Multi-objective optimization of a two-dimensional cutting problem using genetic algorithms

Tiwari, Santosh Kumar; Chakraborti, Nirupam

doi:10.1016/j.jmatprotec.2005.12.011

Cited by 31 publications

(22 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The works dealing with such a multi-objective problem are almost inexistent [3], [4], [5] and they all are based on the usage of Multi-Objective Evolutionary Algorithms (MOEAs). Moreover, the approaches tackling guillotine problems are based on representations where the pattern layouts are explicitly encoded [3], [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hyperheuristic codification for the multi-objective 2D Guillotine Strip Packing Problem

Miranda

Armas

Segura

et al. 2010

IEEE Congress on Evolutionary Computation

View full text Add to dashboard Cite

Most research on Strip Packing Problems is focused on the single-objective formulation of the problem. However, in this work we deal with a more general and practical variant of the problem, which not only seeks to optimize the usage of the raw material, but also the production process. For the problem solution, we have applied some of the most-known multi-objective evolutionary algorithms, since they have shown a promising behavior when affording multi-objective real-world problems. For an initial implementation, we proposed a solution codification which is based on a complete representation of the pattern layouts. Such an approach was promising but wasn't suitable to afford large instances. For this reason, we have focused on the design of a codification which can be much more competitive when compared to some tailor-made methods. In this sense, we present a hyperheuristic-based codification as an alternative to combine heuristics in such a way that a heuristic's strengths make up for the drawbacks of another. Results demonstrate the advantage of using multi-objective approaches, hyperheuristicbased representations, and of course, the importance on the selection of appropriate solution codifications.

show abstract

Section: Introductionmentioning

confidence: 99%

“…Moreover, the approaches tackling guillotine problems are based on representations where the pattern layouts are explicitly encoded [3], [5]. In this work, we propose a new nondirect codification which involves the usage of widely known low-level heuristics and the principles of hyperheuristics.…”

Section: Introductionmentioning

confidence: 99%

Hyperheuristic codification for the multi-objective 2D Guillotine Strip Packing Problem

Miranda

Armas

Segura

et al. 2010

IEEE Congress on Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…Tiwari and Chakraborti [27] studied the problems dealing with both guillotine and non-guillotine cutting cases. Using a tree-encoded multiobjective genetic algorithm, they were able to obtain globally optimum solutions for some known cases.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Approach to Optimal Packing Using Genetic Algorithm and Coulomb Potential Algorithm

Mahanty

Agrawal

Shrin

et al. 2007

Materials and Manufacturing Processes

View full text Add to dashboard Cite

It is difficult and computationally time-consuming to find the best possible solutions for blank packing problems, because they include a lot of underlying combinational conditions. This paper presents two approaches for packing two-dimensional irregular-shaped polygonal elements-a real-encoded genetic algorithm and a hybrid algorithm using a real-encoded genetic algorithm and a local optimization algorithm. The local optimization algorithm presented is a novel one utilizing the Coulomb potential technique.In the hybrid approach, the real-encoded genetic algorithm generates the order of the polygons while the coulomb potential algorithm determines the embodiment layout under the fixed combinations so as to minimize the scrap. The hybrid genetic algorithm is found to give better results for problems of larger size although it takes more computational time.

show abstract

“…They are namely, sequential heuristic procedures [14,16], linear programming based methods [8,9,15] and meta-heuristics techniques [18,19], especially for the two-dimensional case. Among these different heuristics, it is worth mentioning that application of (GA), [17,21,33] is now ubiquitous in the studies related to materials and manufacturing [3,4,31], (GA) is applied to determine the optimized layout of rectangular parts that are related to metal cutting problems. This paper aims to solve those problems by determining a set of cutting patterns (to obtain the blanks).…”

Section: Introductionmentioning

confidence: 99%

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

Rostom¹,

Nassef

Metwalli

2014

Alexandria Engineering Journal

View full text Add to dashboard Cite

The cutting stock problem (CSP) is one of the significant optimization problems in operations research and has gained a lot of attention for increasing efficiency in industrial engineering, logistics and manufacturing. In this paper, new methodologies for optimally solving the cutting stock problem are presented. A modification is proposed to the existing heuristic methods with a hybrid new 3-D overlapped grouping Genetic Algorithm (GA) for nesting of two-dimensional rectangular shapes. The objective is the minimization of the wastage of the sheet material which leads to maximizing material utilization and the minimization of the setup time. The model and its results are compared with real life case study from a steel workshop in a bus manufacturing factory. The effectiveness of the proposed approach is shown by comparing and shop testing of the optimized cutting schedules. The results reveal its superiority in terms of waste minimization comparing to the current cutting schedules. The whole procedure can be completed in a reasonable amount of time by the developed optimization program.ª 2014 Production and hosting by Elsevier B.V. on behalf

show abstract

Multi-objective optimization of a two-dimensional cutting problem using genetic algorithms

Cited by 31 publications

References 27 publications

Hyperheuristic codification for the multi-objective 2D Guillotine Strip Packing Problem

Hyperheuristic codification for the multi-objective 2D Guillotine Strip Packing Problem

Hybrid Approach to Optimal Packing Using Genetic Algorithm and Coulomb Potential Algorithm

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

Contact Info

Product

Resources

About