> Not to dismiss the actual research that has been done, but... Estimates are mostly bullshit anyway.
Right, but in some cases you can know fairly well or quite well. E.g., say you are planning and one of the exogenous
random variables is the number of people,
or customers, who arrive
during the plan. Then with just a few assumptions, that
mostly can be checked just intuitively, that number of
people will have Poisson distribution and all that have to
estimate is the arrival rate parameter. One of the random variables might have to do with weather, but there is a lot
of data on the probability distribution of what the
weather can do.
But generally, yes, that applied math is a lot of theorems
and proofs about what the heck to do if you had a lot more
data than you do have. Or, in the South Pacific, it is an
airline service from island A to island B, terrific if
you are at A and want to get to B but no good if there's
no chance of getting to island A.
As I recall, long in some major US DoD projects,
keeping track of the critical paths and adding
resources to those was regarded as doing a
lot of good.
A related part of project planning is the subject,
often pursued in B-schools, of materials requirements
planning -- in practice there can be a lot to that.
And closely related there is supply chain optimization,
that is, when the heck will the critical parts arrive
for our poor project?
Also related is constraint logic programming or
how the heck to load all those 18 wheel trucks, each
with 40,000 pounds of boxes of fresh pork, from our
only three loading docks where each truck gets what
its scheduled deliveries need and the boxes are ready
from the kill and cut operation when the truck is
parked at the loading dock? Real problem. Such problems
are commonly looking for just a feasible solution, that
is, not necessarily an optimal solution, to some
constraints that might have been for an optimization
problem. Well, then, in such optimization, just
finding a first feasible solution is in principle as
hard as finding an optimal solution given a feasible
solution, so that constraint logic programming gets
into optimization. At one time, SAP, C-PLEX, etc.
got heavily involved.
Another planning problem is dial-a-ride bus
scheduling -- one of my Ph.D. dissertation
advisors tried to get me to pursue that problem
as a dissertation, but I avoided it like the Big
Muddy Swamp full of huge alligators and poisonous
snakes and picked another problem, right,
in stochastic optimal control, a problem I
could actually get some clean results in.
Did I mention, project planning is a big field?
Your software looks like it has a user interface a lot
of people will like a lot, but with enough usage
some users will still encounter some of the
challenging aspects of project planning.
Right, but in some cases you can know fairly well or quite well. E.g., say you are planning and one of the exogenous random variables is the number of people, or customers, who arrive during the plan. Then with just a few assumptions, that mostly can be checked just intuitively, that number of people will have Poisson distribution and all that have to estimate is the arrival rate parameter. One of the random variables might have to do with weather, but there is a lot of data on the probability distribution of what the weather can do.
But generally, yes, that applied math is a lot of theorems and proofs about what the heck to do if you had a lot more data than you do have. Or, in the South Pacific, it is an airline service from island A to island B, terrific if you are at A and want to get to B but no good if there's no chance of getting to island A.
As I recall, long in some major US DoD projects, keeping track of the critical paths and adding resources to those was regarded as doing a lot of good.
A related part of project planning is the subject, often pursued in B-schools, of materials requirements planning -- in practice there can be a lot to that. And closely related there is supply chain optimization, that is, when the heck will the critical parts arrive for our poor project?
Also related is constraint logic programming or how the heck to load all those 18 wheel trucks, each with 40,000 pounds of boxes of fresh pork, from our only three loading docks where each truck gets what its scheduled deliveries need and the boxes are ready from the kill and cut operation when the truck is parked at the loading dock? Real problem. Such problems are commonly looking for just a feasible solution, that is, not necessarily an optimal solution, to some constraints that might have been for an optimization problem. Well, then, in such optimization, just finding a first feasible solution is in principle as hard as finding an optimal solution given a feasible solution, so that constraint logic programming gets into optimization. At one time, SAP, C-PLEX, etc. got heavily involved.
Another planning problem is dial-a-ride bus scheduling -- one of my Ph.D. dissertation advisors tried to get me to pursue that problem as a dissertation, but I avoided it like the Big Muddy Swamp full of huge alligators and poisonous snakes and picked another problem, right, in stochastic optimal control, a problem I could actually get some clean results in.
Did I mention, project planning is a big field?
Your software looks like it has a user interface a lot of people will like a lot, but with enough usage some users will still encounter some of the challenging aspects of project planning.
Good luck with your software.