Talk:Transportation theory (mathematics)

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Transport Mid‑importance

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I agree. This page is not a comprehensive assessment of transportation theory, from supply chain logistics to passenger travel analysis. There is no discussion of Daniel McFadden, Nobel Prize economist, whose work on discrete choice has been used as a standard in the industry to measure ridership. San Francisco's BART system is a case-study testimony to this with the inputs of route, mode, time in estimating a person's transportation decisions. -SociPoet — Preceding unsigned comment added by Socipoet (talk • contribs) 00:24, 12 July 2011 (UTC)[reply]

This page should be more academically aligned with problems such as linear programming and the transshipment problem. For example, the general transportation problem is a subset of the minimum cost flow problem.129.10.245.173 (talk) 22:28, 15 December 2010 (UTC)[reply]

Proposal: Rewrite completely with focus on modelling and linear programming

This page os far too complicated, for the average reader, and yet it contains too little information on how transport problems are actually solved in real life. Ideally the page should be rewritten completely with a focus on modelling and linear programming. Perhaps the best approach is to translate the German page into English https://de.wikipedia.org/wiki/Transportproblem

Support: I think an introduction or an introductory section with more details on the discrete version of the optimal transport problem would be beneficial for the reader. - Saung Tadashi (talk) 20:59, 25 February 2020 (UTC)[reply]

Mathematicians vs Economists Preferred Functions

The main purpose of this section is to document why I am removing the following section and for anybody to argue for its inclusion/modification if they would like.--Slaymaker1907 (talk) 09:05, 2 December 2018 (UTC)[reply]

While mathematicians prefer to work with convex cost functions,[citation needed] economists prefer concave ones.[citation needed] The intuitive justification for this is that once goods have been loaded onto, say, a freight train, transporting the goods 200 kilometers costs much less than what it would cost to transport them on a journey with 2 legs of 100 kilometers, when the extra costs of switching tracks, locomotives, and engineers are included. Concave cost functions represent this economy of scale.

The main issue with this paragraph is that is seems to misinterpret concave vs convex. The author's example of freight trains is neither difficult to model nor concave. A concave objective function is both pretty rare in simple examples as well as being notoriously difficult to actually solve both in practice and in theory. A concave cost function would be something like flying to Washtington D.C from Chicago costs $500 while flying from Chicago to N.Y. then to D.C. costs $300. That violates the triangle inequality and therefore makes it somewhat concave in nature since flying farther is actually cheaper (but flying farther is not always cheaper). Additionally, the comment about mathemiticians and economists seems very opinion based and is not backed up by citations.--Slaymaker1907 (talk) 09:05, 2 December 2018 (UTC)[reply]

Useful Reference is a better introduction

I found http://alexhwilliams.info/itsneuronalblog/2020/10/09/optimal-transport/ was a more readable introduction to the topic than the current article. I wonder if it would be worth adding a reference, or even inviting its author to contribute to this article. John Y (talk) 23:59, 17 May 2023 (UTC)[reply]

Proposal: delete pragraph on "Numerical solution in Excel"

What's up with this paragraph? I've never seen a description of an algorithm implementation in Excel on a math page, and it's definitely not theoretically illuminating. And it's written in a weird style too (second person, oddly formatted) as if it were a "how to do this in Excel" blog post. Ksimplex (talk) 21:16, 1 December 2023 (UTC) @Ksimplex: I agree, an it's also unsourced, and probably original "research". I have removed it from the article. JBW (talk) 21:49, 1 December 2023 (UTC)[reply]