Bison: A fast hybrid procedure for exactly solving the one-dimensional bin packing problem

Schöll, Armin; Klein, Robert; Jürgens, Christian

doi:10.1016/s0305-0548(96)00082-2

Cited by 236 publications

(170 citation statements)

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“…Some stronger lower bounds were studied in Toth 1990b, Scholl, Klein andJurgens 1997). Considerable research has been carried out on both exact methods and heuristic based 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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“…Some stronger lower bounds were studied in Toth 1990b, Scholl, Klein andJurgens 1997). Considerable research has been carried out on both exact methods and heuristic based approaches.…”

Section: The Bin Packing Problemmentioning

confidence: 99%

“…This is a branch and bound based exact method originally proposed in (Martello and Toth 1990b). Some other versions of exact methods are proposed in (Scholl, Klein andJurgens 1997, Belov andScheithauer 2006). Heuristic based approaches have also been studied.…”

Section: The Bin Packing Problemmentioning

confidence: 99%

A simulated annealing hyper-heuristic methodology for flexible decision support

et al. 2011

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“…The third section presents the test sample selection and the results of the computing experiments. It embraces all problem classes described in the paper by Schwerin & Wäscher (1997) and compares the above results with those of MTPCS, Schwerin & Wäscher (1998), and those of BISON, Scholl et al (1997). An outlook on further possible developments is presented in the Conclusion.…”

Section: Introductionmentioning

confidence: 78%

“…One of the sets, data set 2, coincides with that considered in our paper. However, BISON has been tested by the authors according to a scheme different from the classification by Schwerin & Wäscher (1997, 1998 Table 5, built according to the type of Table A3, Scholl et al (1997). The quality index is the number of problems solved to optimum out of 10 (#opt) with the help of algorithms BISON, MBB and SVC.…”

Section: Comparison With Bison Packingmentioning

confidence: 99%

Linear one-dimensional cutting-packing problems: numerical experiments with the sequential value correction method (SVC) and a modified branch-and-bound method (MBB)

et al. 2000

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Two algorithms for the one-dimensional cutting problem, namely, a modified branch-and-bound method (exact method) and a heuristic sequential value correction method are suggested. In order to obtain a reliable assessment of the efficiency of the algorithms, hard instances of the problem were considered and from the computational experiment it seems that the efficiency of the heuristic method appears to be superior to that of the exact one, taking into account the computing time of the latter. A detailed description of the two methods is given along with suggestions for their improvements.

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“…In our case we permit discarding those items which cannot "t and we assume duplication within each bin to be subject to a (side) constraint. Our problem also has connections with cutting stock (trim) and knapsack problems which have been extensively researched ( [3,4]). …”

Section: Introductionmentioning

confidence: 99%