Comments on: queueing theory: part 1

By: Israel Robert A.

Israel Robert A. — Thu, 17 Dec 2015 22:46:00 +0000

“….The reason that ERs, restaraunts, and Post Offices have long wait times is that utilization (or demand) has gone over 100% throughput capacity. The service can no longer serve in the rate as people arrive. Since the service provider has not planned for or reacted to the higher demand, the length of the line grows”…….

it’s not always the case that when demand or utilization goes over 100%, long waiting lines develop in busy systems.
this could also happen when there are system inefficiencies so that average service time< average arrival time.

By: LT

LT — Fri, 22 Jul 2011 05:11:30 +0000

In reply to Richard Karpinski. 10-hour shift, but 8-hour working. Hey, this is normal.

By: Baruch Atta

Baruch Atta — Wed, 28 Jan 2009 14:58:03 +0000

should read…
…can no longer serve at the rate that people arrive…

By: Baruch Atta

Baruch Atta — Wed, 28 Jan 2009 14:54:27 +0000

“…as capacity utilization reaches 80% or so, throughput levels out and Cycle Time explodes. That’s why really busy systems (factories, emergency rooms, etc.) have very long waiting times…”

No, that’s not the reason. The reason that ERs, restaraunts, and Post Offices have long wait times is that utilization (or demand) has gone over 100% throughput capacity. The service can no longer serve in the rate as people arrive. Since the service provider has not planned for or reacted to the higher demand, the length of the line grows.

By: Bob Dierks

Bob Dierks — Tue, 13 Jan 2009 05:48:40 +0000

Refreshing!

By: Richard Karpinski

Richard Karpinski — Fri, 23 Nov 2007 16:48:56 +0000

“assuming a 10 hour shift per day of about 250 working days per year, there is roughly 2,000 working hours.”

This works only if the 10 is in octal notation.

By: Mark Graban

Mark Graban — Thu, 15 Jun 2006 14:51:49 +0000

Little’s Law is an incredbly helpful tool for operations. I highly recommend the Hopp and Spearman book “Factory Physics”, which spends a lot of time on Little’s Law.

Another way of thinking about this is:

Throughput = WIP / CT

WIP = Work in Process

You have to be careful with Little’s Law and resist the temptation to think “all I have to do to increase Throughput is to increase WIP”. It doesn’t work that way, not that Peter was implying that. I’m just saying that’s a common mis-application of Little’s Law.

When you start with zero WIP, you will have zero throughput. Makes sense, right? At first, as WIP increases, your throughput will increase somewhat linearly. But, as capacity utilization reaches 80% or so, throughput levels out and Cycle Time explodes. That’s why really busy systems (factories, emergency rooms, etc.) have very long waiting times. The Cycle Time explosion is made worse with variation. The more variation you have in customer demand and service time, the worse the Cycle Time explosion is. That’s why leveling (heijunka) is key…. on to Peter’s second post, I’m looking forward to reading it.

This Industrial Engineer is just happy to see anyone discussing Little’s Law.

By: Mark Graban

Mark Graban — Thu, 15 Jun 2006 12:53:09 +0000

One problem with your analysis there is that you’re assuming demand (and labor needs) are constant throughout the year. I’m certain that wouldn’t be the case for Amazon or any retailer.

By: www.comp-nerds.co.nr

www.comp-nerds.co.nr — Sat, 20 May 2006 22:37:23 +0000

If you found this article interesting, please digg it at: http://digg.com/technology/queueing_theory so more people will find it.