Bidirectional best-fit heuristic considering compound placement for two dimensional orthogonal rectangular strip packing

Abstract

The two dimensional orthogonal rectangular strip packing problem is a common NPhard optimisation problem whereby a set of rectangular shapes must be placed on a fixed width stock sheet with infinite length in such a way that wastage is minimised and material utilisation is maximised. The bidirectional best-fit heuristic is a deterministic approach which has previously been shown to outperform existing heuristic methods as well as many metaheuristics from the literature. Here, we propose a modification to the original bidirectional best-fit heuristic whereby combinations of pairs of rectangles are considered generating improved results over standard benchmark sets.

3D overlapped grouping Ga for optimum 2D guillotine cutting stock problem

“…Some of the heuristics were described also in Ross et al [2] and Hopper et al [32]. Each one of the selection heuristics was unable to solve [27]. This will be reflected in achieving the best population with minimum trim loss, and patterns by using Selection heuristics improvements.…”

Free-form contour packing based on material grid approximation and lowest-gravity-center methods

“…However, this approach cannot solve problems without rotation constraints. A modified version of the bidirectional best-fit heuristic was presented to solve the orthogonal rectangular strip packing problem (Özcan, Kai, & Drake, 2013). This research considered the placement of rectangles in pairs.…”

Data mining based framework to assess solution quality for the rectangular 2D strip-packing problem

“…As examples of heuristics developed for solve the 2D-SPP with 90 degrees rotation using the area lower bound, He, Wu, and De Souza (2012) proposed an efficient deterministic heuristic algorithm that contains a rapid constructive phase and a partial tree search phase, working well mainly for zero-waste problem instances. In Özcan, Kai, and Drake (2013) a evolution of the best-fit position-based constructive heuristic is presented, improving the solutions for most of the benchmark problem instances.…”