Auto-parametrização de meta-heurísticas para escalonamento dinâmico

Este artigo aborda o problema da parametrização de Técnicas de Optimização Inspiradas na Biologia (BIT - Biological Inspired Optimization Techniques), também conhecidas como Meta-heurísticas, considerando a importância que estas técnicas têm na resolução de situações de mundo real, sujeitas a perturbações externas. É proposto um módulo de aprendizagem com o objectivo de permitir que um Sistema Multi-Agente (SMA) para Escalonamento seleccione automaticamente uma Metaheurística e escolha a parametrização a usar no processo de optimização. Para o módulo de aprendizagem foi usado o Raciocínio baseado em Casos (RBC), permitindo ao sistema aprender a partir da experiência acumulada na resolução de problemas similares. Através da análise dos resultados obtidos é possível concluir acerca das vantagens da sua utilização

Exact/heuristic hybrids using rVNS and hyperheuristics for workforce scheduling

In this paper we study a complex real-world workforce scheduling problem. We propose a method of splitting the problem into smaller parts and solving each part using exhaustive search. These smaller parts comprise a combination of choosing a method to select a task to be scheduled and a method to allocate resources, including time, to the selected task. We use reduced Variable Neighbourhood Search (rVNS) and hyperheuristic approaches to decide which sub problems to tackle. The resulting methods are compared to local search and Genetic Algorithm approaches. Parallelisation is used to perform nearly one CPU-year of experiments. The results show that the new methods can produce results fitter than the Genetic Algorithm in less time and that they are far superior to any of their component techniques. The method used to split up the problem is generalisable and could be applied to a wide range of optimisation problems

Knowledge discovery in hyper-heuristic using case-based reasoning on course timetabling

This paper presents a new hyper-heuristic method using Case-Based Reasoning (CBR) for solving course timetabling problems. The term Hyper-heuristics has recently been employed to refer to 'heuristics that choose heuristics' rather than heuristics that operate directly on given problems. One of the overriding motivations of hyper-heuristic methods is the attempt to develop techniques that can operate with greater generality than is currently possible. The basic idea behind this is that we maintain a case base of information about the most successful heuristics for a range of previous timetabling problems to predict the best heuristic for the new problem in hand using the previous knowledge. Knowledge discovery techniques are used to carry out the training on the CBR system to improve the system performance on the prediction. Initial results presented in this paper are good and we conclude by discussing the con-siderable promise for future work in this area

The role of different crossover methods when solving the open shop scheduling problem via a simple evolutionary approach

The Open Shop Scheduling Problem (OSSP) is one of the most interesting, complexes and not frequently approached scheduling problems. Due to its intractability with other techniques, in this work we present an evolutionary approach to provide approximate solutions. One of the most important points in an Evolutionary Algorithm is to determine how to represent individuals of the evolving population and then to decide suitable genetic operators. In this work, we use permutations as chromosomes. Dealing with permutations requires appropriate crossover operators to ensure feasible offspring. Usual operators are partially-mapped, order, cycle and onecut- point crossover. The goal is to determine which is the most adequate for facing the OSSP with a simple evolutionary algorithm. Several known instances have been considered for testing in order to evaluate the algorithm behavior.Eje: Sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

A Classification of Hyper-heuristic Approaches

The current state of the art in hyper-heuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present and overview of previous categorisations of hyper-heuristics and provide a unified classification and definition which captures the work that is being undertaken in this field. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail. Our goal is to both clarify the main features of existing techniques and to suggest new directions for hyper-heuristic research

Diseño e implementación de un algoritmo para dar solución al problema de asignación de salones (Timetabling) usando el método de colonia de hormigas

En muy corto tiempo el software es posiblemente uno de los productos de la Ingeniería que más se ha transformado, evolucionando desde el software empírico, hasta llegar al desarrollo de aplicaciones bajo los principios y herramientas de la Ingeniería del software, aun así, cuando se ha tratado de mantener en regla dichos principios, aquellos encargados de su elaboración se han visto obligados a enfrentar una serie de problemas muy comunes gracias a la gran exigencia en la capacidad de resultados de los programas y a diferentes requerimientos que con el pasar del tiempo se vuelven aun más grandes debido al cambio de las condiciones de uso, instalación, plataformas, tiempos, hardware, etc. lo que aumenta su complejidad y con el transcurso de los días su obsolescencia. Gracias al rápido avance tecnológico de la información, la cantidad y la complejidad del software se ha acrecentado de una forma considerable, así como también han aumentado los requerimientos en su funcionalidad, confiabilidad y por lo tanto su seguridad, intentando con esto reconocer requisitos incompletos, ambiguos o contradictorios; de esta manera la calidad y la productividad se están transformando en las mayores preocupaciones para los desarrolladores del software. Uno de los problemas en los que la computación de alto desempeño puede mejorar los tiempos de ejecución así como también la forma de implementar un algoritmo determinado es la programación de clases con aulas y horarios adecuados a las necesidades de una institución educativa; problema al que se llamara de ahora en adelante Asignación de Aulas y Horarios, el cual se intentará resolver usando Colonia de Hormigas como metodología heurística para la búsqueda de soluciones. La Asignación de Aulas y Horarios consiste en relacionar un grupo de profesores a un grupo de materias dentro de un período de tiempo fijo, generalmente una semana, satisfaciendo un grupo de restricciones de diferente tipo; encontrar una solución de forma

A Promising Hybrid GA/Heuristic Approach for Open-Shop Scheduling Problems

. Many problems in industry are a form of openshop scheduling problem (OSSP). We describe a hybrid approach to this problem which combines a Genetic Algorithm (GA) with simple heuristic schedule building rules. Excellent performance is found on some benchmark OSS problems, including improvements on previous best-known results. We describe how our approach can be simply amended to deal with the more complex style of open shop scheduling problems which occur in industry, and discuss issues relating to further improvement of performance and integration of the approach into industrial job shop environments. 1 INTRODUCTION The Open-Shop Scheduling Problem (OSSP) is a complex and common industrial problem [6]. OSSPs arise in an environment where there is a collection of operations to perform on one or more machines. Efficient production and manufacturing demands effective methods to optimise various aspects of a schedule, usually focussing on the total time taken to process all of the oper..

A Promising Hybrid GA/Heuristic Approach for Open-Shop Scheduling Problems

. Many problems in industry are a form of openshop scheduling problem (OSSP). We describe a hybrid approach to this problem which combines a Genetic Algorithm (GA) with simple heuristic schedule building rules. Excellent performance is found on some benchmark OSS problems, including improvements on previous best-known results. We describe how our approach can be simply amended to deal with the more complex style of open shop scheduling problems which occur in industry, and discuss issues relating to further improvement of performance and integration of the approach into industrial job shop environments. 1 INTRODUCTION The Open-Shop Scheduling Problem (OSSP) is a complex and common industrial problem [6]. OSSPs arise in an environment where there is a collection of operations to perform on one or more machines. Efficient production and manufacturing demands effective methods to optimise various aspects of a schedule, usually focussing on the total time taken to process all of the oper..

A cross-domain multi-armed bandit hyper-heuristic

Orientadora : Profª. Drª. Aurora PozoCo-orientador : Prof. Dr. Richard Aderbal GonçalvesDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa: Curitiba, 26/02/2016Inclui referências : f. 64-70Resumo: Muitos problemas de otimização do mundo real são complexos e possuem muitas variáveis e restrições. Por esta causa, o uso de meta-heurísticas tornou-se a principal maneira de resolver problemas com essas características. Uma das principais desvantagens do uso de meta-heurísticas e que são geralmente desenvolvidas utilizando características do domínio fazendo com que sejam atreladas a ele dificultando sua utilização em outros problemas. Em buscas de algoritmos mais adaptáveis o conceito de hiper-heurísticas surgiu. Hiper- heurísticas são métodos de busca que visam solucionar problemas de otimização selecionando ou gerando heurísticas. Hiper-heurísticas de seleção escolhem uma boa heurística para ser aplicada a partir de um conjunto de heurísticas. O método de seleção e a principal peca de uma hiper-heurística de seleção tendo impacto fundamental em sua performance. Apesar de existirem vários trabalhos sobre hiper-heurísticas de seleção, ainda não existe consenso sobre como uma boa estratégia de seleção deve ser definida. Em busca de uma estratégia de seleção, algoritmos inspirados nos conceitos do problema Multi-Armed Bandit (MAB) serão estudados. Estes algoritmos foram aplicados ao contexto da Seleção Adaptativa de Operadores obtendo resultados promissores. Entretanto, ainda existem poucas abordagens para o contexto de hiper-heurísticas. Nesta dissertação propomos uma hiper-heurística que utiliza algoritmos MAB como sua estratégia de seleção. A abordagem proposta e desenvolvida utilizando o framework HyFlex, que foi proposto para facilitar a implementação e comparação de novas Hiper- heurísticas. Os parâmetros foram configurados através de um estudo empírico, e a melhor configuração encontrada foi comparada com os 10 primeiros colocados da competição CHeSC 2011. Os resultados obtidos foram bons e comparáveis com os das melhores abordagens da literatura. O algoritmo proposto alcançou a quarta colocação. Apesar dos bons resultados, os experimentos demonstram que a abordagem proposta sofre grande influencia dos parâmetros. Trabalhos futuros irão investigar formas de amenizar esta influência.Abstract: Many real word optimization problems are very complex with many variables and constraints, and cannot be solved by exact methods in a reasonable computational time. As an alternative, meta-heuristics emerged as an efficient way to solve this type of problems even though they cannot ensure optimal values. The main issue of meta-heuristics is that they are built using domain-specific knowledge, therefore they require a great effort to be used in a new domain. In order to solve this problem, the concept of Hyper-heuristics were proposed. Hyper-heuristics are search methods that aim to solve optimization problems by selecting or generating heuristics. Selection hyper-heuristics choose from a pool of heuristics a good one to be applied at the current stage of the optimization process. The selection mechanism is the main part of a selection hyper-heuristic and has a great impact on its performance. Although there are several works focused on selection hyperheuristics, there is no unanimity about which is the best way to define a selection strategy. In this dissertation, a deterministic selection strategy based on the concepts of the MultiArmed Bandit (MAB) problem is proposed to cross-domain optimization. Multi-armed bandit approaches define a selection function with two components, the first is based on the performance of an operator and the second based on the number of times that the operator was used. These approaches had showed a promising performance over the Adaptive Operator Selection context. However, there are few works on literature that aim the hyper-heuristic context, as proposed here. The proposed approach is integrated into the HyFlex framework, that was developed to facilitate the implementation and comparison of hyper-heuristics. An empirical parameter configuration was performed and the best setup was compared to the top ten CHeSC 2011 algorithms using the same methodology adopted during the competition. The results obtained were good comparable to those attained by the literature. Moreover, it was concluded that the behavior of MAB selection is heavily affected by its parameters. As this is not a desirable behavior to hyper-heuristics, future research will investigate ways to better deal with the parameter setting

An intelligent manufacturing system for heat treatment scheduling

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This research is focused on the integration problem of process planning and scheduling in steel heat treatment operations environment using artificial intelligent techniques that are capable of dealing with such problems. This work addresses the issues involved in developing a suitable methodology for scheduling heat treatment operations of steel. Several intelligent algorithms have been developed for these propose namely, Genetic Algorithm (GA), Sexual Genetic Algorithm (SGA), Genetic Algorithm with Chromosome differentiation (GACD), Age Genetic Algorithm (AGA), and Mimetic Genetic Algorithm (MGA). These algorithms have been employed to develop an efficient intelligent algorithm using Algorithm Portfolio methodology. After that all the algorithms have been tested on two types of scheduling benchmarks. To apply these algorithms on heat treatment scheduling, a furnace model is developed for optimisation proposes. Furthermore, a system that is capable of selecting the optimal heat treatment regime is developed so the required metal properties can be achieved with the least energy consumption and the shortest time using Neuro-Fuzzy (NF) and Particle Swarm Optimisation (PSO) methodologies. Based on this system, PSO is used to optimise the heat treatment process by selecting different heat treatment conditions. The selected conditions are evaluated so the best selection can be identified. This work addresses the issues involved in developing a suitable methodology for developing an NF system and PSO for mechanical properties of the steel. Using the optimisers, furnace model and heat treatment system model, the intelligent system model is developed and implemented successfully. The results of this system were exciting and the optimisers were working correctly