A Multi-Criteria Decision-Making Approach for Warehouse Location Selection using TOPSIS
Viewed = 0 time(s)
This research makes use of the Method for Order of Preference by Similarity to Ideal Solution, also known as the TOPSIS approach, in order to discover the most suitable site for a company's warehouse. Following the establishment of the criteria for the selection of the warehouse location, weights were allotted to each of the criteria. The min-max method was utilized to do data normalization once it had been collected for each prospective location. After constructing the decision matrix with the weighted normalized values and determining the ideal and non-ideal solutions for each criterion, the results were then presented. Following the calculation of the Euclidean distance between each potential location and the ideal and non-ideal solutions, the TOPSIS formula was used to determine the relative proximity between each of the potential locations. The site of the potential location that was the highest relative closeness to the optimum solution was chosen to be the optimal location for the warehouse. By employing this strategy, the company will be able to make an educated decision regarding the location of their warehouse, which will, in the long run, result in improved operational efficiency and cost savings.
Indahingwati, A., Barid, M., Wajdi, N., Susilo, D. E., Kurniasih, N., & Rahim, R. (2018). Comparison Analysis of TOPSIS and Fuzzy Logic Methods On Fertilizer Selection. International Journal of Engineering and Technology(UAE), 7(2.3), 109–114.
Kabir, G., & Hasin, M. A. A. (2012). Comparative analysis of TOPSIS and Fuzzy TOPSIS for the evaluation of travel website service quality. International Journal for Quality Research, 6(3), 169–185.
Kaliszewski, I., & Podkopaev, D. (2016). Simple additive weighting - A metamodel for multiple criteria decision analysis methods. Expert Systems with Applications, 54, 155–161. https://doi.org/10.1016/j.eswa.2016.01.042
Karande, P., Zavadskas, E. K., & Chakraborty, S. (2016). A study on the ranking performance of some MCDM methods for industrial robot selection problems. International Journal of Industrial Engineering Computations, 7(3), 399–422. https://doi.org/10.5267/j.ijiec.2016.1.001
Lestari, V. N. S., Lestari, V. N. S., Djanggih, H., Aswari, A., Hipan, N., & Siahaan, A. P. U. (2018). Technique for Order Preference by Similarity to Ideal Solution as Decision Support Method for Determining Employee Performance of Sales Section. International Journal of Engineering & Technology, 7(2.14), 281–285. https://doi.org/10.14419/ijet.v7i2.12.14693
Maulana, A. A., & Hidayat, N. S. (2018). Sistem Pendukung Keputusan Penentuan Penerima Bantuan Keluarga Miskin Menggunakan Metode Analytical Hierarchy Process – Technique For Order Of Preference By Similarity To Ideal Solution ( AHP - TOPSIS ). Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, 2(10), 3890–3898.
Pamučar, D., & Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert Systems with Applications, 42(6), 3016–3028. https://doi.org/10.1016/j.eswa.2014.11.057
Primasari, C. H., Wardoyo, R., & Sari, A. K. (2018). Integrated AHP, Profile Matching, and TOPSIS for selecting type of goats based on environmental and financial criteria. International Journal of Advances in Intelligent Informatics, 4(1), 28–39. https://doi.org/10.26555/ijain.v4i1.105
Siregar, V. M. M., Sonang, S., Purba, A. T., Sugara, H., & Siagian, N. F. (2021). Implementation of TOPSIS Algorithm for Selection of Prominent Student Class. Journal of Physics: Conference Series, 1783(1), 012038. https://doi.org/10.1088/1742-6596/1783/1/012038
Syamsudin, S., & Rahim, R. (2017). Study Approach Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). International Journal of Recent Trends in Engineering and Research, 3(3), 268–285. https://doi.org/10.23883/IJRTER.2017.3077.GZXDL
Xu, H., Ma, C., Lian, J., Xu, K., & Chaima, E. (2018). Urban flooding risk assessment based on an integrated k-means cluster algorithm and improved entropy weight method in the region of Haikou, China. Journal of Hydrology, 563, 975–986. https://doi.org/10.1016/j.jhydrol.2018.06.060
Yaakob, A. M., & Gegov, A. (2016). Interactive TOPSIS Based Group Decision Making Methodology Using Z-Numbers. International Journal of Computational Intelligence Systems, 9(2), 311–324. https://doi.org/10.1080/18756891.2016.1150003
Zanakis, S. H., Solomon, A., Wishart, N., & Dublish, S. (1998). Multi-attribute decision making: A simulation comparison of select methods. European Journal of Operational Research, 107(3), 507–529. https://doi.org/10.1016/S0377-2217(97)00147-1
Copyright (c) 2023 Ira Modifa Tarigan, Muhammad Ade Kurnia Harahap, Endang setyawati, Jimmy Moedjahedy, Ernie C Avila, Robbi Rahim
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.