Introduction
The queuing theory is central to solving current logistics and optimization issues. It allows one to predict when their delivery will arrive and to maintain the interest of customers even if they have to wait in line. Furthermore, the theory is central to the truck industry, which has been experiencing dramatic growth in the United States. A business owner should be aware of the queuing approach to succeed.
Main body
Congestion is an issue that often has to be addressed in situations that involve queues. Klodawski, Jachimowski, Jacyna-Golda, and Izdebski (2018) present an order-picking simulation in a congested case that aims to maximize efficiency. They conclude that the process becomes more efficient when the number of pickers increases. The queuing theory is a valuable asset in the research, helping to achieve and explain the results.
The theory has more practical applications such as logistics, where congestion is a constant concern. Shukla, Chhadva, Arora, Sheth, and Malhotra (2017) apply queuing theory to logistics and warehouse optimization. As a result, they were able to determine the average waiting time at each service line and whether the number of lines was sufficient. With this information, it is possible to calculate the optimal warehouse size and number of service lines, leading to increased productivity.
Lastly, queuing theory can also be applied to the service industry, with the discussion author’s fast food description being an example. Aradhye and Kallurkar (2014) describe a “Just-In-Time” system that focuses on eliminating added activities that do not produce value. The application of such a system to a pilgrimage location allowed them to reduce the average 8 hours waiting time to a consistent 30 minutes that allowed the pilgrims to make plans with certainty and increased their satisfaction.
Conclusion
The queuing theory is a vital mathematical tool with a large variety of real-world applications. It can be used in logistics as well as in any business that interacts with a large number of customers who cannot all be served simultaneously. At the same time, the theory is firmly grounded in mathematics, giving it credibility and making it valuable for modeling. Knowledge and understanding of the method are conducive to the success of a business.
References
Aradhye, A. S., & Kallurkar, S. P. (2014). A Case Study of Just-In-Time System in Service Industry. Procedia Engineering, 97, 2232-2237.
Klodawski, M., Jachimowski, R., Jacyna-Golda, I., & Izdebski, M. (2018). Simulation analysis of order picking efficiency with congestion situations. International Journal of Simulation Modelling, 17(3), 431-443.
Shukla, H., Chhadva, J., Arora, J., Sheth, K., & Malhotra, K. (2017). Application of operation research in logistics and warehouse optimization. International Journal of Innovative Research in Technology & Science, 5(6). Web.