Sparse, Continuous Policy Representations for Uniform Online Bin Packing via Regression of Interpolants

Drake, John H.; Swan, Jerry; Neumann, Geoffrey; Özcan, Ender

doi:10.1007/978-3-319-55453-2_13

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…The Bin Packing Problem (BPP) consists of packing a set of items by minimizing the number of bins used [54], [55]. In general, the BPP is an exciting problem since many other optimization problems such as the cutting stock problem [56] and the knapsack problem [57] can be modeled as BPPs [58].…”

Section: ) Bin Packingmentioning

confidence: 99%

A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems

et al. 2020

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Hyper-heuristics aim at interchanging different solvers while solving a problem. The idea is to determine the best approach for solving a problem at its current state. This way, every time we make a move it gets us closer to a solution. The problem changes; so does its state. As a consequence, for the next move, a different solver may be invoked. Hyper-heuristics have been around for almost 20 years. However, combinatorial optimization problems date from way back. Thus, it is paramount to determine whether the efforts revolving around hyper-heuristic research have been targeted at the problems of the highest interest for the combinatorial optimization community. In this work, we tackle such an endeavor. We begin by determining the most relevant combinatorial optimization problems, and then we analyze them in the context of hyper-heuristics. The idea is to verify whether they remain as relevant when considering exclusively works related to hyper-heuristics. We find that some of the most relevant problem domains have also been popular for hyper-heuristics research. Alas, others have not and few efforts have been directed towards solving them. We identify the following problem domains, which may help in furthering the impact of hyper-heuristics: Shortest Path, Set Cover, Longest Path, and Minimum Spanning Tree. We believe that focusing research on ways for solving them may lead to an increase in the relevance and impact that hyperheuristics have on combinatorial optimization problems.

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Section: ) Bin Packingmentioning

confidence: 99%

A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems

et al. 2020

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“…In these approaches, a heuristic function is evolved as a GP syntax tree. However, more recently, both a look-up table (referred to as a "matrix") [29,30] and function interpolation [31] have also been used to represent a heuristic function. Other metaheuristics have been automatically designed using hyper-heuristics, such as particle swarm optimisation [32] and variable neighbourhood search [33].…”

Section: Automated Design Using Hyper-heuristicsmentioning

confidence: 99%

A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming

Hong

Drake

Woodward

et al. 2018

Applied Soft Computing

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Evolutionary programming can solve black-box function optimisation problems by evolving a population of numerical vectors. The variation component in the evolutionary process is supplied by a mutation operator, which is typically a Gaussian, Cauchy, or Lévy probability distribution. In this paper, we use genetic programming to automatically generate mutation operators for an evolutionary programming system, testing the proposed approach over a set of function classes, which represent a source of functions. The empirical results over a set of benchmark function classes illustrate that genetic programming can evolve mutation operators which generalise well from the training set to the test set on each function class. The proposed method is able to outperform existing human designed mutation operators with statistical significance in most cases, with competitive results observed for the rest.

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“…In order to test solutions for online bin packing problems with uniform distribution, specific problem sets are proposed by Asta et al and Ender et al [29,30]. The generator functions for these datasets were used by several other studies for benchmarking state-ofthe-art 3DBPP online algorithms [5,29,31].…”

Section: Introductionmentioning

confidence: 99%

A Software Toolbox for Realistic Dataset Generation for Testing Online and Offline 3D Bin Packing Algorithms

Ribeiro

Ananno

2023

Processes

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Packing products into a pallet or other medium is an unavoidable activity for producing companies. In many cases, packing is based on operator experience and training using packing patterns that have worked before. Automated packing, on the other hand, requires a systematic procedure for devising packing solutions. In the scientific literature, this problem is known as 3D bin packing (3DBP) and many authors have proposed exact and heuristic solutions for many variations of the problem. There is, however, a lack of datasets that can be used to test and validate such solutions. Many of the available datasets use randomly generated products with extremely limited connection to real practice. Furthermore, they contain a reduced number of product configurations and ignore that packing relates to customers’ orders, which have specific relative mixes of products. This paper proposes a software toolbox for generating arbitrarily large datasets for 3DBPP based on real industry data. The toolbox was developed in connection with the analysis of a real dataset from the food and beverages sector, which enabled the creation of several synthetic datasets. The toolbox and the synthetic datasets are publicly available and can be used to generate additional data for testing and validating 3DBP solutions. The industry is increasingly becoming data dependent and driven. The ability to generate good quality synthetic data to support the development of solutions to real industry problems is of extreme importance. This work is a step in that direction in a domain where open data are scarce.

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Sparse, Continuous Policy Representations for Uniform Online Bin Packing via Regression of Interpolants

Cited by 6 publications

References 23 publications

A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems

A Systematic Review of Hyper-Heuristics on Combinatorial Optimization Problems

A hyper-heuristic approach to automated generation of mutation operators for evolutionary programming

A Software Toolbox for Realistic Dataset Generation for Testing Online and Offline 3D Bin Packing Algorithms

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