The multi-objective fuzzy mathematical programming model for humanitarian relief logistics
© 2020 KIIE. The concern about humanitarian relief logistics has spurred an interest in disaster management. The use of warehouses for storing relief items is one of the most important parts that can help the victims immediately during a disaster occurrence. Effective warehouse location selection is...
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| Main Authors: | , |
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| Format: | Journal |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85083585061&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/70506 |
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| Institution: | Chiang Mai University |
| Summary: | © 2020 KIIE. The concern about humanitarian relief logistics has spurred an interest in disaster management. The use of warehouses for storing relief items is one of the most important parts that can help the victims immediately during a disaster occurrence. Effective warehouse location selection is quite difficult since there are several criteria such as budget, response time, and uncertainty of data. Thus, this study aims to propose a multi-objective fuzzy mathematical programming model for humanitarian relief logistics. This study presents an integrated facility location problem, inventory problem and distribution problem in humanitarian relief logistics under consideration of the inherent uncertainty of input parameters. The proposed model focuses on minimization of the response time and the planning budget that determines the location of warehouses, total inventory to be stored in each warehouse, distributed management of the warehouses, and the holding utilization of the warehouses. To solve the proposed multi-objective optimization model with fuzzy parameters, an equivalent auxiliary crisp model and an epsilon constraint approach are adopted in this study. A real case study in Pichit province, Thailand, is employed to validate the proposed model. Finally, the results are proposed and discussed that represent several alternatives to decision makers. |
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