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 (GLS). 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 using GLS. 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, and the results are compared with those obtained by heuristics and exact methods from the literature.

No abstract

No abstract

The history of the Euclidean Steiner tree problem, which is the problem of constructing a shortest possible network interconnecting a set of given points in the Euclidean plane, goes back to Gergonne in the early 19th century. We present a detailed account of the mathematical contributions of some of the earliest papers on the Euclidean Steiner tree problem. Furthermore, we link these initial contributions with results from the recent literature on the problem.

Abstract:The Euclidean Steiner tree problem asks for a shortest network interconnecting a set of terminals in the plane. Over the last decade, the maximum problem size solvable within 1 h (for randomly generated problem instances) has increased from 10 to approximately 50 terminals. We present a new exact algorithm, called geosteiner96 . It has several algorithmic modifications which improve both the generation and the concatenation of full Steiner trees. On average, geosteiner96 solves randomly generated problem instances with 50 terminals in less than 2 min and problem instances with 100 terminals in less than 8 min. In addition to computational results for randomly generated problem instances, we present computational results for (perturbed) regular lattice instances and public library instances.