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Approximating Multi-Objective Hyper-Heuristics for Solving 2D Irregular Cutting Stock Problems

  • Juan Carlos Gomez
  • Hugo Terashima-Marín
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6438)

Abstract

This article presents a method based on the multi-objective evolutionary algorithm NSGA-II to approximate hyper-heuristics for solving irregular 2D cutting stock problems under multiple objectives. In this case, additionally to the traditional objective of minimizing the number of sheets used to fit a finite number of irregular pieces, the time required to perform the placement task is also minimized, leading to a bi-objective minimization problem with a tradeoff between the number of sheets and the time required for placing all pieces. We solve this problem using multi-objective hyper-heuristics (MOHHs), whose main idea consists of finding a set of simple heuristics which can be combined to find a general solution for a wide range of problems, where a single heuristic is applied depending on the current condition of the problem, instead of applying a unique single heuristic during the whole placement process. The MOHHs are approximated after going through a learning process by mean of the NSGA-II, which evolves combinations of condition-action rules producing at the end a set of Pareto-optimal MOHHs. We tested the approximated MMOHHs on several sets of benchmark problems, having outstanding results for most of the cases.

Keywords

Hiper-Heuristics Multi-Objective Optimization Cutting Evolutionary Computation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Juan Carlos Gomez
    • 1
  • Hugo Terashima-Marín
    • 1
  1. 1.Tecnológico de MonterreyMonterreyMéxico

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