Informatica

This paper investigates the problem of partitioning a complete weighted graph into complete subgraphs, each having the same number of vertices, with the objective of minimizing the sum of edge weights of the resulting subgraphs. This NP-complete problem arises in many applications such as assignment and scheduling-related group partitioning problems and micro-aggregation techniques. In this paper, we present a mathematical programming model and propose a complementary column generation approach to solve the resulting model. A dual based lower bounding feature is also introduced to curtail the notorious tailing-off effects often induced when using column generation methods. Computational results are presented for a wide range of test problems.

In this paper, we present a new local search algorithm for solving the Quadratic Assignment Problem based on the Kernighan-Lin heuristic for the Graph Partitioning Problem. We also prove that finding a local optimum for the Quadratic Assignment Problem, with the neighborhood structure defined in the algorithm, is PLS-complete. The greatest advantages of the algorithm are its simplicity and speed in generating high quality solutions. The algorithm has been implemented and tested on an IBM 3090 computer with a variety of test problems of dimensions up to 100, including many test problems available in the literature and a new set of test problems with known optimal permutations.