The Indiana Jones take off is fine.
Mentioning the knapsack problem is less good
because it's not so important in practice.
Saying that the knapsack problem is difficult
to solve, e.g., encounters exponential
algorithms because technically it's in
NP-complete, is next to irrelevant for practice,
misleading, and hype and not fine.

> it's also important to update how you state your value to others

On this, I outlined my suggestion: Own a little
company and sell results based on how much money
they save the customer. Make the sale about
saving the customers money in ways that even
an auditor can confirm are correct.

INFORMS is clearly an echo chamber,
people in optimization looking for
work and talking to themselves.

Broadly for optimization in business, there is
a very serious problem: Optimization is not
a 'profession' like law, medicine, or major
parts of engineering. So, there is no licensing,
certification such as the CPA,
peer-review of practice, legal liability,
etc. So, as I said, the field "don't
get no respect".
Also missing is a point the legal profession
has: Any working lawyer must report only to
a lawyer; the interface between the optimization
guy and the business guy is nearly impossible.

> Is the point of your argument ...

I tried to make several points. One of the
points was about 'optimal'. The mathematical
definition is fine, but long that definition
was taken as suggesting that what we should
do in practice is look for such solutions,
then strain to find them, etc. That turned
out to be a grand mistake.

Why? Because maybe there is, compared with
what the customers is doing now, $10 million
to be saved with an optimal solution. But
too commonly saving all $10 million is too
difficult for the algorithms and computing.
So, straining to save all the $10 million
converts an important business problem into
a much more difficult mathematical problem.
It also turns out that commonly it's fairly
easy to save, say, $9 million. The difficulty is
saving the last of the money, and the
most difficult money to save is the last,
say, the last 10 cents.

'Optimal' was taken as a moral objective,
as I said, as if saving the last 10 cents
was worth much more than 10 cents.

Struggling over 'optimal' taken literally
and, thus, making real problems much more
difficult than necessary, was several torpedoes
below the waterline of the ship of optimization.

Part of this mistake was the simplistic and excessive
emphasis on NP-completeness -- for real
problems the whole P versus NP question is
next to irrelevant. One way to see this is
the simplex algorithm -- it's the core of
optimization and astoundingly fast in practice
but worst case exponential. There is a
polynomial algorithm for linear programming,
but it's way too slow in practice. In
practice, that an algorithm is worst case
exponential is commonly just irrelevant.

I had to conclude that for business, optimization
is a dead field. It got started due to WWII
and US DoD funding, and maybe in places there
is still some interest for US DoD problems.

Here is a little: A post above, in response to
a post of mine, claimed that IBM had a good
optimization group. If so, then good for IBM.
But I was at IBM's Watson lab, published a paper
on optimization,
and off and on
considered joining the optimization group there.
Phil Wolfe, William Pulleyblank,
Ellis Johnson. and others were in that group.
At one point, Roger Wets was visiting.
The group did the IBM Optimization
Subroutine Library (OSL).
Then in 3 years near 1994, IBM lost
$16 billion. Johnson joined
George Nemhauser at Georgia Tech.
Pulleyblank became a professor at
West Point. Basically the optimization
group fell apart. Maybe they put
a group back together, but
losing Johnson and Pulleyblank were
big mistakes.

E.g., again, with Pulleyblank at West Point,
the US DoD remains interested in
optimization.

Heck, I supported myself and my wife
through our Ph.D. degrees by working in
optimization for the US DoD.

In academics, the professors were to do research
to get the field going, e.g., research in
'systems analysis', 'mathematical sciences',
'civil engineering', 'production', etc. Yes,
if optimization problems were easy to solve,
then optimization would have central roles
in those fields. Alas, mostly important
practical
optimization problems
are not so easy to solve, even approximately. So, the professors
are still doing research -- maybe in some
decades or centuries they will have something
of serious importance for those fields. I doubt
that the research is very well supported.

I tried to give a summary of essentially a
'cultural contradiction' expecting optimization
to be a popular field in business: By the
time computing is ready to make optimization
easy enough, there are other things to do with
the computing making much more money than with
optimization.

It's not that optimization can't save money
in business; there is money to be saved;
in a lot of stable businesses, optimization
can provide some of the highest ROI available
to the business. So, there is some ground available
there, what is in principle some fertile ground.
So, there can be some optimization groups
here and there. If the course prof has such
a group in Australia, good for him. With
some really impressive 'cases' published in, say,
INFORMS, maybe mainline business will try
optimization again. I doubt it, but maybe.
Don't hold your breath waiting; there are lots
of impressive cases long since published in
INFORMS, and ORSA, Mangement Science,
etc. The optimization literature is huge
going back to the late 1940s, e.g.,
for Dantzig at Rand and Berkeley.

Here's a little on the difficulty: In the US
there are college accrediting groups, and
for some years they said that an undergraduate
degree in business should have courses
in optimization and statistics. So, for
years each business school student, undergraduate
or MBA, got a course in optimization. For
some years, I taught such courses.
Still the
field didn't take off.

I can't recommend that anyone try to have
a career in optimization in business.
You stand to have an easier time supporting
a family with a career as a plumber, literally.
With software, do an information technology
start up, sell out, and pocket, say, $10 million --
knocks the socks off optimization. With irony,
if interested in 'optimization' of your career
and financial security, then avoid optimization!

Optimization is like some item at Dunkin Donuts
that doesn't sell. Lots of other stuff at
Dunkin Donuts sells really well, but that one
item just doesn't. They can do a good job
getting the item ready to eat, put it out
in the display cases, and wait, and what happens
is the item just sits there and goes stale.
Then they throw away the stale, unsold items.
It was a waste. Finally, Dunkin Donuts just
quits offering the item.

Dunkin Donuts doesn't go on and on about
why the item really should sell.
Instead, they just listen to the clear
message they've gotten from the market
and, thus, save having to figure out
solid reasons it doesn't sell.

Similarly all across
business -- some stuff doesn't sell or
doesn't sell very well or sells only a little
and then only into a tiny market. Optimization
in business is like that -- at best, it's a
super tough sale; usually it just doesn't sell.

Optimization, as a field, in business, is a dead
duck. F'get about it and pursue something else.