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Quadratic assignment problem (QAP) is one of the most difficult combinatorial optimization problems in the NP-hard class. Due to the difficulty of the problem, many researchers have been studying this type of assignment problem. In this work, simulated annealing method is parallelized on MATLAB platform and is used to solve 36 problems from QAPLIB which is a well-known QAP library. The performance of different parallelization methods is compared for the problems used. As a result, when compared with the serial simulated annealing method, it is seen that the parallel methods give faster results when the appropriate parameters are used. [ABSTRACT FROM AUTHOR]

With this study, we propose a novel parallel robust tabu-search algorithm (Parallel-Tabu-QAP) for the NP-Hard Quadratic Assignment Problem (QAP)of whose instances having large number of location and facilites have not been reported to be solved exactly so far. Parallel- Tabu-QAP algorithm that has two phases optimizes the parameters of tabu-search algorithm in its first phase by using genetic algorithms through generations. The individuals that have parameters of the tabu-search are optimized in a population that is located on a master processor. In the second phase, slave processors optimize the solution of the problem by restarting their search process in case of stagnations. With its stagnation prevention and parallel optimization talents, parallel-tabu-QAP algorithm is observed to obtain better results than its sequential and statically parameter-tuned counterparts. The algorithm has 0.05% deviation for the benchmark tests performed on more than 100 problem instances. These experimental results show that the parallel-tabu-QAP algoritm is one the best performing techniques in its heuristics algorithms class when compared with state-of-the-art QAP algorithms. [ABSTRACT FROM AUTHOR]

In today's market conditions, increasing global competition, rapid changes in technology and shorter life cycle of product the facility layout to be evaluated as the dynamic facility layout problem (DFLP). The purpose of DFLP is to assign the departments to locations for a multi-period planning horizon so that the sum of the material handling and rearrangement costs is minimized. DFLP is generally formulated as a quadratic assignment problem (QAP) which is NP-hard. The larger the size of the problem, the harder to find the optimal solution. In this paper, a simulated annealing (SA) heuristic is developed to solve the DFLP. To test the performance of the proposed SA heuristic, a set of problems from literature is used. The results obtained show that the proposed heuristic is very effective for DFLP. [ABSTRACT FROM AUTHOR]