I'm surprised they're only pricing each mile at $5. The opportunity cost of time on the bus would be a lot more than that too.
MIT team’s school-bus algorithm could save $5M and 1M bus miles
41–50 of 113 posts
Re: MIT team’s school-bus algorithm could save $5M and 1M bus miles
#42Since this forum for founder/entrepreneur types, let me inject a bit of caution from my own experience with dealing with government organizations: If you save a business organization money, they appreciate it. If you save a government organization money, the next year the "savings" is likely to be deducted from their budget. They don't get to benefit from the savings and thus their motivations aren't what you might e…
Interestingly in this case it seems it was the government that initiated the search for cost savings: > In hopes of spending less this year, the school system offered $15,000 in prize money in a contest that challenged competitors to reduce the number of buses.
The way this typically works is that they were told from on high that their budget was getting cut, so they had to figure out a way to do so. Not sure if that happened here, or if these are just folks who are really interested in governing well, but that's been my experience.
Re: MIT team’s school-bus algorithm could save $5M and 1M bus miles
#43Since this forum for founder/entrepreneur types, let me inject a bit of caution from my own experience with dealing with government organizations: If you save a business organization money, they appreciate it. If you save a government organization money, the next year the "savings" is likely to be deducted from their budget. They don't get to benefit from the savings and thus their motivations aren't what you might e…
The part that always blew me away about governmentese was when i realized that "budget cuts" meant the program isnt getting less money than last fiscal year, but their increase YoY has been reduced!
How else could you plan beyond one year, other than in reference to expected increases/decreases in subsequent years? Those expected increases can't be ignored in "the budget", so they are planned for and probably allocated before that fiscal year ever starts. If you reduce that increase, you absolutely cut the budget.
Re: MIT team’s school-bus algorithm could save $5M and 1M bus miles
#44Earlier quoted context omitted.
The part that always blew me away about governmentese was when i realized that "budget cuts" meant the program isnt getting less money than last fiscal year, but their increase YoY has been reduced!
How else could you plan beyond one year, other than in reference to expected increases/decreases in subsequent years? Those expected increases can't be ignored in "the budget", so they are planned for and probably allocated before that fiscal year ever starts. If you reduce that increase, you absolutely cut the budget.
But to an idiot like me, the absolute amount should go down if it is a cut; not up!
So if i spent $500 eating lunch at work two years ago; $600 last year and plan/allocated $610 this year i had a "budget cut?" no. I spent more.
Thats how i think
Re: MIT team’s school-bus algorithm could save $5M and 1M bus miles
#45Earlier quoted context omitted.
How else could you plan beyond one year, other than in reference to expected increases/decreases in subsequent years? Those expected increases can't be ignored in "the budget", so they are planned for and probably allocated before that fiscal year ever starts. If you reduce that increase, you absolutely cut the budget.
But to an idiot like me, the absolute amount should go down if it is a cut; not up! So if i spent $500 eating lunch at work two years ago; $600 last year and plan/allocated $610 this year i had a "budget cut?" no. I spent more. Thats how i think
I suppose they should be talking about budget velcoties.
Re: MIT team’s school-bus algorithm could save $5M and 1M bus miles
#46Given the scale of the problems they are tackling, I'm quite surprised how little they are saving. $5m over $120m is only 4%. I would imagine municipalities' manual route planners wouldn't be that great.
We have found with many real-world scenarios, that at a much smaller scale we could save easily up to 40% in driving time and fuel costs. When we studied cases of 20+ vehicles and ~1000 stops, sometimes the savings were up to 60%.
In one scenario we took 8 cars off the road form a fleet of 30. [1] That's 26% compared to the article's 11.5%. Not to discount its results, dropping 75 bus routes is incredible! Imagine dropping another 75 :)
Note that since this is an NP-complete problem, the larger the size of the problem, the more constraints you add, the harder it is for any human route planner to plan routes efficiently -- so the larger the potential efficiency gains for an algorithm.
Disclaimer/plug: founder of Routific here.
Re: MIT team’s school-bus algorithm could save $5M and 1M bus miles
#47I worked with a startup years ago that did these kinds of optimizations (for deliveries). The logistics & transportation market in the US is about a TRILLION dollars -- small optimizations can make a HUGE impact. The challenge for practical implementations is that there are lots and lots and lots of optimization factors that are very hard to account for. Often, you don't even know what they are until you try to autom…
Another example: a lot of rural areas have patches of gravel roads. It's not obvious from Google maps where the roads go from paved to gravel (and they sometimes do it in random sections). Naively looking at a map, you'd think a roundabout route had potential to be shorter, but not realize the road was gravel.
In rural areas, school buses drive on gravel. Where I grew up, the buses were speed limited by law, so the speed on gravel is often the same as on the highway. Road surface might not matter as much as you think.
Re: MIT team’s school-bus algorithm could save $5M and 1M bus miles
#48Since this forum for founder/entrepreneur types, let me inject a bit of caution from my own experience with dealing with government organizations: If you save a business organization money, they appreciate it. If you save a government organization money, the next year the "savings" is likely to be deducted from their budget. They don't get to benefit from the savings and thus their motivations aren't what you might e…
There is a difference between local government and state or federal government here: local governments are more likely to be perennially underfunded, rather than competing with other agencies for federal money.
Re: MIT team’s school-bus algorithm could save $5M and 1M bus miles
#49Earlier quoted context omitted.
How else could you plan beyond one year, other than in reference to expected increases/decreases in subsequent years? Those expected increases can't be ignored in "the budget", so they are planned for and probably allocated before that fiscal year ever starts. If you reduce that increase, you absolutely cut the budget.
But to an idiot like me, the absolute amount should go down if it is a cut; not up! So if i spent $500 eating lunch at work two years ago; $600 last year and plan/allocated $610 this year i had a "budget cut?" no. I spent more. Thats how i think
Think of the budget as a multi-year allocation. If Budget A from 2 years ago was "$500 this year, $600 next year, $700 the year after that" and now you reduce the allocation to $610, that is a budget cut from the original Budget A.
Re: MIT team’s school-bus algorithm could save $5M and 1M bus miles
#50I worked with a startup years ago that did these kinds of optimizations (for deliveries). The logistics & transportation market in the US is about a TRILLION dollars -- small optimizations can make a HUGE impact. The challenge for practical implementations is that there are lots and lots and lots of optimization factors that are very hard to account for. Often, you don't even know what they are until you try to autom…
Despite gaps between technology and the real-world, there is still tremendous value to be had by technology, if you combined it with the real-world, i.e. humans.
Routing algorithms can come up with extremely efficient routes, which can serve as a starting point for the human route planner to fix (to account for real-world knowledge).
Consider the alternative where the human route planner starts from scratch.