OPTIMIZATION OF DISASTER RELIEF LOGISTICS DISTRIBUTION USING THE FUZZY TRANSPORTATION PROBLEM MODEL
Abstract
The distribution of disaster relief logistics faces significant challenges due to uncertainty in demand, supply constraints, and accessibility constraints in affected areas. The novelty of this study lies in integrating trapezoidal fuzzy numbers to represent uncertainty in disaster logistics, thereby offering a more realistic model than conventional deterministic models. This study proposes developing a fuzzy-logic-based transportation model to optimize logistics resource allocation. The model was applied to a disaster relief distribution scenario with five source locations and five destination points. The model is solved using the Vogel Approximation Method and optimality test using the Simplex Transportation Method. Next, to determine the distribution route that minimizes costs and distance, a simulation was conducted in MATLAB. The results show that the fuzzy transportation problem model produces more efficient distribution solutions than conventional transportation models, which can be used only for certain data. However, this study is limited to single-objective cost minimization using simulated data. Therefore, future research should consider applying multi-objective optimization to minimize both distribution cost and time simultaneously using real-time geospatial data.
Downloads
References
L. Sahoo, “TRANSPORTATION PROBLEM IN FERMATEAN FUZZY ENVIRONMENT,” RAIRO - Operations Research, vol. 57, no. 1, pp. 145–156, Jan. 2023, doi:
https://doi.org/10.1051/ro/2022210
W. Putri, I. Hasbiyati, and M. D. H. Gamal, “OPTIMIZATION OF PARKING LOT IN THE FORMS OF PARALLELOGRAM AND RIGHT TRIANGLE FOR CARS AND MOTORBIKES,” Math. Model. Appl, vol. 4, no. 4, pp. 64–71, 2019, doi: https://doi.org/10.11648/j.mma.20190404.12
I. Hasbiyati, W. Putri, A. Adnan, A. Ahriyati, and A. Hasriati, “PARKING LOT OPTIMIZATION IN PARALLELOGRAM USING THE CONCEPT AREA OF RECTANGULAR AND RIGHT TRIANGLE,” Pure and Applied Mathematics Journal, vol. 8, no. 4, p. 77, 2019, doi: https://doi.org/10.11648/j.pamj.20190804.12
I. Hasbiyati, M. Abdullah, and R. Salambue, “NORTHWEST CORNER METHOD FOR NATURAL DISASTER NOTIFICATION,” BAREKENG: J. Il. Mat. & Ter, vol. 16, no. 1, pp. 271–280, 2022, doi: https://doi.org/10.30598/barekengvol16iss1pp269-278
I. Hasbiyati, “ANALYSIS OF MULTI-STAGE STOCHASTIC OPTIMIZATION MODEL FOR STOCHASTIC TRANSPORTATION PROBLEMS,” 2019.
D. Mardanya and S. K. Roy, “NEW APPROACH TO SOLVE FUZZY MULTI-OBJECTIVE MULTI-ITEM SOLID TRANSPORTATION PROBLEM,” RAIRO - Operations Research, vol. 57, no. 1, pp. 99–120, Jan. 2023, doi: https://doi.org/10.1051/ro/2022211
L. A. Zadeh, “FUZZY S E T S *,” 1965, doi: https://doi.org/10.1016/S0019-9958(65)90241-X
R. D. Tordecilla, L. D. C. Martins, J. Panadero, P. J. Copado, E. Perez-Bernabeu, and A. A. Juan, “FUZZY SIMHEURISTICS FOR OPTIMIZING TRANSPORTATION SYSTEMS: DEALING WITH STOCHASTIC AND FUZZY UNCERTAINTY,” Applied Sciences (Switzerland), vol. 11, no. 17, Sep. 2021, doi: https://doi.org/10.3390/app11177950
D. Gurukumaresan, C. Duraisamy, and R. Srinivasan, “OPTIMAL SOLUTION OF FUZZY TRANSPORTATION PROBLEM USING OCTAGONAL FUZZY NUMBERS,” Computer Systems Science and Engineering, vol. 37, no. 3, pp. 415–421, 2021, doi: https://doi.org/10.32604/csse.2021.014130
M. Pooja, C. Thangapandi, and R. Srinivasan, “A NEW ALGORITHMIC APPROACH FOR SOLVING TRANSPORTATION PROBLEM IN FUZZY ENVIRONMENT WITH TRAPEZOIDAL FUZZY NUMBERS,” Advances in Mathematics: Scientific Journal, vol. 10, no. 6, pp. 3071–3084, 2021.
S. Dhouib, “SOLVING THE TRAPEZOIDAL FUZZY TRANSPORTATION PROBLEMS VIA NEW HEURISTIC: THE DHOUIB-MATRIX-TP1,” International Journal of Operations Research and Information Systems, vol. 12, no. 4, 2021, doi: https://doi.org/10.4018/IJORIS.294119
J. Pratihar, R. Kumar, S. A. Edalatpanah, and A. Dey, “MODIFIED VOGEL’S APPROXIMATION METHOD FOR TRANSPORTATION PROBLEM UNDER UNCERTAIN ENVIRONMENT,” Complex and Intelligent Systems, vol. 7, no. 1, pp. 29–40, Feb. 2021, doi: https://doi.org/10.1007/s40747-020-00153-4
Z. S. Mahdi, H. A. Wasi, and M. A. K. Shiker, “SOLVING TRANSPORTATION PROBLEMS BY USING MODIFICATION TO VOGEL’S APPROXIMATION METHOD,” in AIP Conference Proceedings, American Institute of Physics Inc., Dec. 2023, doi: https://doi.org/10.1063/5.0161614
Y. Lu and S. Li, “GREEN TRANSPORTATION MODEL IN LOGISTICS CONSIDERING THE CARBON EMISSIONS COSTS BASED ON IMPROVED GREY WOLF ALGORITHM,” Sustainability (Switzerland), vol. 15, no. 14, Jul. 2023, doi: https://doi.org/10.3390/su151411090
L. Kané, M. Diakité, S. Kané, H. Bado, M. Konaté, and K. Traoré, “THE NEW ALGORITHM FOR FULLY FUZZY TRANSPORTATION PROBLEM BY TRAPEZOIDAL FUZZY NUMBER (A GENERALIZATION OF TRIANGULAR FUZZY NUMBER),” Journal of Fuzzy Extension and Applications, vol. 2, no. 3, pp. 204–225, Sep. 2021.
A. H. Taha, Operations Research an Introduction. Edinburgh, 2017.
S. F. Hiller and J. G. Lieberman, Introduction-to-Operations-Research-Hillier-Lieberman. New York: McGraw-Hill, 2000.
S. H. Nasseri, E. Behmanesh, F. Taleshian, M. Abdolalipoor, and N. A. Taghi-Nezhad, “FULLY FUZZY LINEAR PROGRAMMING WITH INEQUALITY CONSTRAINTS,” 2013. [Online]. Available: http://ijim.srbiau.ac.ir/
Z. Xiao, S. Xia, K. Gong, and D. Li, “THE TRAPEZOIDAL FUZZY SOFT SET AND ITS APPLICATION IN MCDM,” Appl Math Model, vol. 36, no. 12, pp. 5844–5855, Dec. 2012, doi: https://doi.org/10.1016/j.apm.2012.01.036
I. Hasbiyati, S. Wahyuni, E. Agustiarini, M. S. Amini, and A. Ahriyati, “SIMPLEX TRANSPORTATION METHOD FOR DETERMINING TRANSSHIPMENT OF CLOTHING RAW MATERIALS,” Jurnal Matematika UNAND, vol. 14, no. 1, pp. 62–84, Jan. 2025, doi: https://doi.org/10.25077/jmua.14.1.62-84.2025
Wayne L. Winston, Operations Research, Application and Algorithms, Fourth Edition. Toronto: Thomson Learning Academic Resource Center, 2004. [Online]. Available: www.duxbury.com
A. Sheikhi and M. J. Ebadi, “ON SOLVING LINEAR FRACTIONAL PROGRAMMING TRANSPORTATION PROBLEMS WITH FUZZY NUMBERS,” Journal of Fuzzy Extension and Applications, vol. 4, no. 4, pp. 327–339, 2023.
P. Vinothini and K. Kavitha, “A STAGE STRUCTURE PREY PREDATOR MODEL USING PENTAGONAL FUZZY NUMBERS AND FUNCTIONAL RESPONSE,” Baghdad Science Journal, vol. 21, no. 10, pp. 3234–3247, 2024, doi: https://doi.org/10.21123/bsj.2024.9945
Copyright (c) 2026 Ihda Hasbiyati, Hasriati Hasriati, Harison Harison, Maimunah Maimunah, Aziza Masli, Ahriyati Ahriyati
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this Journal agree to the following terms:
- Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
- Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.
1.gif)
