A Genetic Algorithm for solving Quadratic Assignment Problem(QAP)
This is an incremental improvement for researchers and practitioners in operations research and logistics, addressing a known bottleneck in solving QAP.
The paper tackles the Quadratic Assignment Problem (QAP), an NP-hard combinatorial optimization problem for facility layout, by proposing a Genetic Algorithm (GA) that solves it efficiently in reasonable time, as validated with examples from the QAP library.
The Quadratic Assignment Problem (QAP) is one of the models used for the multi-row layout problem with facilities of equal area. 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 aim of minimizing the sum of the distances multiplied by the corresponding flows. The QAP is among the most difficult NP-hard combinatorial optimization problems. Because of this, this paper presents an efficient Genetic algorithm (GA) to solve this problem in reasonable time. For validation the proposed GA some examples are selected from QAP library. The obtained results in reasonable time show the efficiency of proposed GA.