A genetic programming hyper-heuristic approach for evolving 2-D strip packing heuristics

We present a genetic programming (GP) system to evolve reusable heuristics for the 2-D strip packing problem. The evolved heuristics are constructive, and decide both which piece to pack next and where to place that piece, given the current partial solution. This paper contributes to a growing research area that represents a paradigm shift in search methodologies. Instead of using evolutionary computation to search a space of solutions, we employ it to search a space of heuristics for the problem. A key motivation is to investigate methods to automate the heuristic design process. It has been stated in the literature that humans are very good at identifying good building blocks for solution methods. However, the task of intelligently searching through all of the potential combinations of these components is better suited to a computer. With such tools at their disposal, heuristic designers are then free to commit more of their time to the creative process of determining good components, while the computer takes on some of the design process by intelligently combining these components. This paper shows that a GP hyper-heuristic can be employed to automatically generate human competitive heuristics in a very-well studied problem domain

A Parallel Hyper-heuristic Approach for the Two-dimensional Rectangular Strip-packing Problem

In this paper, we present a parallel hyper-heuristic approach for two-dimensional rectangular strip-packing problems (2DSP). This is an island model with a special master-slave structure, and in all the islands we run a memetic algorithm-based hyper-heuristic (HH). The basic technique of this HH is a memory-based evolutionary technique, the “extended virtual loser” (EVL). The memory-based technique memorises the past events, e.g., past successes of the evolutionary process or bad values of the variables; thus, we can influence the operations of the evolutionary algorithms using thismemory. The EVL technique learns the bad values of the variables based on the worst solutions of the population and computes probabilities to control the mutation steps. With the help of the EVL technique, we can use a mutation-omitting recombination operator and obtain a learning mechanism for the selection of heuristics. In the HH, the selection of the low-level heuristics is modified with mutations based on the EVL technique using a local search. The island model achieved good performance. The test instances show that the proposed algorithm is efficient for the rectangular strip-packing problem

Multi-objective strip packing using an evolutionary algorithm

Good algorithms exist for solving the strip packing problem when the objective is to minimise the amount of wasted material. We describe a multi-objective evolutionary algorithm for strip packing (MOSP) that optimises not only for wastage, but also for the operating speed of the cutting equipment, by minimising the number of independent cuts required by a packing. We show that MOSP returns a set of packings offering a range of trade-offs between the two objectives, and also that, by using heuristics that consider cuts, it derives packings with wastage levels that are better than most previously-published algorithms that optimise for wastage alone

A Hybrid Demon Algorithm for the Two-Dimensional Orthogonal Strip Packing Problem

This paper develops a hybrid demon algorithm for a two-dimensional orthogonal strip packing problem. This algorithm combines a placement procedure based on an improved heuristic, local search, and demon algorithm involved in setting one parameter. The hybrid algorithm is tested on a wide set of benchmark instances taken from the literature and compared with other well-known algorithms. The computation results validate the quality of the solutions and the effectiveness of the proposed algorithm

Approximation algorithms for the strip packing problem with unloading constraints

Orientadores: Eduardo Candido Xavier, Flávio Keidi MiyazawaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Neste trabalho estudamos problemas de empacotamento com restrições de descarregamento considerados NP-difíceis. Estes problemas possuem aplicações nas áreas de logística e roteamento. Assumindo a hipótese de que P ? NP, sabemos que não existem algoritmos eficientes para resolver tais problemas. Uma das abordagens consideradas para tratar tais problemas é a de algoritmos de aproximação, que são algoritmos eficientes (complexidade de tempo polinomial) e que geram soluções com garantia de qualidade. Estudamos técnicas para o desenvolvimento de algoritmos aproximados e também alguns algoritmos para problemas de empacotamento online que podem ser utilizados na resolução do problema estudado. Propomos também algumas heurísticas para o problema e, além disto, provamos que duas destas heurísticas possuem garantias de aproximação com fatores constantes. Realizamos testes computacionais com estes algoritmos propostos. Dentre estes, a heurística GRASP foi a que obteve melhores resultados para as instâncias de teste consideradasAbstract: In this work we study some NP-hard packing problems with unloading constraints. These problems have applications in logistics and routing problems. Assuming P ? NP, there are no efficient algorithms to solve these problems. On way to deal with these problems is using approximation algorithms, that are efficient algorithms (polynomial time complexity) that produce solutions with quality guarantee. We study techniques used in the development of approximation algorithms and some algorithms for online packing problems which can be used to solve the considered problem. We propose some heuristics for the problem and prove that two of them have constant approximation guarantees. We also perform computational tests with the proposed algorithms. Among them, the GRASP heuristic achieved the best results on the considered instancesMestradoTeoria da ComputaçãoMestre em Ciência da Computaçã

A genetic programming hyper-heuristic approach to automated packing

This thesis presents a programme of research which investigated a genetic programming hyper-heuristic methodology to automate the heuristic design process for one, two and three dimensional packing problems. Traditionally, heuristic search methodologies operate on a space of potential solutions to a problem. In contrast, a hyper-heuristic is a heuristic which searches a space of heuristics, rather than a solution space directly. The majority of hyper-heuristic research papers, so far, have involved selecting a heuristic, or sequence of heuristics, from a set pre-defined by the practitioner. Less well studied are hyper-heuristics which can create new heuristics, from a set of potential components. This thesis presents a genetic programming hyper-heuristic which makes it possible to automatically generate heuristics for a wide variety of packing problems. The genetic programming algorithm creates heuristics by intelligently combining components. The evolved heuristics are shown to be highly competitive with human created heuristics. The methodology is first applied to one dimensional bin packing, where the evolved heuristics are analysed to determine their quality, specialisation, robustness, and scalability. Importantly, it is shown that these heuristics are able to be reused on unseen problems. The methodology is then applied to the two dimensional packing problem to determine if automatic heuristic generation is possible for this domain. The three dimensional bin packing and knapsack problems are then addressed. It is shown that the genetic programming hyper-heuristic methodology can evolve human competitive heuristics, for the one, two, and three dimensional cases of both of these problems. No change of parameters or code is required between runs. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains