Mathematical Programming Examples Continued
Example 5: Reallocating Students to Achieve Racial Balance.
To comply with recent judicial guidelines on racial composition,
a school district must reallocate its white and nonwhite high school students
to its five schools. This reallocation will be achieved by busing students.
At present, all students attend the school in their neighborhood, giving
rise to the racial imbalance. The school district's Board of Education
(BOE) has compiled the following data on the enrollment of the five schools
and the district as a whole:
School | Total Enrollment | Nonwhite Students | White Students | % White |
1 | 1000 | 200 | 800 | 80 |
2 | 800 | 200 | 600 | 75 |
3 | 500 | 50 | 450 | 90 |
4 | 1200 | 900 | 300 | 25 |
5 | 500 | 450 | 50 | 10 |
District | 4000 | 1800 | 2200 | 55 |
To School 1 | To School 2 | To School 3 | To School 4 | To School 5 | |
To School 1 |
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To School 2 |
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To School 3 |
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To School 4 |
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To School 5 |
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Formulate a mathematical program that the BOE can use
to determine an acceptable busing plan that minimizes the total distance
traveled by the students.
Solution:
Decision variables:
Let Wij and Nij be the number of white and nonwhite students reallocated from school i to school j, respectively.
Let Ei be the enrollment at school i after the reallocation. Let EWi and ENi be the white and nonwhite enrollment at school i after the reallocation, respectively.
Objective Function:
Let dij be the distance from school i to school j as given in the table above. Since dii = 0, the total distance traveled by the bused students is given by the objective function
d11 (W11 + N11) + d12 (W12 + N12) + d13 (W13 + N13) +
d14 (W14 + N14) + d15(W15 + N15) + ... +
d51 (W51 + N51) + d52 (W52 + N52) + d53 (W53 + N53) +
d54 (W54 + N54) + d55 (W55 + N55).
We wish to minimize this function subject to the following sets of constraints:
The number of nonwhite students will be
ENj = N1j + N2j + N3j + N4j + N5j.
The total enrollment at school j will be
Ej = EWj + ENj.
The proportion of white students will be EWj/Ej.
Thus, for each school j have the following constraints:
EWj = W1j + W2j + W3j + W4j + W5j
ENj = N1j + N2j + N3j + N4j + N5j
Ej = EWj + ENj
0.45 <= EWj/Ej <= 0.65.
These last constraints are not linear as written, so we
rewrite them as
0.45 Ej <= EWj
EWj <= 0.65 Ej
2) BOE Guideline 2
If E1 is greater than 1000, it means that the enrollment
at school 1 will increase.
Since the enrollment at any school may not increase by
more than 5%,
we must add the constraint E1 <= 1050.
If E1 is less than 1000, it means that the enrollment
at school 1 will decrease.
Since the enrollment at any school may not decrease by
more than 15%,
we must add the constraint E1 >= 850.
We will have four more sets of similar constraints for the other schools.
3) BOE Guideline 3
Schools 1, 2 and 3 are predominantly white. Thus,
W12, W13, W21, W23, W31 and W32 = 0.
Schools 4 and 5 predominantly nonwhite, Thus
N14, N15, N24, N25, N34, N35, N45 and N54 = 0.
Finally, we require all variables to be >=0.