Location optimization of multiple distribution centers under fuzzy environment
Locating distribution centers optimally is a crucial and systematic task for decision-makers. Optimally located distribution centers can significantly improve the logistics system’s efficiency and reduce its operational costs. However, it is not an easy task to optimize distribution center locations and previous studies focused primarily on location optimization of a single distribution center. With growing logistics demands, multiple distribution centers become necessary to meet customers’ requirements, but few studies have tackled the multiple distribution center locations (MDCLs) problem. This paper presents a comprehensive algorithm to address the MDCLs problem. Fuzzy integration and clustering approach using the improved axiomatic fuzzy set (AFS) theory is developed for location clustering based on multiple hierarchical evaluation criteria. Then, technique for order preference by similarity to ideal solution (TOPSIS) is applied for evaluating and selecting the best candidate for each cluster. Sensitivity analysis is also conducted to assess the influence of each criterion in the location planning decision procedure. Results from a case study in Guiyang, China, reveals that the proposed approach developed in this study outperforms other similar algorithms for MDCLs selection. This new method may easily be extended to address location planning of other types of facilities, including hospitals, fire stations and schools.
Key wordsMultiple distribution centers Location selection Clustering algorithm Axiomatic fuzzy set (AFS) Technique for order preference by similarity to ideal solution (TOPSIS)
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- Chou, S.Y., Chang, Y.H., Shen, C.Y., 2008. A fuzzy simple additive weighting system under group decision making for facility location selection with objective/subjective attributes. European Journal of Operational Research, 189(1):132–145. [doi:10.1016/j.ejor.2007.05.006]zbMATHCrossRefGoogle Scholar
- José, L.P.T., Eugenio, P., Víctor, Y., 2012. Complete fuzzy scheduling and fuzzy earned value management in construction projects. Journal of Zhejiang University-SCIENCE A (Applied Physics and Engineering), 13(1): 56–68. [doi:10.1631/jzus.A1100160]Google Scholar
- Küçükaydin, H., Aras, N., Altınel, I.K., 2011. Competitive facility location problem with attractiveness adjustment of the follower: A bi-level programming model and its solution. European Journal of Operational Research, 208(3):206–220. [doi:10.1016/j.ejor.2010.08.009]MathSciNetzbMATHCrossRefGoogle Scholar
- Negi, D.S., 1984. Fuzzy Analysis and Optimization. PhD Thesis, Department of Industrial Engineering, Kansas State University, Kansas, USA.Google Scholar