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Two Phase Retrial Queue Evaluation Using DEMATEL (Decision Making Trial and Evaluation Laboratory)

Sathiyaraj Chinnasamy, M. Ramachandran, Prabakaran Nanjundan, Vidhya Prasanth


In this study, retrial queues are characterized by calls that detect that all servers are busy joining the review team, and retrying their demands in random order and at random intervals. If the server is found to be busy, customers can join the review team at a random time. But they keep making the same demands, at random or at set intervals. Recurring queues are widely used to model many realworld situations in telephone switching systems, internet access, call centers, telecommunications networks, and computer systems. Clients not accessing the server are forced to leave the service area. Diagrams can also be used to visualise the structure of complex causal relationships. In order to identify the causal connections between strategic criteria, this DEMATEL method gathers collective knowledge. This model is particularly realistic and scalable. It gives a vague DEMATEL version below the panel selection to solve the power area selection problem, in which reviews of various options under precise subjective houses and all house super weights are expected in linguistic values denoted by ambiguous numbers. Alternatives: stochastic decomposition, steady-state distribution, server vacations, server setup times, and waiting time distribution. Evaluation preferences: stochastic decomposition, steady-state distribution, server vacations, server setup times, and waiting time distribution. In this analysis using DEMATEL method, as a result, random decay is ranked first, while server vacations are ranked last. It determines the solution with the longest distance from the optimal solution and the negative-optimal solution, but the comparison of these distances is not considered significant.


DEMATEL method, retrial queue, stochastic decomposition

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Falin Gennadi I, Artalejo Jesus R. A finite source retrial queue. Eur J Oper Res. 1998; 108(2): 409–424.

Gómez-Corral Antonio. A bibliographical guide to the analysis of retrial queues through matrix analytic techniques. Ann Oper Res. 2006; 141(1): 163–191.

Sherman Nathan P, Kharoufeh Jeffrey P. An M/M/1 retrial queue with unreliable server. Oper Res Lett. 2006; 34(6): 697–705.

Artalejo JR, Falin JI. Stochastic decomposition for retrial queues. Top. 1994; 2(2): 329–342.

Choi BD, Kim YC, Lee YW. The M/M/c retrial queue with geometric loss and feedback. Comput Math Appl. 1998; 36(6): 41–52.

Falin GI, Artalejo Jesus R, Martin M. On the single server retrial queue with priority customers. Queueing Syst. 1993; 14(3): 439–455.

Zhang Feng, Jinting Wang. Performance analysis of the retrial queues with finite number of sources and service interruptions. J Korean Stat Soc. 2013; 42(1): 117–131.

Phung-Duc Tuan, Hiroyuki Masuyama, Shoji Kasahara, Yutaka Takahashi. A matrix continued fraction approach to multiserver retrial queues. Ann Oper Res. 2013; 202(1): 161–183.

Wang Jinting, Peng Zhang. A discrete-time retrial queue with negative customers and unreliable server. Comput Ind Eng. 2009; 56(4): 1216–1222.

Choudhury Gautam, Jau-Chuan Ke. An unreliable retrial queue with delaying repair and general retrial times under Bernoulli vacation schedule. Appl Math Comput. 2014; 230: 436–450.

Economou Antonis, Spyridoula Kanta. Equilibrium customer strategies and social-profit maximization in the single‐server constant retrial queue. Nav Res Logist (NRL). 2011; 58(2): 107–122.

Dragieva Velika I. Number of retrials in a finite source retrial queue with unreliable server. Asia-Pac J Oper Res. 2014; 31(02): 1440005.

Fallin D, Schork NJ. Accuracy of haplotype frequency estimation for biallelic loci, via the expectation-maximization algorithm for unphased diploid genotype data. The American Journal of Human Genetics. 2000 Oct 1;67(4):947-59.

Choi Bong Dae, Yang Woo Shin, Wi Chong Ahn. Retrial queues with collision arising from unslotted CSMA/CD protocol. Queueing Syst. 1992; 11(4): 335–356.

Choudhury Gautam, Sangeeta Kalita. A two-phase queueing system with repeated attempts and Bernoulli vacation schedule. Int J Oper Res. 2009; 5(4): 392–407.

Gao Shan. A preemptive priority retrial queue with two classes of customers and general retrial times. Oper Res. 2015; 15(2): 233–251.

Higle Julia L, Suvrajeet Sen. Finite master programs in regularized stochastic decomposition. Math Program. 1994; 67(1): 143–168.

Qian Xiaoning, Dougherty Edward R. Effect of function perturbation on the steady-state distribution of genetic regulatory networks: Optimal structural intervention. IEEE Trans Signal Process. 2008; 56(10): 4966–4976.

Allahverdi A, Ng CT, Cheng TE, Kovalyov MY. A survey of scheduling problems with setup times or costs. European journal of operational research. Jun 2008; 187(3): 985–1032.

Hallas Jesper, David Gaist, Lars Bjerrum. The waiting time distribution as a graphical approach to epidemiologic measures of drug utilization. Epidemiology. 1997; 8(6): 666–670.

Si Sheng-Li, Xiao-Yue You, Hu-Chen Liu, Ping Zhang. DEMATEL technique: A systematic review of the state-of-the-art literature on methodologies and applications. Math Probl Eng. 2018; 2018: 3696457.

O'leary Daniel E. Validation of expert systems‐with applications to auditing and accounting expert systems. Decis Sci. 1987; 18(3): 468–486.

Hritonenko Natali, Yuri Yatsenko. Applied mathematical modelling of engineering problems. Vol. 81. Springer Science & Business Media; 2003.

Zhang Weiquan, Yong Deng. Combining conflicting evidence using the DEMATEL method. Soft Comput. 2019; 23(17): 8207–8216.

Markatos NC. The mathematical modelling of turbulent flows. Appl Math Model. 1986; 10(3): 190–220.

Liebowitz Jay. The handbook of applied expert systems. CRC Press; 2019.

Tzeng Gwo-Hshiung, Cheng-Hsin Chiang, Chung-Wei Li. Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert Syst Appl. 2007; 32(4): 1028–1044.

Wu Wei-Wen. Choosing knowledge management strategies by using a combined ANP and DEMATEL approach. Expert Syst Appl. 2008; 35(3): 828–835.

Zhou Quan, Weilai Huang, Ying Zhang. Identifying critical success factors in emergency management using a fuzzy DEMATEL method. Saf Sci. 2011; 49(2): 243–252.

Lin Ru-Jen. Using fuzzy DEMATEL to evaluate the green supply chain management practices. J Clean Prod. 2013; 40: 32–39.

Du Yuan-Wei, Xiao-Xue Li. Hierarchical DEMATEL method for complex systems. Expert Syst Appl. 2021; 167: 113871.

Gölcük İlker, Adil Baykasoğlu. An analysis of DEMATEL approaches for criteria interaction handling within ANP. Expert Syst Appl. 2016; 46: 346–366.


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