The quadratic assignment problem (QAP) is one of the fundamental combinatorial optimization problems in the branch of optimization or operations research in mathematics, from the category of the facilities location problems. It was originally proposed by Tjalling Koopmans and Martin J. Beckmann. The problem models the following real-life problem:
There are a set of n facilities and a set of n locations. For each pair of locations, a distance is specified and for each pair of facilities a weight or flow is specified (e.g., the amount of supplies transported between the two facilities). The problem is to assign all facilities to different locations with the goal of minimizing the sum of the distances multiplied by the corresponding flows. The problem statement resembles that of the assignment problem, except that the cost function is expressed in terms of quadratic inequalities, hence the name.
Formal mathematical definition The formal definition of the quadratic assignment problem is as follows.
Given a positive integer n {\displaystyle n} and n 4 {\displaystyle n^{4}} cost coefficients { c i , j , p , q } {\displaystyle \{c_{i,j,p,q}\}} , find the n × n {\displaystyle n\times n} matrix [ x i j ] {\displaystyle [x_{ij}]} that minimizes the objective function
∑ i , j ∑ p , q c i , j , p , q x i , j x p , q {\displaystyle \sum _{i,j}\sum _{p,q}c_{i,j,p,q}x_{i,j}x_{p,q}}
subject to the constraints
∑ j x i , j = ∑ i x i , j = 1 , {\displaystyle \sum _{j}x_{i,j}=\sum _{i}x_{i,j}=1,}
x i j ∈ { 0 , 1 } {\displaystyle x_{ij}\in \{0,1\}}
Koopmans-Beckmann formulation The quadratic assignment problem was originally posed by Tjalling Koopmans and Martin J. Beckmann in the following form.
Given square matrices D and T, find the permutation matrix X that minimizes the double-dot product of T with A = X D X ⊺ {\displaystyle A=XDX^{\intercal }} . In other words, given D and T, find { x i j } 1 ≤ i , j ≤ n {\displaystyle \{x_{ij}\}_{1\leq i,j\leq n}} so as to
minimize ∑ 1 ≤ i , j ≤ n a i j t i j subject to ∑ 1 ≤ k ≤ n x i k = ∑ 1 ≤ k ≤ n x j k = 1 , x i j ∈ { 0 , 1 } . {\begin{aligned}{\text{minimize}}\quad &\sum _{1\leq i,j\leq n}a_{ij}t_{ij}\\{\text{subject to}}\quad &\sum _{1\leq k\leq n}x_{ik}=\sum _{1\leq k\leq n}x_{jk}=1,\\&x_{ij}\in \{0,1\}.\end{aligned}}
… excerpt ends here. Continue reading the full article.
