2023/10/02 by Puneet S. Bagga, Bagga, Puneet S., Arthur Delarue +1
Computer Science · Decision Sciences · Engineering · #Artificial Intelligence (cs.AI) #Auction Theory and Applications #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Metaheuristic Optimization Algorithms Research #Optimization and Control (math.OC) #Vehicle Routing Optimization Methods
paper · pdf · doi:10.48550/arxiv.2310.01604
openalex publication_date 2023/10/02 · openalex created_date 2023/10/05 · openalex updated_date 2026/07/28
The Quadratic Assignment Problem (QAP) is an NP-hard problem which has proven particularly challenging to solve: unlike other combinatorial problems like the traveling salesman problem (TSP), which can be solved to optimality for instances with hundreds or even thousands of locations using advanced integer programming techniques, no methods are known to exactly solve QAP instances of size greater than 30. Solving the QAP is nevertheless important because of its many critical applications, such as electronic wiring design and facility layout selection. We propose a method to solve the original Koopmans-Beckman formulation of the QAP using deep reinforcement learning. Our approach relies on a novel double pointer network, which alternates between selecting a location in which to place the next facility and a facility to place in the previous location. We train our model using A2C on a large dataset of synthetic instances, producing solutions with no instance-specific retraining necessary. Out of sample, our solutions are on average within 7.5% of a high-quality local search baseline, and even outperform it on 1.2% of instances.