Abstract
The irregular strip packing problem (ISPP) is a class of cutting and packing problem (C&P) in which a set of items with arbitrary formats must be placed in a container with a variable length. The aim of this work is to minimize the area needed to accommodate the given demand. ISPP is present in various types of industries from manufacturers to exporters (e.g., shipbuilding, clothes, and glass). In this paper, we propose a parallel Biased Random-Key Genetic Algorithm (µ-BRKGA) with multiple populations for the ISPP by applying a collision-free region (CFR) concept as the positioning method, in order to obtain an efficient and fast layout solution. The layout problem for the proposed algorithm is represented by the placement order into the container and the corresponding orientation. In order to evaluate the proposed (µ-BRKGA) algorithm, computational tests using benchmark problems were applied, analyzed, and compared with different approaches.
Highlights
The large market dispute between manufacturing and exporters, coupled with the scarcity of some items that make up the raw material for a product manufacturing, has motivated research around the world to find answers that reproduce efficient solutions at a low cost.Cutting and packing (C&P) problems belong to NP-hard [1]
We propose a parallel Biased Random-Key Genetic Algorithm (μ-BRKGA) with multiple populations for the irregular strip packing problem (ISPP) by applying a collision-free region (CFR) concept as the positioning method, in order to obtain an efficient and fast layout solution
Our research focus is on a particular C&P problem commonly referred to as the irregular strip packing problem (ISPP)
Summary
Cutting and packing (C&P) problems belong to NP-hard [1]. A dataset of items must be packed in a two-dimensional stage and an objective function can be analyzed through the minimum area needed to place pieces or the maximum number of items allowed in a current layout. Our research focus is on a particular C&P problem commonly referred to as the irregular strip packing problem (ISPP). A variation of C&P can be described as a container (C) with a constant width (W) and a variable length (L) and a dataset of irregular polygons, where the objective is to minimize L and place (with no overlaps) the entire demand into C.
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