Tabu List Size Research Articles

Solving the combined part sequencing and tool replacement problem for an automated machining center: A tabu search approach

This paper addresses a joint part sequencing and tool replacement problem on an automated machining center. The objective is to minimize the expected production cost subject to available spare tools. In the literature, it has been shown that the sequencing problems with sequence-dependent setups are equivalent to the “travelling salesman problem” (TSP) and thus are NP-complete. The problem under consideration is further complicated by the position-dependent cost components associated with tool replacement decisions. To provide an efficient planning tool for shop floor decision making, a tabu search approach is proposed. The application of the proposed approach is demonstrated using an example problem. Our computational experience shows that good solutions can be found within a relatively short search time. The impact of tool spare level on the performance of the machining center is also examined. Finally, the effects of tabu-list size and path diversification are discussed.

Extensions of a tabu search adaptation to the quadratic assignment problem

The adaptation of tabu search to the Quadratic Assignment Problem (QAP) is refined by incorporating knowledge about permutations yielding good objective function values into the solution process. This knowledge serves two purposes. First, it is used to guide the nonimproving moves toward targeted “good” permutations. Second, it allows the possibility to restrict search in the solution space by fixing those parts of permutations that are common in good quality solutions. Fixing and freeing parts of permutations provides an interplay between intensification and diversification of search. Restricting the search neighborhood becomes essential in solution attempts for QAPs of larger dimensions due to the increase in computational time. Computational results for QAPs of dimension 100 are very promising. An implementation of the approach to dynamic tabu list sizes that creates “moving gaps” in the tabu list is also described. Combining these ingredients, the method obtains very good results for all Skorin-Kapov's problems of dimensions 42–90.

Massively parallel tabu search for the quadratic assignment problem

A new heuristic algorithm to perform tabu search on the Quadratic Assignment Problem (QAP) is developed. A massively parallel implementation of the algorithm on the Connection Machine CM-2 is provided. The implementation usesn2 processors, wheren is the size of the problem. The elements of the algorithm, calledPar_tabu, include dynamically changing tabu list sizes, aspiration criterion and long term memory. A new intensification strategy based on intermediate term memory is proposed and shown to be promising especially while solving large QAPs. The combination of all these elements gives a very efficient heuristic for the QAP: the best known or improved solutions are obtained in a significantly smaller number of iterations than in other comparative studies. Combined with the implementation on CM-2, this approach provides suboptimal solutions to QAPs of bigger dimensions in reasonable time.

Metastrategy simulated annealing and tabu search algorithms for the vehicle routing problem

The vehicle routing problem (VRP) under capacity and distance restrictions involves the design of a set of minimum cost delivery routes, originating and terminating at a central depot, which services a set of customers. Each customer must be supplied exactly once by one vehicle route. The total demand of any vehicle must not exceed the vehicle capacity. The total length of any route must not exceed a pre-specified bound. Approximate methods based on descent, hybrid simulated annealing/tabu search, and tabu search algorithms are developed and different search strategies are investigated. A special data structure for the tabu search algorithm is implemented which has reduced notably the computational time by more than 50%. An estimate for the tabu list size is statistically derived. Computational results are reported on a sample of seventeen bench-mark test problems from the literature and nine randomly generated problems. The new methods improve significantly both the number of vehicles used and the total distances travelled on all results reported in the literature.

Tabu Search Applied to the Quadratic Assignment Problem

This paper describes an adaptation of Tabu Search, a recent technique to overcome local optimality, to the Quadratic Assignment Problem (QAP). Computational experiments with different parameter values and different strategies have been performed for some QAPs from the literature and some randomly generated QAPs of dimensions varying between 42 and 90. The method is implemented in a flexible form which allows the user to interact and change the parameters (tabu list size, the iteration limit, a search diversification parameter and the number of new starting solutions) during the run. The results suggest that good tabu list sizes increase with dimension of the problem. The algorithm appears to be a very efficient method for QAPs: for the standard problems tested, the best known solutions were obtained using less CPU time than previously reported in the literature. For Steinberg's problem (n = 36) with rectangular distances, a better solution than previously known in the literature was obtained. In addition, in comparison with simulated annealing, tabu search appears to be superior with respect to the solution quality. INFORMS Journal on Computing, ISSN 1091-9856, was published as ORSA Journal on Computing from 1989 to 1995 under ISSN 0899-1499.