New methods for solving a vertex p-center problem with uncertain demand-weighted distance: A real case study

Nematian, J.; Sadati, M.

Growing Science » International Journal of Industrial Engineering Computations » New methods for solving a vertex p-center problem with uncertain demand-weighted distance: A real case study

Journals

IJIEC Volumes

Volume 1 (17)
- Issue 1 (9)
- Issue 2 (8)

Volume 2 (68)
- Issue 1 (12)
- Issue 2 (20)
- Issue 3 (20)
- Issue 4 (16)

Volume 3 (76)
- Issue 1 (9)
- Issue 2 (15)
- Issue 3 (20)
- Issue 4 (12)
- Issue 5 (20)

Volume 4 (50)
- Issue 1 (14)
- Issue 2 (10)
- Issue 3 (12)
- Issue 4 (14)

Volume 5 (47)
- Issue 1 (13)
- Issue 2 (12)
- Issue 3 (12)
- Issue 4 (10)

Volume 6 (39)
- Issue 1 (7)
- Issue 2 (12)
- Issue 3 (10)
- Issue 4 (10)

Volume 7 (47)
- Issue 1 (10)
- Issue 2 (14)
- Issue 3 (10)
- Issue 4 (13)

Volume 8 (30)
- Issue 1 (9)
- Issue 2 (7)
- Issue 3 (8)
- Issue 4 (6)

Volume 9 (32)
- Issue 1 (9)
- Issue 2 (6)
- Issue 3 (7)
- Issue 4 (10)

Volume 10 (34)
- Issue 1 (8)
- Issue 2 (10)
- Issue 3 (8)
- Issue 4 (8)

Volume 11 (36)
- Issue 1 (9)
- Issue 2 (8)
- Issue 3 (9)
- Issue 4 (10)

Volume 12 (29)
- Issue 1 (9)
- Issue 2 (6)
- Issue 3 (8)
- Issue 4 (6)

Volume 13 (41)
- Issue 1 (10)
- Issue 2 (8)
- Issue 3 (10)
- Issue 4 (13)

Volume 14 (50)
- Issue 1 (11)
- Issue 2 (15)
- Issue 3 (9)
- Issue 4 (15)

Volume 15 (55)
- Issue 1 (19)
- Issue 2 (15)
- Issue 3 (12)
- Issue 4 (9)

Volume 16 (12)
- Issue 1 (12)

Countries

International Journal of Industrial Engineering Computations

ISSN 1923-2934 (Online) - ISSN 1923-2926 (Print) Quarterly Publication Volume 6 Issue 2 pp. 253-266 , 2015

New methods for solving a vertex p-center problem with uncertain demand-weighted distance: A real case study Pages 253-266 Download PDF

Authors: Javad Nematian, Mir Ehsan Hesam Sadati

DOI: 10.5267/j.ijiec.2014.10.004

Keywords: Fuzzy random chance-constrained programming, Fuzzy random variable, Possibility theory, Vertex p-center problem

Abstract: Vertex and p-center problems are two well-known types of the center problem. In this paper, a p-center problem with uncertain demand-weighted distance will be introduced in which the demands are considered as fuzzy random variables (FRVs) and the objective of the problem is to minimize the maximum distance between a node and its nearest facility. Then, by introducing new methods, the proposed problem is converted to deterministic integer programming (IP) problems where these methods will be obtained through the implementation of the possibility theory and fuzzy random chance-constrained programming (FRCCP). Finally, the proposed methods are applied for locating bicycle stations in the city of Tabriz in Iran as a real case study. The computational results of our study show that these methods can be implemented for the center problem with uncertain frameworks.

How to cite this paper

 Nematian, J & Sadati, M. (2015). New methods for solving a vertex p-center problem with uncertain demand-weighted distance: A real case study.International Journal of Industrial Engineering Computations , 6(2), 253-266.

Refrences

Albareda-Sambola, M., D?az, J. A., & Fern?ndez, E. (2010). Lagrangean duals and exact solution to the capacitated p-center problem. European Journal of Operational Research, 201(1), 71-81.

Daskin, M. S. (2011). Network and discrete location: models, algorithms, and applications. John Wiley & Sons.

D?yen, A., Aras, N., & Barbaroso?lu, G. (2012). A two-echelon stochastic facility location model for humanitarian relief logistics. Optimization Letters, 6(6), 1123-1145.

Dubois, D., & Prade, H. (2001). Possibility theory, probability theory and multiple-valued logics: A clarification. Annals of mathematics and Artificial Intelligence, 32(1-4), 35-66.

Farahani, R. Z., SteadieSeifi, M., & Asgari, N. (2010). Multiple criteria facility location problems: A survey. Applied Mathematical Modelling, 34(7), 1689-1709.

Hakimi, S. L. (1965). Optimum distribution of switching centers in a communication network and some related graph theoretic problems. Operations Research, 13(3), 462-475.

Ishii, H., Lee, Y. L., & Yeh, K. Y. (2007). Fuzzy facility location problem with preference of candidate sites. Fuzzy Sets and Systems, 158(17), 1922-1930.

Kariv, O., & Hakimi, S. L. (1979). An algorithmic approach to network location problems. I: The p-centers. SIAM Journal on Applied Mathematics, 37(3), 513-538.

Kim, Y., Lee, Y., & Han, J. (2011). A splitter location–allocation problem in designing fiber optic access networks. European Journal of Operational Research, 210(2), 425-435.

Küçükdeniz, T., Baray, A., Ecerkale, K., & Esnaf, ?. (2012). Integrated use of fuzzy c-means and convex programming for capacitated multi-facility location problem. Expert Systems with Applications, 39(4), 4306-4314.

Kwakernaak, H. (1978). Fuzzy random variables—I. Definitions and theorems. Information Sciences, 15(1), 1-29.

Lu, C. C., & Sheu, J. B. (2013). Robust vertex p-center model for locating urgent relief distribution centers. Computers & Operations Research, 40(8), 2128-2137.

Murat, A., Verter, V., & Laporte, G. (2011). A multi-dimensional shooting algorithm for the two-facility location–allocation problem with dense demand. Computers & Operations Research, 38(2), 450-463.

Puri, M. L., & Ralescu, D. A. (1986). Fuzzy random variables. Journal of Mathematical Analysis and Applications, 114(2), 409-422.

Revelle, C. S., Eiselt, H. A., & Daskin, M. S. (2008). A bibliography for some fundamental problem categories in discrete location science. European Journal of Operational Research, 184(3), 817-848.

Schütz, P., Stougie, L., & Tomasgard, A. (2008). Stochastic facility location with general long-run costs and convex short-run costs. Computers & Operations Research, 35(9), 2988-3000.

Wang, S., Watada, J., & Pedrycz, W. (2009). Value-at-Risk-based two-stage fuzzy facility location problems. IEEE Transactions on Industrial Informatics,5(4), 465-482.

Wang, S., & Watada, J. (2012). A hybrid modified PSO approach to VaR-based facility location problems with variable capacity in fuzzy random uncertainty. Information Sciences, 192, 3-18.

Wang, S., & Watada, J. (2013). Capacitated two-stage facility location problem with fuzzy costs and demands. International Journal of Machine Learning and Cybernetics, 4(1), 65-74.

Wen, M., & Kang, R. (2011). Some optimal models for facility location–allocation problem with random fuzzy demands. Applied Soft Computing, 11(1), 1202-1207.

Xu, D., Gao, D., & Wu, C. (2013). A primal-dual-approximation algorithm for the stochastic facility location problem with submodular penalties. Optimization, (ahead-of-print), 1-10.