The Vehicle Positioning Problem (VPP) is a classical combinato-rial optimization problem that has a natural formulation as an integer quadratic program. This MIQCP is closely related to the Quadratic Assignment Problem and, as far as we know, has not received any attention yet. We show in this article that such a formulation has interesting theoretical properties. Its QP relaxation produces, in...

We present the first linear formulation using distance variables (used previously for the Linear Arrangement Problem) to solve the Quadratic Assignment Problem (QAP). The model involves O(n2) variables. It has been stengthened by facets and valid inequalities, and numerically tested with QAPLIB instances whose distance matrices are given by the shortest paths in grid graphs. For all the instanc...

Ant Colony Optimization (ACO) is a bioinspired metaheuristic based on ants foraging used to solve different classes of problems. In this paper, we show how, using a Two-Stage approach the quality of the solutions of ACO is improved. The Two-Stage approach can be applied to different ACO. The performance of this new approach is studied in the Traveling Salesman Problem and Quadratic Assignment P...

This paper is concerned with automated tuning of parameters in local-search based meta-heuristics. Several generic approaches have been introduced in the literature that returns a ”one-size-fits-all” parameter configuration for all instances. This is unsatisfactory since different instances may require the algorithm to use very different parameter configurations in order to find good solutions....

The quadratic assignment problem (QAP) is one of the most difficult combinatorial optimization problems. One of the most powerful and commonly used heuristics to obtain approximations to the optimal solution of the QAP is simulated annealing (SA). We present an efficient implementation of the SA heuristic which performs more than 100 times faster then existing implementations for large problem ...

The Genetic Immune Strategy for Multiple Objective Optimization (GISMOO) is a hybrid algorithm for solving multiobjective problems. The performance of this approach has been assessed using a classical combinatorial multiobjective optimization benchmark: the multiobjective 0/1 knapsack problem (MOKP) [1] and two-dimensional unconstrained multiobjective problems (ZDT) [2]. This paper shows that t...

By the use of the GRIBB software for distributed computing across the Internet, we are investigating the obstacles and the potential for efficient parallelization of Branch and Bound algorithms. Experiments have been carried out using two different applications, i.e. the Quadratic Assignment Problem (QAP) and the Traveling Salesman Problem (TSP). The results confirm the potential of the approac...

This work is devoted to the Dynamic Space Allocation Problem, where project duration is divided into a number of consecutive periods, each of them associated with a number of activities. The resources required by the activities have to be available in the corresponding workspaces and those sitting idle during a period have to be stored. This problem contains the Quadratic Assignment Problem (QA...

Glover and Punnen (1997) asked whether there exists a polynomial time algorithm that always produces a tour which is not worse than at least n!=p(n) tours for some polynomial p(n) for every TSP instance on n cities. They conjectured that, unless P=NP, the answer to this question is negative. We prove that the answer to this question is, in fact, positive. A generalization of the TSP, the quadra...

In the classical Travelling Salesman Problem (TSP), the objective function sums the costs for travelling from one city to the next city along the tour. In the q-stripe TSP with [Formula: see text], the objective function sums the costs for travelling from one city to each of the next q cities in the tour. The resulting q-stripe TSP generalizes the TSP and forms a special case of the quadratic a...