Transportation theory (mathematics) explained

In mathematics and economics, transportation theory or transport theory is a name given to the study of optimal transportation and allocation of resources. The problem was formalized by the French mathematician Gaspard Monge in 1781.[1]

In the 1920s A. N. Tolstoi was one of the first to study the transportation problem mathematically. In 1930, in the collection Transportation Planning Volume I for the National Commissariat of Transportation of the Soviet Union, he published a paper "Methods of Finding the Minimal Kilometrage in Cargo-transportation in space".[2] [3]

Major advances were made in the field during World War II by the Soviet mathematician and economist Leonid Kantorovich.[4] Consequently, the problem as it is stated is sometimes known as the Monge–Kantorovich transportation problem.[5] The linear programming formulation of the transportation problem is also known as the Hitchcock–Koopmans transportation problem.[6]

Motivation

Mines and factories

Suppose that we have a collection of

m

mines mining iron ore, and a collection of

n

factories which use the iron ore that the mines produce. Suppose for the sake of argument that these mines and factories form two disjoint subsets

M

and

F

of the Euclidean plane

R2

. Suppose also that we have a cost function

c:R2 x R2\to[0,infty)

, so that

c(x,y)

is the cost of transporting one shipment of iron from

x

to

y

. For simplicity, we ignore the time taken to do the transporting. We also assume that each mine can supply only one factory (no splitting of shipments) and that each factory requires precisely one shipment to be in operation (factories cannot work at half- or double-capacity). Having made the above assumptions, a transport plan is a bijection

T:M\toF

.In other words, each mine

m\inM

supplies precisely one target factory

T(m)\inF

and each factory is supplied by precisely one mine. We wish to find the optimal transport plan, the plan

T

whose total cost

c(T):=\summc(m,T(m))

is the least of all possible transport plans from

M

to

F

. This motivating special case of the transportation problem is an instance of the assignment problem.More specifically, it is equivalent to finding a minimum weight matching in a bipartite graph.

This can be generalized to the continuous case, where there are infinitely many mines and factories distributed on the real line, or generally in any metric space. This case is usually pictured as "changing the shape of a pile of dirt", and thus called the earth mover's problem.

Moving books: the importance of the cost function

The following simple example illustrates the importance of the cost function in determining the optimal transport plan. Suppose that we have

n

books of equal width on a shelf (the real line), arranged in a single contiguous block. We wish to rearrange them into another contiguous block, but shifted one book-width to the right. Two obvious candidates for the optimal transport plan present themselves:
  1. move all

n

books one book-width to the right ("many small moves");
  1. move the left-most book

n

book-widths to the right and leave all other books fixed ("one big move").If the cost function is proportional to Euclidean distance (

c(x,y)=\alpha\|x-y\|

for some

\alpha>0

) then these two candidates are both optimal. If, on the other hand, we choose the strictly convex cost function proportional to the square of Euclidean distance (

c(x,y)=\alpha\|x-y\|2

for some

\alpha>0

), then the "many small moves" option becomes the unique minimizer.

Note that the above cost functions consider only the horizontal distance traveled by the books, not the horizontal distance traveled by a device used to pick each book up and move the book into position. If the latter is considered instead, then, of the two transport plans, the second is always optimal for the Euclidean distance, while, provided there are at least 3 books, the first transport plan is optimal for the squared Euclidean distance.

Hitchcock problem

The following transportation problem formulation is credited to F. L. Hitchcock:[7]

Suppose there are

m

sources

x1,\ldots,xm

for a commodity, with

a(xi)

units of supply at

xi

and

n

sinks

y1,\ldots,yn

for the commodity, with the demand

b(yj)

at

yj

. If

c(xi, yj)

is the unit cost of shipment from

xi

to

yj

, find a flow that satisfies demand from supplies and minimizes the flow cost. This challenge in logistics was taken up by D. R. Fulkerson[8] and in the book Flows in Networks (1962) written with L. R. Ford Jr.[9]

Tjalling Koopmans is also credited with formulations of transport economics and allocation of resources.

Abstract formulation of the problem

Monge and Kantorovich formulations

The transportation problem as it is stated in modern or more technical literature looks somewhat different because of the development of Riemannian geometry and measure theory. The mines-factories example, simple as it is, is a useful reference point when thinking of the abstract case. In this setting, we allow the possibility that we may not wish to keep all mines and factories open for business, and allow mines to supply more than one factory, and factories to accept iron from more than one mine.

Let

X

and

Y

be two separable metric spaces such that any probability measure on

X

(or

Y

) is a Radon measure (i.e. they are Radon spaces). Let

c:X x Y\to[0,infty)

be a Borel-measurable function. Given probability measures

\mu

on

X

and

\nu

on

Y

, Monge's formulation of the optimal transportation problem is to find a transport map

T:X\toY

that realizes the infimum

inf\left\{\left.\intXc(x,T(x))d\mu(x)\right|T*(\mu)=\nu\right\},

where

T*(\mu)

denotes the push forward of

\mu

by

T

. A map

T

that attains this infimum (i.e. makes it a minimum instead of an infimum) is called an "optimal transport map".

Monge's formulation of the optimal transportation problem can be ill-posed, because sometimes there is no

T

satisfying

T*(\mu)=\nu

: this happens, for example, when

\mu

is a Dirac measure but

\nu

is not.

We can improve on this by adopting Kantorovich's formulation of the optimal transportation problem, which is to find a probability measure

\gamma

on

X x Y

that attains the infimum

inf\left\{\left.\intXc(x,y)d\gamma(x,y)\right|\gamma\in\Gamma(\mu,\nu)\right\},

where

\Gamma(\mu,\nu)

denotes the collection of all probability measures on

X x Y

with marginals

\mu

on

X

and

\nu

on

Y

.

Cost duality

Notes and References

  1. G. Monge. Mémoire sur la théorie des déblais et des remblais. Histoire de l'Académie Royale des Sciences de Paris, avec les Mémoires de Mathématique et de Physique pour la même année, pages 666–704, 1781.
  2. [Alexander Schrijver|Schrijver, Alexander]
  3. Ivor Grattan-Guinness, Ivor, Companion encyclopedia of the history and philosophy of the mathematical sciences, Volume 1, JHU Press, 2003. Cf. p.831
  4. L. Kantorovich. On the translocation of masses. C.R. (Doklady) Acad. Sci. URSS (N.S.), 37:199–201, 1942.
  5. Book: Cédric Villani. Topics in Optimal Transportation. 2003. American Mathematical Soc.. 978-0-8218-3312-4. 66.
  6. Book: Singiresu S. Rao. Engineering Optimization: Theory and Practice. 2009. John Wiley & Sons. 978-0-470-18352-6. 221. 4th.
  7. Frank L. Hitchcock (1941) "The distribution of a product from several sources to numerous localities", MIT Journal of Mathematics and Physics 20:224–230 .
  8. D. R. Fulkerson (1956) Hitchcock Transportation Problem, RAND corporation.
  9. [L. R. Ford Jr.]

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