China
Africa
Explore chapters and articles related to this topic
Design For Flow (DFF): The Essential Foundation for Job Shop Lean
Published in Shahrukh A. Irani, Job Shop Lean, 2020
Convert Flow Time to WIP: Despite its simplicity, Little’s Law makes it easy to justify converting an existing Functional Layout into a Cellular Layout because that will reduce the Flow Time for any order. The theoretical form of Little’s Law is as follows: Lq = λWq, where Lq is the average number of customers in a line who are awaiting service, λ is the arrival rate of customers that are seeking service, and Wq is the average wait time for a customer in the line before he/she gets served. Little’s Law can be adapted to convert a reduction in Flow Time due to layout improvements into a reduction in WIP. If Lq = WIP, λ = Throughput, and Wq = Flow Time (the average time that a unit of product sends in the facility), then WIP = Throughput * Flow Time. For example, assume that 25 units/day are being produced (= Throughput) in the current facility layout and that it takes 5 days (Flow Time) to complete one unit. In this case, the WIP = 125 units. Say that the Flow Time for a unit is reduced to 1 day by moving all the machines needed to produce it into a work cell. Now, the WIP = 25 units. Clearly, by speeding up the product’s flow in the facility, the cost of carrying the order as WIP inventory gets reduced.
DDoS Malware: The Curse of Virus Rain™
Published in Rocky Dr. Termanini, The Nano Age of Digital Immunity Infrastructure Fundamentals and Applications, 2018
Little’s law provides a fundamental relationship between three key parameters in a queuing (or waiting line/service) system: the average number of items in the system, the average waiting time (or flow time) in the system for an item, and the average arrival rate of items to the system. The system can be very general. For example, it might include both the service facility and the waiting line, or it might be only the waiting line. An important feature of Little’s law is that by knowing, perhaps via direct measurement, two of the three parameters, the third can be calculated. This is an extremely useful property since measurement of all three parameters may be difficult in certain applications. Little’s law is applicable to many environments including manufacturing and service industries. We’re going to apply Little’s law to DDoS attacks on target systems. This is an unusual application but it works very well.
1
Published in Sunderesh S. Heragu, Facilities Design, 2022
One of the most fundamental results in queuing theory that allows us to determine the performance measures of a system is Little’s law. It defines the relationship between the operational characteristics of a queuing system. It is one of the simplest, yet most powerful formulae in queuing theory. It basically states that under steady-state conditions, the average number of customers in any queuing system is equal to the product of the mean arrival rate and the average time spent in the system by a customer. This result holds for any interarrival or service time distribution, any service discipline, and for any number of servers. The only requirement is that the queuing system must be in steady state.
Equilibrium and socially optimal strategies of a double-sided queueing system with two-mass point matching time
Published in Quality Technology & Quantitative Management, 2023
Zhen Wang, Cheryl Yang, Yiqiang Q. Zhao
Using (11)-(14) and Little’s law (Little’s law: in the steady state of a queuing system, the average value of the number of individuals in the system is equal to the average individual arrival rate (unit time) multiplied by the individual’s average length of stay , i.e. .), the expected waiting times for passenger and taxi queues can be obtained as follows, respectively,