Abstract
The Cash in Transit (CIT) deals with the money distribution and picking up between depot(s), central bank, bank branches, Automated Teller Machines (ATMs), jewelry stores, and exchange offices, safely and quickly. It is critical for companies carrying out CIT activities to identify risks and their priorities. It is one of the problems to be resolved for CIT companies to determine the risks that may occur on the route for each vehicle that will carry money from a certain center to the demand points. It is important for decision makers how critical these routes are concerning risks. Within the scope of the study, determining the risks, the weights of these risks, and high-risk routes accordingly are discussed. The literature review related to the problem is first consulted. After the literature review, interviews are made with experts on their subject. The main criteria and sub-criteria to define the risks are determined. Then, these criteria are weighted by the Interval-Valued Intuitionistic Fuzzy Analytical Hierarchy Process (IVIF-AHP), and risk assessment is made to alternative routes based on these weighted risks. The routes are evaluated by Interval-Valued Intuitionistic Fuzzy Technique for Order Preference by Similarity to Ideal Solution (IVIF-TOPSIS) methodology, and thus, it is determined which route has the highest risk and which route has the lowest risk. In this way, it is stated on which route should be used for the vehicles to distribute money and which routes should be improved.
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References
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Aslihan Yildiz and Ertugrul Ayyildiz developed the theoretical formulation, performed the analytic calculations and numerical analysis. Ertugrul Ayyildiz and Alev Taskin contributed to the sensitivity analysis and final version of the manuscript. Aslihan Yildiz and Coskun Ozkan drafted the manuscript. Ali Fuat Guneri supervised the work.
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Yildiz, A., Guneri, A.F., Ozkan, C. et al. An integrated interval-valued intuitionistic fuzzy AHP-TOPSIS methodology to determine the safest route for cash in transit operations: a real case in Istanbul. Neural Comput & Applic 34, 15673–15688 (2022). https://doi.org/10.1007/s00521-022-07236-y
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DOI: https://doi.org/10.1007/s00521-022-07236-y