Showing papers by "David Pisinger published in 2003"

Guided Local Search for the Three-Dimensional Bin-Packing Problem

Abstract: The three-dimensional bin-packing problem is the problem of orthogonally packing a set of boxes into a minimum number of three-dimensional bins. In this paper we present a heuristic algorithm based on guided local search. Starting with an upper bound on the number of bins obtained by a greedy heuristic, the presented algorithm iteratively decreases the number of bins, each time searching for a feasible packing of the boxes. The process terminates when a given time limit has been reached or the upper bound matches a precomputed lower bound. The algorithm can also be applied to two-dimensional bin-packing problems by having a constant depth for all boxes and bins. Computational experiments are reported for two- and three-dimensional instances with up to 200 boxes, showing that the algorithm on average finds better solutions than do heuristics from the literature.

Dynamic Programming on the Word RAM

Abstract: Dynamic programming is one of the fundamental techniques for solving optimization problems. In this paper we propose a general framework which can be used to decrease the time and space complexity of these algorithms with a logarithmic factor. The framework is based on word encoding, i.e. by representing subsolutions as bits in an integer. In this way word parallelism can be used in the evaluation of the dynamic programming recursion. Using this encoding the subset-sum problem can be solved in O( n b/ log b) time and O(b/ log b) space, where n is the number of integers given and b is the target sum. The knapsack problem can be solved in O( n m/ log m) time and O(m/ log m) space, where n is the number of items and m = max{b,z} is the maximum of the capacity b and the optimal solution value z . The problem of finding a path of a given length b in a directed acyclic graph G=(V,E) can be solved in O(|E|b/ log b) time and O(|V|b/ log b) space. Several other examples are given showing the generality of the achieved technique. Extensive computational experiments are provided to demonstrate that the achieved results are not only of theoretical interest but actually lead to algorithms which are up to two orders of magnitude faster than their predecessors. This is a surprising observation as the increase in speed is larger than the word size of the processor.

Guided Local Search for Final Placement in VLSI Design

Abstract: The design of a VLSI circuit consists of two main parts: First, the logical functionality of the circuit is described, and then the physical layout of the modules and connections is settled In the latter process one wishes to place the modules such that the necessary wiring becomes as small as possible in order to minimize area usage and delays on signal paths The placement problem is the subproblem of the layout problem which considers the geometric locations of the modules A new heuristic is presented for the general cell placement problem where the objective is to minimize total bounding box netlength The heuristic is based on the Guided Local Search (GLS) metaheuristic GLS modifies the objective function in a constructive way to escape local minima Previous attempts to use local search on final (or detailed) placement problems have often failed as the neighbourhood quickly becomes too excessive for large circuits Nevertheless, by combining GLS with Fast Local Search it is possible to focus the search on appropriate sub-neighbourhoods, thus reducing the time complexity considerably Comprehensive computational experiments with the developed algorithm are reported on a broad range of industrial circuits The experiments demonstrate that the developed algorithm is able to improve the estimated routing length of large-sized layouts with as much as 20 percent when compared to existing algorithms