I believe I get fully what you are saying.

At one point, we need a correction: The optimization was to schedule the fleet for FedEx. They were spending big time money on the airplane operations, and a better schedule, saving even 1% of that, would amount to maybe $millions a year, at any rate, money worth saving and where, in comparison, my work cost peanuts. At FedEx at the time, the 1%, the automation of the fleet scheduling, the business planning potential, made the project worthwhile.

The "decision maker" was F. Smith, founder, COB, CEO. My office was next to his. On paper I reported to a Senior VP, but in reality reported directly to Smith. Smith wrote a memo giving the optimization problem to me -- I still have a copy. The project was approved, and by passing all the executive considerations you mentioned. The project was not "killed". Instead, due to (a) commuting between Memphis and Maryland where my wife was in her Ph.D. program, (b) wanting to stay in Maryland, where I'd done the best work for FedEx and saved it the first time (access to consultants and time sharing computing), (c) the Senior VP I was reporting to telling me there "was no money in the budget for me", and (d) the promised stock very late, I left for the Ph.D.

I got Smith to approve the project by explaining "Integer linear programming set covering" to him for this application: (i) Take all or a reasonably large subset of all the reasonable tours from Memphis and back. All the planes were the same, French Falcons, .... (ii) For each tour, program standard means of evaluating flight times and costs and find the cost of the tour. Throw out some tours for however goofy reasons, e.g., can't fly over this city at that time of day. Note, some of the costs and constraints were really goofy, way beyond what could be converted to software even for non-linear integer programming. Such is the magic of set covering enumeration.

(iii) Intuitively regard each tour as a piece in a jigsaw puzzle where have lots of extra pieces, each piece has a cost, and want to cover. "set covering", the board exactly at minimum total cost. That's when Smith understood the work well enough to approve it as my project. Considering what airplanes cost, my project was not very expensive.

(iv) Now have a 0-1 integer linear program with one row for each city and one column for each tour. In a column, a row i = 1 to 90 is 1 if the tour serves city i and a 0 otherwise. The cost of that column is just the cost of the tour. The right side is all 1s. The constraints are all >=. With 90 US cities, there were only 90 rows in the linear program. So, use some LP (linear programming) software to get solutions feasible and optimal or nearly so. At some point if necessary just use old branch and bound. Worth a try. My computing was VM/CMS time sharing on relatively large IBM mainframes.

"1% is a fraction of inflation." Maybe you are saying that CEOs should have optimization involved only for super big aspects of the company, like Ike's work on D-day, and ignoring a 1% reduction in the cost of M1 rifle ammunition. Well, not then at FedEx. Fleet operating costs were by far the biggest expense of the company and .... Even for D-day, such a 1% reduction might have been worthwhile but to be handled by some Colonel and not Ike!!

Your other points seem to be correct. One response is when working for US national security, some of the problems were
"strategic" for the US and where ... would be as you describe "advising" "options" at no higher than some mid-level uniform in the Pentagon and not for the POTUS. But for my work in the commercial economy, I never was trying to advize the CEO of some $800 billion company on some crucial decision. And I didn't work on enough problems to encounter many of the considerations you listed.

Again, my main point here was just the "applied" optimization in the book title in the OP. My experience, academic, military, commercial, student, professor, research and applications, indicates that outside of the US military there is nearly nothing real about "applied" optimization -- optimization applications are nearly like hen's teeth.

Maybe Amazon, Walmart, Google, Microsoft, and a few of the largest companies have an office of planning and analysis on the organization chart reporting to some VP for something or other and there occasionally develop/run some optimization models, but I never saw any evidence of such. Sure, such an "office" would have to do the TLC of the CEO you mentioned. I'd guess that such an "office" would get their optimization done with a lot of contact with some professors, but the only professors I ever saw doing any such work were the few I contacted. I just didn't see any credible evidence of "applied" optimization in the US commercial economy.

Now, in the last 10 years or so, everything about such optimization -- data collection, data manipulation, word processing for the math, the basic desktop computing, statistical tools, and, sure, Gurobi, etc. are just MUCH better. Soooo, maybe now the US commercial world is ready to exploit optimization like Dantzig, Kuhn, Tucker, Arrow, Hurwicz, Nemhauser, etc. intended. Maybe.

If I got an offer, now, for such work I'd turn it down and stay with my startup. (a) You're talking too much in office politics; that's not one of my specialties; and I have none of that in my startup. (b) I know the math for my startup, all nicely written up with TeX, but I'm rusty on a lot of the rest of math. E.g., for Lagrangian relaxation I'd look at my notes for the last time I did that. For some of the classic integer programming on graphs, I'd go for my grad school notes. For anything in probability, I'd want to review the Radon-Nikodym theorem and conditional expectation, all the standard theorems on convergence of random variables, etc. (c) From the time I got the offer, I'd have to start spending my money, and I might get fired before I even got that back -- such a job would be a gamble where I could lose money significant for me.

Again, my post here was just to object to "applied" for optimization. No way am I looking for a job; not now; not any more; not again. Instead I'm staying with my startup.