Abstract
Inventory problem and queuing problems are two of the most widely studied domains in operation research. But when we are applying these ideas in real systems, we are facing some issues related to their form as such in the system which usually is a combination of more than one model under the operations research category of models. So, a combinational study of models becomes an essential part of implementation and Inventory queuing system is one such combinational model where we combine ideas of a queuing model and an inventory model to suit the necessities of real life implementation. This is a more realistic one for study and in this paper we are using a well-established line of a Birth–Death process to study which both is interesting and a fruitful line of study. To add another dimension to the study we have added simulation, a relatively easy and most sought-after avenue of studying practical implementation of models which is usually used when we have no compact analytical solutions is available by theoretical modeling. We first study an inventory queuing system as a two-dimensional Birth–Death process and derive stationary probabilities analytically. As the solutions obtained have not turned out to be as compact as we usually have in an inventory set up, we moved into the simulation domain and employed two lines of simulation, one using the usual newsboy type and the other inspired by the BD type study and we have obtained a reasonable set measures to make some nice conclusions.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Taha, Hamdy. 2010. Operations Research, 5th edn. Pearson.
Banks, J., J.S. Carson, B.L. Nelson, et al. 2010. Discrete Event System Simulation, 5th edn. Pearson.
Cooper, R.B. 1981. Introduction to Queuing Theory. North Holland.
Prabhu, N.U. 1997. Qualitative research practice. A Guide for Social Science Students and Researchers. London: Sage Publications.
Sztrik, János. 2010. Queuing theory and its applications, a personal view. In International Conference on Applied Informatics, Proceedings of the 8th Conference, vol. 1, 9–30. Eger: Hungary.
Bolanle, Amole Bilqis. 2011. Application of queuing theory to port congestion problem in Nigeria. European Journal of Business and Management 3 (8): 2011.
Mwangi, Sammy Kariuki, and Thomas Mageto Ombuni. 2015. An empirical analysis of queuing model and queuing behaviour in relation to customer satisfaction at Jkuat students finance office. American Journal of Theoretical and Applied Statistics 4 (4): 233–246.
Ehsanifar, Mohammad, Nima Hamta, and Mahshid Hemesy. 2017. A simulation approach to evaluate performance indices of fuzzy exponential queuing system (An M/M/C model in a banking case study). Journal of Industrial Engineering and management Studies 4 (2): 35–51.
Vijayarangam, J. 2015. Simulation based analysis of a newsboy problem. Journal Technology Advances and Scientific Research 13: 8–10.
Nair, Anoop N., and M.J. Jacob. 2015. An (s, S) production inventory controlled self-service queuing system. Journal of Probability and Statistics 2015 (2): 1–8.
Arivarignan, G., C. Elango, and N. Arumugam. 2002. A continuous review perishable inventory control system at service facilities. Advances in Stochastic Modelling, Notable Publications, NJ, USA, 29–40.
Berman, O., and E. Kim. 1999. Stochastic models for inventory management at service facilities. Communications in Statistics-Stochastic Models 15 (4): 695–718.
Berman, O., E.H. Kaplan, and D. Shimshak. 1993. Deterministic approximations for inventory management at service facilities. IIE Transactions 25 (5): 98–104.
Deepak, T.G., A. Krishnamoorthy, V.C. Narayan, and K. Vineetha. 2008. Control policies for inventory with service time. Annals of Operations Research 160: 191–213.
Kalpakam, S., and S. Shanthi. 2001. Perishable inventory system with modified (S-1, S) policy and arbitrary processing times. Computers and Operations Research. 28 (5): 453–471.
Kalpakam, S., and S. Shanthi. 2000. A perishable system with modified base stock policy and random supply quantity. Computers and Mathematics with Applications 39 (12): 79–89.
Levi, R., G. Perakis, and J. Uichanco. 2015. The data-driven newsvendor problem. New Bounds and Insights: Journal Of Operations Research 63 (6): 1294–1306.
Manuel, P., B. Sivakumar, and G. Arivarignan. 2008. A perishable inventory system with service facilities and retrial customers. Computers and Industrial Engineering 54 (3): 484–502.
Karthick, T., B. Sivakumar, and G. Arivarignan. 2015. An inventory system with two types of customers and retrial demands. International Journal of Systems Science Operations and Logistics 2 (2): 90–112.
Vijayarangam, J., B. Navin Kumar, A. Vasudevan, and Pandiyarajan. 2020. Simplified analysis of a multiproduct newsboy problem using simulation. IOP Conference Series, Materials Science and Engineering. https://doi.org/10.1088/1757-899X/988/1/012089.
Krishnamoorthy, A., B. Lakshmi, and R. Manikandan. 2011. A revisit to queuing-inventory system with positive service time. Opsearch 48 (2): 153–169.
Mode, C.J. 1962. Some multi-dimensional birth and death processes and their applications in population genetics. Biometrics 18 (4): 543–567.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Vijayarangam, J., Perumal, S., Viswanath, J. (2022). Inventory Queuing System Study Using Simulation and Birth–Death Process. In: Peng, SL., Lin, CK., Pal, S. (eds) Proceedings of 2nd International Conference on Mathematical Modeling and Computational Science. Advances in Intelligent Systems and Computing, vol 1422. Springer, Singapore. https://doi.org/10.1007/978-981-19-0182-9_3
Download citation
DOI: https://doi.org/10.1007/978-981-19-0182-9_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-0181-2
Online ISBN: 978-981-19-0182-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)