matchpairs

Assign salespeople to flights such that the total cost of transportation is minimized.

A company has four salespeople who need to travel to key cities around the country. The company must book their flights, and wants to spend as little money as possible. These salespeople are based in different parts of the country, so the cost for them to fly to each city varies.

This table shows the cost for each salesperson to fly to each key city.

Each city represents a sales opportunity. If a city is missed, then the company loses out on an average revenue gain of $2,000.

Create a cost matrix to represent the cost of each salesperson flying to each city.

C = [600 670 960 560
     900 280 970 540
     310 350 950 820
     325 290 600 540];

Use matchpairs to assign the salespeople to the cities with minimal cost. Specify the cost of unassignment as 1000, since the cost of unassignment is counted twice if a row and a column remain unmatched.

M = matchpairs(C,1000)

M = 4×2

     3     1
     2     2
     4     3
     1     4

matchpairs calculates the least expensive way to get a salesperson to each city.

Match rows to columns when you have many more columns than rows in the cost matrix.

Create a 3-by-8 cost matrix. Since you have only three rows, matchpairs can produce at most three matches with the eight columns.

rng default % for reproducibility
C = randi([10 100], 3, 8)

C = 3×8

    84    93    35    97    97    22    82    13
    92    67    59    24    54    48    97    87
    21    18    97    98    82    93    69    94

Use matchpairs to match the rows and columns of the cost matrix. To get the maximum number of matches, use a large cost of unassignment (relative to the magnitude of the entries in the cost matrix). Specify three outputs to return the indices of unmatched rows and columns.

[M,uR,uC] = matchpairs(C,1e4)

M = 3×2

     3     2
     2     4
     1     8

uR =

  0x1 empty double column vector

uC = 5×1

     1
     3
     5
     6
     7

Five of the columns in C are not matched with any rows.

Assign taxis to routes such that the profit is maximized.

A taxi company has several ride requests from across the city. The company wants to dispatch its limited number of taxis in a way that makes the most money.

This table shows the estimated taxi fare for each of five ride requests. Only three of the five ride requests can be filled.

Create a profits matrix to represent the profits of each taxi ride.

P = [5.7 6.3 3.1 4.8 3.5
     5.8 6.4 3.3 4.7 3.2
     5.7 6.3 3.2 4.9 3.4];

Use matchpairs to match the taxis to the most profitable rides. Specify three outputs to return any unmatched rows and columns, and the 'max' option to maximize the profits. Specify the cost of unassignment as zero, since the company makes no money from unfilled taxis or ride requests.

costUnmatched = 0;
[M,uR,uC] = matchpairs(P,costUnmatched,'max')

M = 3×2

     1     1
     2     2
     3     4

uR =

  0x1 empty double column vector

uC = 2×1

     3
     5

matchpairs calculates the most profitable rides to fill. The solution leaves ride requests 3 and 5 unfilled.

Calculate the total profits for the calculated solution. Since costUnmatched is zero, you only need to add together the profits from each match.

TotalProfits = sum(P(sub2ind(size(P), M(:,1), M(:,2))))

TotalProfits = 17

Use matchpairs to track the movement of several points by minimizing the total changes in distance.

Plot a grid of points at time $\mathit{t}=0$ in green. At time $\mathit{t}=1$, some of the points move a small amount in a random direction.

[x,y] = meshgrid(4:6:16);
x0 = x(:)';
y0 = y(:)';
plot(x0,y0,'g*')
hold on
rng default % for reproducibility
x1 = x0 + randn(size(x0));
y1 = y0 + randn(size(y0));
plot(x1,y1,'r*')

Use matchpairs to match the points at $\mathit{t}=0$ with the points at $\mathit{t}=1$. To do this, first calculate a cost matrix where C(i,j) is the Euclidean distance from point i to point j.

C = zeros(size(x).^2);
for k = 1:length(y1)
  C(k,:) = vecnorm([x1(k)-x0; y1(k)-y0],2,1)';
end
C

C = 9×9

    2.8211    3.2750    9.2462    6.1243    6.3461   10.7257   11.7922   11.9089   14.7169
    4.9987    2.2771    7.5752    6.2434    4.3794    8.4485   11.1792   10.2553   12.5447
   15.2037    9.3130    3.7833   17.1539   12.2408    8.7988   20.7211   16.8803   14.5783
    6.9004    8.6551   13.1987    1.1267    5.3446   11.3075    5.1888    7.3633   12.3901
    8.6703    6.3191    8.7571    5.9455    0.3249    6.0714    8.2173    5.6816    8.3089
   13.5530    8.1918    4.7464   12.7818    6.8409    1.4903   14.6652    9.9242    7.3426
   11.5682   13.1257   16.8150    5.5702    8.3359   13.4144    0.4796    6.2201   12.2127
   13.6699   12.3432   13.7784    8.6461    6.3438    8.8167    5.8858    0.3644    6.1337
   20.6072   17.2853   15.6495   16.5444   12.1590    9.6935   13.9562    8.3006    3.8761

Next, use matchpairs to match the rows and columns in the cost matrix. Specify the cost of unassignment as 1. With such a low cost of unassignment relative to the entries in the cost matrix, it is likely matchpairs will leave some points unmatched.

M = matchpairs(C,1)

M = 5×2

     4     4
     5     5
     6     6
     7     7
     8     8

The values M(:,2) correspond to the original points $\left({\mathit{x}}_0,{\mathit{y}}_0\right)$, while the values M(:,1) correspond to the moved points $\left({\mathit{x}}_1,{\mathit{y}}_1\right)$.

Plot the matched pairs of points. The points that moved farther than 2*costUnmatched away from the original point remain unmatched.

xc = [x0(M(:,2)); x1(M(:,1))];
yc = [y0(M(:,2)); y1(M(:,1))];
plot(xc,yc,'-o')