A Hybrid Improvement Heuristic for the One-Dimensional Bin Packing Problem

Alvim, Adriana C. F.; Ribeiro, Celso C.; Glover, Fred; Aloise, Dario José

doi:10.1023/b:heur.0000026267.44673.ed

Cited by 115 publications

(104 citation statements)

References 24 publications

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“…Compared with the current best bin packing heuristic approach HP_BP (Alvim et al 2004), the simulated annealing hyper-heuristic is slightly worse in terms of solution quality (see table 6). In fact, HP_BP performed excellently and achieved around 1581 optimal solutions out of 1587 problem instances.…”

Section: Computational Resultsmentioning

confidence: 96%

“…Tables 5 and 6 respectively present a comparison of our simulated annealing hyper-heuristic with a hybrid variable neighbourhood search algorithm (Perturbation MBS' + VNS) (Fleszar and Hindi 2002) and a recently proposed hybrid procedure (HP_BP) in (Alvim et al 2004) (the computational results of Perturbation MBS' + VNS on the third group of data sets (Sch_Wae1, Sch_Wae2, Wae_Gau1) are not available in the literature). It can be seen that our simulated annealing hyper-heuristic produced similar results to the hybrid VNS in the first group of data sets and slightly better results in the second group of data sets in terms of solution quality.…”

Section: Computational Resultsmentioning

confidence: 99%

“…General local search approaches cannot be well guided by this objective function (Falkenauer 1998). Recently, Alvim et al (Alvim et al 2004) proposed a hybrid algorithm which combines several strategies. Superior results were obtained by the algorithm compared with other existing heuristic approaches.…”

Section: The Bin Packing Problemmentioning

confidence: 99%

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A simulated annealing hyper-heuristic methodology for flexible decision support

et al. 2011

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Abstract. One of the main motivations for investigating hyper-heuristic methodologies is to provide a more general search framework than is currently available. Most of the current search techniques represent approaches that are largely adapted for specific search problems (and, in some cases, even specific problem instances). There are many real-world scenarios where the development of such bespoke systems is entirely appropriate. However, there are other situations where it would be beneficial to have methodologies which are more generally applicable to more problems. One of our motivating goals is to underpin the development of more flexible search methodologies that can be easily and automatically employed on a broader range of problems than is currently possible. Almost all the heuristics that have appeared in the literature have been designed and selected by humans. In this paper, we investigate a simulated annealing hyper-heuristic methodology which operates on a search space of heuristics and which employs a stochastic heuristic selection strategy and a short-term memory. The generality and performance of the proposed algorithm is demonstrated over a large number of benchmark data sets drawn from three very different and difficult (NP-hard) problems: nurse rostering, university course timetabling and onedimensional bin packing. Experimental results show that the proposed hyper-heuristic is able to achieve significant performance improvements over a recently proposed tabu search hyper-heuristic without lowering the level of generality. We also show that our hyper-heuristic is capable of producing competitive results against bespoke meta-heuristics methods for these problems. In some cases, the simulated annealing hyper-heuristic has even obtained considerable improvements over some of the current best problem-specific meta-heuristic approaches. The contribution of this paper is to present a method which can be readily (and automatically) applied to very different problems whilst still being able to produce results on benchmark problems which are competitive with bespoke human designed tailor made algorithms for those problems.

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Section: Computational Resultsmentioning

confidence: 96%

Section: Computational Resultsmentioning

confidence: 99%

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A simulated annealing hyper-heuristic methodology for flexible decision support

et al. 2011

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“…The application of MBS' followed by a variable neighbourhood search proved an effective combination. Alvim et al (2004) also suggested the use of a hybrid heuristic that incorporates many different techniques. Their approach, HI BP, is probably the most complex one, but it also produces the best results of all reviewed techniques (see Table 2).…”

Section: Related Workmentioning

confidence: 99%

Nature inspired genetic algorithms for hard packing problems

Rohlfshagen

Bullinaria

2008

Ann Oper Res

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This paper presents two novel genetic algorithms (GAs) for hard industrially relevant packing problems. The design of both algorithms is inspired by aspects of molecular genetics, in particular, the modular exon-intron structure of eukaryotic genes. Two representative packing problems are used to test the utility of the proposed approach: the bin packing problem (BPP) and the multiple knapsack problem (MKP). The algorithm for the BPP, the exon shuffling GA (ESGA), is a steady-state GA with a sophisticated crossover operator that makes maximum use of the principle of natural selection to evolve feasible solutions with no explicit verification of constraint violations. The second algorithm, the Exonic GA (ExGA), implements an RNA inspired adaptive repair function necessary for the highly constrained MKP. Three different variants of this algorithm are presented and compared, which evolve a partial ordering of items using a segmented encoding that is utilised in the repair of infeasible solutions. All algorithms are tested on a range of benchmark problems, and the results indicate a very high degree of accuracy and reliability compared to other approaches in the literature.

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“…In most published works, a classical encoding scheme S is used where S(i) is the index of the bin where item i is placed. [8] proposes a hybrid metaheuristic based on tabu search. [9] proposes a hybrid grouping genetic algorithm which uses the heuristic F F D [1] and the dominance criterion from [10].…”

Section: State Of the Artmentioning

confidence: 99%

An Analogy between Bin Packing Problem and Permutation Problem: A New Encoding Scheme

Gourgand

Grangeon

Klement

2014

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Part 2: Knowledge Discovery and SharingInternational audienceThe bin packing problem aims to pack a set of items in a minimum number of bins, with respect to the size of the items and capacity of the bins. This is an NP-hard problem. Several approach methods have been developed to solve this problem. In this paper, we propose a new encoding scheme which is used in a hybrid resolution: a metaheuristic is matched with a list algorithm (Next Fit, First Fit, Best Fit) to solve the bin packing problem. Any metaheuristic can be used but in this paper, our proposition is implemented on a single solution based metaheuristic (stochastic descent, simulated annealing, kangaroo algorithm). This hybrid method is tested on literature instances to ensure its good results

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A Hybrid Improvement Heuristic for the One-Dimensional Bin Packing Problem

Cited by 115 publications

References 24 publications

A simulated annealing hyper-heuristic methodology for flexible decision support

A simulated annealing hyper-heuristic methodology for flexible decision support

Nature inspired genetic algorithms for hard packing problems

An Analogy between Bin Packing Problem and Permutation Problem: A New Encoding Scheme

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