Paulo Oswaldo Boaventura-Netto

A survey for the quadratic assignment problem

The quadratic assignment problem (QAP), one of the most difficult problems in the NP-hard class, models many real-life problems in several areas such as facilities location, parallel and distributed computing, and combinatorial data analysis. Combinatorial optimization problems, such as the traveling salesman problem, maximal clique and graph partitioning can be formulated as a QAP. In this paper, we present some of the most important QAP formulations and classify them according to their mathematical sources. We also present a discussion on the theoretical resources used to define lower bounds for exact and heuristic algorithms. We then give a detailed discussion of the progress made in both exact and heuristic solution methods, including those formulated according to metaheuristic strategies. Finally, we analyze the contributions brought about by the study of different approaches.

The dynamic space allocation problem

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 (QAP) as a particular case, which puts it in the NP-hard class. In this context, the difficulty of identifying optimal solutions, even for instances of medium size, justifies the use of heuristic techniques. This work proposes a construction and a hybrid algorithm (HGT) based on the GRASP and Tabu search metaheuristics. Comparisons are presented for values obtained by HGT, pure GRASP versions, Tabu search and literature results. Computational results show the proposed methods to be competitive in relation to instances in the literature and to existing techniques.

GRASP para o PQA: um limite de aceitação para soluções iniciais

The Quadratic Assignment Problem (QAP) is an NP-hard problem which has been defying researchers both in theoretical aspects and in what concerns computational results. Owing to its high complexity, several heuristics have been developed trying to approximate its solution. The GRASP metaheuristic (greedy randomized adaptive search procedures) shows a good efficiency with it. In this work a proposal to discard initial supposedly bad initial solutionsis based on cost normalization is presented. A reduction of computational time to find optimal or near-optimal solutions was observed with this GRASP, when compared with the original version.

Combinatorial instruments in the design of a heuristic for the quadratic assignment problem

This work discusses the use of a neighbouring structure in the design of specific heuristics for the Quadratic Assignment Problem (QAP). This structure is formed by the 4- and 6-cycles adjacent to a vertex in the Hasse diagram of the permutation lattice and it can be adequately partitioned in subsets of linear and quadratic cardinalities, a characteristics which frequently allows an economy in the processing time. We propose also a restart strategy and a mechanism for generating initial solutions which constitute, together with the neighbouring structure, a possible QAP-specific heuristic proposal. For the construction of these instruments we used the relaxed ordered set of QAP solutions.

P-antiregular graphs

We generalize the definition of an antiregular graph by subjecting antiregularity to the presence of a given set P of graph properties. In this paper, we study the case where this set contains only vertex-connectivity. We discuss the construction of the class, looking also for structural characteristics and some properties.

QAPV: a polynomial invariant for graph isomorphism testing

To each instance of the Quadratic Assignment Problem (QAP) a relaxed instance can be associated. Both variances of their solution values can be calculated in polynomial time. The graph isomorphism problem (GIP) can be modeled as a QAP, associating its pair of data matrices with a pair of graphs of the same order and size. We look for invariant edge weight functions for the graphs composing the instances in order to try to find quantitative differences between variances that could be associated with the absence of isomorphism. This technique is sensitive enough to show the effect of a single edge exchange between two regular graphs of up to 3,000 vertices and 300,000 edges with degrees up to 200. Planar graph pairs from a dense family up to 300,000 vertices were also discriminated. We conjecture the existence of functions able to discriminate non-isomorphic pairs for every instance of the problem.

Efficient Metaheuristics For The Dynamic Space Allocation Problem

This work is devoted to the Dynamic Space Allocation Problem (DSAP), 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 (QAP) as a particular case, which places it in the NP-hard class. In this context, the difficulty of identifying optimal solutions, even for instances of medium size, justifies the use of heuristic techniques. This work presents new construction and local search methods and heuristic algorithms based on the VNS and ILS metaheuristics to obtain near optimal solutions for the DSAP. Comp arisons are presented for values obtained by VNS, ILS, and results from the literature. Computational results show the proposed methods to be competitive in relation to instances in the literature and to existing techniques.

METAHEURISTICS EVALUATION: A PROPOSAL FOR A MULTICRITERIA METHODOLOGY

In this work we propose a multicriteria evaluation scheme for heuristic algorithms based on the classic Condorcet ranking technique. Weights are associated to the ranking of an algorithm among a set being object of comparison. We used five criteria and a function on the set of natural numbers to create a ranking. The discussed comparison involves three well-known problems of combinatorial optimization - Traveling Salesperson Problem (TSP), Capacitated Vehicle Routing Problem (CVRP) and Quadratic Assignment Problem (QAP). The tested instances came from public libraries. Each algorithm was used with essentially the same structure, the same local search was applied and the initial solutions were similarly built. It is important to note that the work does not make proposals involving algorithms: the results for the three problems are shown only to illustrate the operation of the evaluation technique. Four metaheuristics - GRASP, Tabu Search, ILS and VNS - are therefore only used for the comparisons.

Ranking graph edges by the weight of their spanning arborescences or trees

A result based on a classic theorem of graph theory is generalized for edge-valued graphs, allowing determination of the total value of the spanning arborescences with a given root and containing a given arc in a directed valued graph. A corresponding result for undirected valued graphs is also presented. In both cases, the technique allows for a ranking of the graph edges by importance under this criterion. This ranking is proposed as a tool to determine the relative importance of the edges of a graph in network vulnerability studies. Some examples of application are presented.

Algoritmo guloso adaptativo e aleatório para o problema quadrático de alocação

O Problema Quadratico de Alocacao (PQA) pertence a classe dos problemas NP-hard. Apesar de muitos metodos heuristicos terem sido desenvolvidos visando a resolucao de suas instâncias, ainda nao e possivel encontrar solucoes otimas para instâncias de ordem acima de 25- Uma versao do GRASP, desenvolvida por Li, Pardalos e Resende [LPR94], se mostrou bastante eficiente para o PQA, o que motivou os autores a elaborar uma proposta de modificacao na sua busca local, na tentativa de diminuir o numero de iteracoes necessario a obtencao da melhor solucao conhecida.