The quadratic assignment problem is a classical combinatorial optimization problem that has garnered much attention due to both its many applications and its solution complexity. Originally used to model a location problem in the 1950s, the QAP is computationally very difficult to solve, making it an ideal candidate for metaheuristic approaches. Path relinking is an evolutionary metaheuristic based on maintaining and exploiting search information by drawing on principles shared in common with tabu search. This article proposes and implements a design for both a sequential and a parallel path-relinking algorithm tailored for the QAP. To demonstrate the benefits of the parallelization, we tested both the sequential and parallel versions of the algorithm using problems from QAPLIB. In spite of the underlying PR method's simplicity, we obtain results that are competitive with some of the best outcomes in the literature.This article is part of a special issue on advanced heuristics in transportation and logistics.