> An underappreciated queue type is LIFO (last-in, first-out). It sounds unfair, but it keeps you from moving the median response time at the cost of the maximum response time
Suppose you are a building contractor. You have given start dates for future jobs, but your current job is going to run over the expected time. You can choose between:
1 slip every job, annoying all of the customers whose jobs are queued up. You get a bad reputation.
2 Move onto the next job on time, and gradually complete the stalled job in the background by sending workers back to it when you have spare (which you should have, because in general you must overestimate or things will go badly wrong).
That customer will now suffer because their job is going to take a multiple of the expected time, but all of the other customers are happy, so your reputation is good.
I’ve observed airlines will do this as well if they have maintenance or gate queues. They will sacrifice 1-2 flights (hours late or even cancelled) to keep many other flights near on-time. Fewer angry customers, better reported average “on-time” metrics.
I had a section in the post I cut out about how optimizing queue selection started out as a technical problem, but transformed into more of a business and ethical problem the more I pondered it.
You're effectively deciding how to distribute suffering across a large group of people.
Comes up in any situation where large metric gains can be accomplished by optimizing for specific groups - recommender and personalization systems are another example.
1 slip every job, annoying all of the customers whose jobs are queued up. You get a bad reputation.
2 Move onto the next job on time, and gradually complete the stalled job in the background by sending workers back to it when you have spare (which you should have, because in general you must overestimate or things will go badly wrong). That customer will now suffer because their job is going to take a multiple of the expected time, but all of the other customers are happy, so your reputation is good.