efficiency of the service system. A service family is a strategy by which different modules are configured, based on the service platform, to create a variety of differentiated services. This research considered both the effect of multi-server queues and the heterogeneous service processes in service family design problems to establish a framework of service modularization from three different perspectives—process, activity, and component. To optimize the service family design, a nonlinear integer-programming model was established to determine the optimal configurations of modules and prices for the service family and the optimal number of servers. The model is transformed into a linear form, and thus, can be solved using a commercial optimization software for small-scale problems. An improved genetic algorithm integrated with a neighborhood search was further developed to solve large-scale problems. The correctness of the linearized model and the effectiveness of the meta-heuristic algorithm were demonstrated through case studies and numerical experiments. |
| first_indexed | 2024-12-18T01:52:14Z |
| format | Article |
| id | doaj.art-640edfaf38824adf86ef50e6099876ff |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T01:52:14Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-640edfaf38824adf86ef50e6099876ff2022-12-21T21:25:00ZengIEEEIEEE Access2169-35362021-01-019514325145110.1109/ACCESS.2021.30680089382985Service Family Design Optimization Considering a Multi-Server QueueZhuotong Miao0https://orcid.org/0000-0002-0375-1518Xinggang Luo1https://orcid.org/0000-0002-7689-8449Zhongliang Zhang2https://orcid.org/0000-0001-6555-7908Qing Zhou3College of Information Science and Engineering, Northeastern University, Shenyang, ChinaCollege of Information Science and Engineering, Northeastern University, Shenyang, ChinaManagement School, Hangzhou Dianzi University, Hangzhou, ChinaManagement School, Hangzhou Dianzi University, Hangzhou, ChinaService firms not only need to develop differentiated services to meet the requirements of customers with various preferences, but also have to improve service flexibility and the efficiency of the service system. A service family is a strategy by which different modules are configured, based on the service platform, to create a variety of differentiated services. This research considered both the effect of multi-server queues and the heterogeneous service processes in service family design problems to establish a framework of service modularization from three different perspectives—process, activity, and component. To optimize the service family design, a nonlinear integer-programming model was established to determine the optimal configurations of modules and prices for the service family and the optimal number of servers. The model is transformed into a linear form, and thus, can be solved using a commercial optimization software for small-scale problems. An improved genetic algorithm integrated with a neighborhood search was further developed to solve large-scale problems. The correctness of the linearized model and the effectiveness of the meta-heuristic algorithm were demonstrated through case studies and numerical experiments.https://ieeexplore.ieee.org/document/9382985/Linearizationmulti-sever queueoptimizationservice family design |
| spellingShingle | Zhuotong Miao Xinggang Luo Zhongliang Zhang Qing Zhou Service Family Design Optimization Considering a Multi-Server Queue IEEE Access Linearization multi-sever queue optimization service family design |
| title | Service Family Design Optimization Considering a Multi-Server Queue |
| title_full | Service Family Design Optimization Considering a Multi-Server Queue |
| title_fullStr | Service Family Design Optimization Considering a Multi-Server Queue |
| title_full_unstemmed | Service Family Design Optimization Considering a Multi-Server Queue |
| title_short | Service Family Design Optimization Considering a Multi-Server Queue |
| title_sort | service family design optimization considering a multi server queue |
| topic | Linearization multi-sever queue optimization service family design |
| url | https://ieeexplore.ieee.org/document/9382985/ |
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