Approaches to Tackle the Nesting Problems

Júnior, Bonfim Amaro; Pinheiro, Plácido Rogério

doi:10.1007/978-3-319-33625-1_26

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…The approaches presented in this section were divided into three distinct categories as [26] mathematical programming, metaheuristics, and mixed strategies. For our research, we adopted a Biased Random-Key Genetic Algorithm (BRKGA) applied with multiple parallel populations to tackle ISPP.…”

Section: Approaches To Tackle Irregular Strip Packing Problemsmentioning

confidence: 99%

A Parallel Biased Random‐Key Genetic Algorithm with Multiple Populations Applied to Irregular Strip Packing Problems

Júnior

Pinheiro

Coelho

2017

Mathematical Problems in Engineering

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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.

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Section: Approaches To Tackle Irregular Strip Packing Problemsmentioning

confidence: 99%

A Parallel Biased Random‐Key Genetic Algorithm with Multiple Populations Applied to Irregular Strip Packing Problems

Júnior

Pinheiro

Coelho

2017

Mathematical Problems in Engineering

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“…This process, known as cutting and packing (C&P), is classified as an optimization problem. It aims to efficiently arrange items within a given space, maintaining the same dimension [1,2]. Employing heuristics and exact techniques for C&P can provide a significant commercial advantage [3].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Algorithms for Optimization Sequence of Cut in the Laser Cutting Path Problem

Junior,

de Carvalho,

Santos

et al. 2023

Applied Sciences

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Efficiently cutting smaller two-dimensional parts from a larger surface area is a recurring challenge in many manufacturing environments. This point falls under the cut-and-pack (C&P) problems. This study specifically focused on a specialization of the cut path determination (CPD) known as the laser cutting path planning (LCPP) problem. The LCPP aims to determine a sequence of cutting and sliding movements for the head that minimizes the parts’ separation time. It is important to note that both cutting and glide speeds (moving the head without cutting) can vary depending on the equipment, despite their importance in real-world scenarios. This study investigates an adaptive biased random-key genetic algorithm (ABRKGA) and a heuristic to create improved individuals applied to LCPP. Our focus is on dealing with more meaningful instances that resemble real-world requirements. The experiments in this article used parameter values for typical laser cutting machines to assess the feasibility of the proposed methods compared to an existing strategy. The results demonstrate that solutions based on metaheuristics are competitive and that the inclusion of heuristics in the creation of the initial population benefits the execution of the evolutionary strategy in the treatment of practical problems, achieving better performance in terms of the quality of solutions and computational time.

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“…Cutting and packing (C&P) are optimization problems that concern the efficient arrangement of items within a larger space with the same dimensionality. This type of problem appears in several real-world industrial settings, including manufacturing, logistics, and 3D printing [1][2][3]. Therefore, optimization methods for C&P can represent a valuable commercial advantage [4].…”

Section: Introductionmentioning

confidence: 99%

Metaheuristics for the Minimum Time Cut Path Problem with Different Cutting and Sliding Speeds

et al. 2021

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The problem of efficiently cutting smaller two-dimensional pieces from a larger surface is recurrent in several manufacturing settings. This problem belongs to the domain of cutting and packing (C&P) problems. This study approached a category of C&P problems called the minimum time cut path (MTCP) problem, which aims to identify a sequence of cutting and sliding movements for the head device to minimize manufacturing time. Both cutting and slide speeds (just moving the head) vary according to equipment, despite their relevance in real-world scenarios. This study applied the MTCP problem on the practical scope and presents two metaheuristics for tackling more significant instances that resemble real-world requirements. The experiments presented in this study utilized parameter values from typical laser cutting machines to assess the feasibility of the proposed methods compared to existing commercial software. The results show that metaheuristic-based solutions are competitive when addressing practical problems, achieving increased performance regarding the processing time for 94% of the instances.

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Approaches to Tackle the Nesting Problems

Cited by 5 publications

References 19 publications

A Parallel Biased Random‐Key Genetic Algorithm with Multiple Populations Applied to Irregular Strip Packing Problems

A Parallel Biased Random‐Key Genetic Algorithm with Multiple Populations Applied to Irregular Strip Packing Problems

Evolutionary Algorithms for Optimization Sequence of Cut in the Laser Cutting Path Problem

Metaheuristics for the Minimum Time Cut Path Problem with Different Cutting and Sliding Speeds

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