[pt] Este trabalho tem como objetivo propor uma metaheurística Iterated Local Search para minimizar o tempo de término ponderado de jobs em problemas de escalonamento de máquinas idênticas paralelas. O objetivo secundário do trabalho é propor uma solução prática para um problema real da companhia estudada em questão. A motivação para o trabalho consiste em uma aplicação prática na indústria de óleo e gás, onde os navios PLSV realizam operações em poços de forma ordenada e visando a antecipação dos poços de petróleo mais produtivos. As características do problema, tais quais: elegibilidade entre navio e operações, tempos de setup relativos à família de atividades, associações entre operações e poços e setups que dependem da chegada de material se adequam a modelagem baseada em escalonamento de máquinas paralelas idênticas. No trabalho uma introdução à respeito do tema é apresentada, em seguida é feita uma revisão da literatura sobre problemas de máquinas paralelas idênticas, a formulação matemática com a descrição do problema é apresentada, a metodologia contemplando representação da solução, heuristica construtiva, vizinhanças exploradas, busca local e Iterated Local Search é exposta, por fim são apresentados os resultados do método e as conclusões sobre o trabalho. / [en] This work objective is to propose an Iterated Local Search metaheuristic to minimize the weighted completion time of jobs on identical parallel machines scheduling problems. The secondary objective of this work is to propose a practical solution to the real problem of the studied company. The motivation of this work consists on a practical application in the Oil and Gas industry, where the PLSV vessels perform ordered operations in a set of wells aiming to maximize the oil production. The characteristics of the problem such as: eligibility between vessels and operations, setup times related to the family of activities, association between operations and wells and setup times depending on the material arrival fit well the identical parallel machine schedule modelling. In this work, an introduction about the theme is presented, followed by a literature review on identical parallel machine scheduling problems, the mathematical formulation with the problem description is presented, the methodology, including the solution representation, constructive heuristic, neighborhood structures, local search and Iterated Local Search is exposed, at last, the method results and conclusions of the work are summarized.

[pt] O transporte escolar é fundamental para garantir o acesso e permanência dos alunos nas escolas públicas, principalmente nas áreas rurais, onde os estudantes estão localizados em uma grande área com baixa densidade e as estradas encontram-se em situações precárias. O presente trabalho tem como objetivo aplicar a metaheurística Iterated Local Search para o roteamento de 13.664 alunos da zona rural do estado do Rio de Janeiro. Para isso, considerouse o problema de roteamento de veículo escolares, do inglês School Bus Routing Problem (SBRP), com frota heterogênea e escola única, com o objetivo de minimizar o custo total considerando as restrições de capacidade dos veículos e distância máxima de percurso. Para aplicação do método, foram considerados os dados fornecidos pela Secretaria de Estado de Educação do Rio de Janeiro (SEEDUC-RJ). Os resultados são apresentados em dois cenários, o primeiro considera os dados de 79 rotas utilizadas pela SEEDUC-RJ para comparação dos resultados obtidos com o ILS. O método mostrou uma redução de 40,5 por cento no custo médio das rotas e 46 or cento na quilometragem média por aluno. O segundo cenário considera o roteamento da totalidade dos alunos, que foram divididos em 506 instâncias considerando escola e turno. A maior instância roteada possui 534 alunos. Os resultados consolidados por município são apresentados e mostram a concentração de municípios com maior custo médio por rota no noroeste fluminense. A implementação das rotas propostas pode trazer economia significativa com as despesas relacionadas ao transporte escolar rural, além de indicar um aumento no nível de serviço para os estudantes, com redução da quilometragem média por aluno. / [en] School transport is essential to ensure access and permanence of students in public schools, especially in rural areas, where students are located in a large area with low density and roads are in precarious conditions. This work aims to apply the Iterated Local Search metaheuristic to route 13.664 rural students in the state of Rio de Janeiro. For this, we considered the School Bus Routing Problem (SBRP), with heterogeneous fleet and single school, in order to minimize the total cost considering the vehicles capacity constraints and maximum travel distance. To apply the method, the data provided by Secretaria de Estado de Educação do Rio de Janeiro (SEEDUCRJ) were considered. The results are presented in two scenarios, the first considers data from 79 routes used by SEEDUC-RJ to compare the results obtained with the ILS. The method showed a reduction of 40.5 percent in the average cost of routes and 46 percent in the average mileage per student. The second scenario considers the routing of all students, who were divided into 506 instances considering school and shift. The largest routed instance has 534 students. The results consolidated by municipality are presented and show the concentration of municipalities with the highest average cost per route in northwestern Rio de Janeiro. The implementation of the proposed routes can bring significant savings with expenses related to rural school transport, in addition to indicating an increase in the level of service for students, with a reduction in the average mileage per student.

Agent-based computing has attracted much attention as a promising technique for application domains that are distributed, complex and heterogeneous. Current research on multi-agent systems (MAS) has become mature enough to be applied as a technology for solving problems in an increasingly wide range of complex applications. The main formal architectures used to describe the relationships between agents in MAS are centralised and distributed architectures. In computational complexity theory, researchers have classified the problems into the followings categories: (i) P problems, (ii) NP problems, (iii) NP-complete problems, and (iv) NP-hard problems. A method for computing the solution to NP-hard problems, using the algorithms and computational power available nowadays in reasonable time frame remains undiscovered. And unfortunately, many practical problems belong to this very class. On the other hand, it is essential that these problems are solved, and the only possibility of doing this is to use approximation techniques. Heuristic solution techniques are an alternative. A heuristic is a strategy that is powerful in general, but not absolutely guaranteed to provide the best (i.e. optimal) solutions or even find a solution. This demands adopting some optimisation techniques such as Evolutionary Algorithms (EA). This research has been undertaken to investigate the feasibility of running computationally intensive algorithms on multi-agent architectures while preserving the ability of small agents to run on small devices, including mobile devices. To achieve this, the present work proposes a new Hybrid Multi-Agent Architecture (HMAA) that generates new heuristics for solving NP-hard problems. This architecture is hybrid because it is "semi-distributed/semi-centralised" architecture where variables and constraints are distributed among small agents exactly as in distributed architectures, but when the small agents become stuck, a centralised control becomes active where the variables are transferred to a super agent, that has a central view of the whole system, and possesses much more computational power and intensive algorithms to generate new heuristics for the small agents, which find optimal solution for the specified problem. This research comes up with the followings: (1) Hybrid Multi-Agent Architecture (HMAA) that generates new heuristic for solving many NP-hard problems. (2) Two frameworks of HMAA have been implemented; search and optimisation frameworks. (3) New SMA meeting scheduling heuristic. (4) New SMA repair strategy for the scheduling process. (5) Small Agent (SMA) that is responsible for meeting scheduling has been developed. (6) “Local Search Programming” (LSP), a new concept for evolutionary approaches, has been introduced. (7) Two types of super-agent (LGP_SUA and LSP_SUA) have been implemented in the HMAA, and two SUAs (local and global optima) have been implemented for each type. (8) A prototype for HMAA has been implemented: this prototype employs the proposed meeting scheduling heuristic with the repair strategy on SMAs, and the four extensive algorithms on SUAs. The results reveal that this architecture is applicable to many different application domains because of its simplicity and efficiency. Its performance was better than many existing meeting scheduling architectures. HMAA can be modified and altered to other types of evolutionary approaches.

Conditional preference networks (CP-nets) exploit the power of ceteris paribus rules to represent preferences over combinatorial decision domains compactly. CP-nets have much appeal. However, their study has not yet advanced sufficiently for their widespread use in real-world applications. Known algorithms for deciding dominance---whether one outcome is better than another with respect to a CP-net---require exponential time. Data for CP-nets are difficult to obtain: human subjects data over combinatorial domains are not readily available, and earlier work on random generation is also problematic. Also, much of the research on CP-nets makes strong, often unrealistic assumptions, such as that decision variables must be binary or that only strict preferences are permitted. In this thesis, I address such limitations to make CP-nets more useful. I show how: to generate CP-nets uniformly randomly; to limit search depth in dominance testing given expectations about sets of CP-nets; and to use local search for learning restricted classes of CP-nets from choice data.

This dissertation explores approaches to the satisfiability problem, focusing on local search methods. The research endeavours to better understand how and why some local search methods are effective. At the root of this understanding are a set of metrics that characterize the behaviour of local search methods. Based on this understanding, two new local search methods are proposed and tested, the first, SDF, demonstrating the value of the insights drawn from the metrics, and the second, ESG, achieving state-of-the-art performance and generalizing the approach to arbitrary 0-1 integer linear programming problems. This generality is demonstrated by applying ESG to combinatorial auction winner determination. Further augmentations to local search are proposed and examined, exploring hybrids that incorporate aspects of backtrack search methods.

M.Sc., Faculty of Sciences, University of the Witwatersrand, 2011 / Abstract This dissertation is concerned with discrete global optimization of nonlinear problems. These problems are constrained and unconstrained and are not easily solvable since there exists multiplicity of local and global minima. In this dissertation, we study the current methods for solving such problems and highlight their ine ciencies. We introduce a new local search procedure. We study the rapidly-exploring random tree (RRT) method, found mostly in the research area of robotics. We then design two global optimization algorithms based on RRT. RRT has never been used in the eld of global optimization. We exploit its attractive properties to develop two new algorithms for solving the discrete nonlinear optimization problems. The rst method is called RRT-Optimizer and is denoted as RRTOpt. RRTOpt is then modi ed to include probabilistic elements within the RRT. We have denoted this method by RRTOptv1. Results are generated for both methods and numerical comparisons are made with a number of recent methods.

A crescente necessidade de buscar operações mais eficientes, com menor custo e mais sustentáveis tem feito com que empresas passassem a procurar oportunidades pelas quais estes objetivos pudessem ser atingidos. Na área de transportes encontrou-se na colaboração uma oportunidade para tal. Este trabalho trata o problema de cobertura rotas com cardinalidade restrita (PCRCR), onde empresas que realizam viagens de carga cheia se unem com o objetivo de reduzir o deslocamento vazio de veículos através da formação de ciclos. É chamado de problema de cardinalidade restrita uma vez que limitamos o número de máximo de viagens no ciclo, o que torna este problema NP-Hard. Existem na literatura duas heurísticas (construtivas) e um modelo por programação linear inteira para a solução deste problema. Este trabalho apresenta uma heurística baseada em um método de busca local que reduziu em média 3,19% os melhores resultados apresentados na literatura. Também são apresentados os tempos de execução de cada um dos algoritmos e a importância de escolher de uma boa solução inicial quando se deseja implantar uma Heurística com Busca Local. / The growing need to seek more efficient, lower cost and more sustainable operations has caused industries to seek opportunities in which these objectives could be achieved. In the area of transportation, collaboration is an opportunity for that. This work deals with the cardinality constrained lane covering problem (CCLCP), where companies who uses full truck loads join efforts in order to reduce empty vehicle travel through closed cycle formation. It is known as cardinality constraint problem as the maximum number of trips in the cycle is limited to an integer number, which makes this problem NP-Hard. There are two heuristics in the literature (constructive) and an integer linear programming model for solving this problem. This work presents a heuristic based on a local search method that reduced an average of 3.19% the better results in the literature. It also presents the execution times of each algorithm and the importance of choosing a good initial solution when you want to create a Local Search Heuristic.

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 2012

The propositional satisfiability (SAT) problem is of considerable theoretical and practical relevance to the artificial intelligence (AI) community and has been used to model many pervasive AI tasks such as default reasoning, diagnosis, planning, image interpretation, and constraint satisfaction. Computational methods for SAT have historically fallen into two broad categories: complete search and local search. Within the local search category, clause weighting methods are amongst the best alternatives for SAT, becoming particularly attractive on problems where a complete search is impractical or where there is a need to find good candidate solutions within a short time. The thesis is concerned with the study of improvements to clause weighting local search methods for SAT. The main contributions are: A component-based framework for the functional analysis of local search methods. A clause weighting local search heuristic that exploits longer-term memory arising from clause weight manipulations. The approach first learns which clauses are globally hardest to satisfy and then uses this information to treat these clauses differentially during weight manipulation [Ferreira Jr and Thornton, 2004]. A study of heuristic tie breaking in the domain of additive clause weighting local search methods, and the introduction of a competitive method that uses heuristic tie breaking instead of the random tie breaking approach used in most existing methods [Ferreira Jr and Thornton, 2005]. An evaluation of backbone guidance for clause weighting local search, and the introduction of backbone guidance to three state-of-the-art clause weighting local search methods [Ferreira Jr, 2006]. A new clause weighting local search method for SAT that successfully exploits synergies between the longer-term memory and tie breaking heuristics developed in the thesis to significantly improve on the performance of current state-of-the-art local search methods for SAT-encoded instances containing identifiable CSP structure. Portions of this thesis have appeared in the following refereed publications: Longer-term memory in clause weighting local search for SAT. In Proceedings of the 17th Australian Joint Conference on Artificial Intelligence, volume 3339 of Lecture Notes in Artificial Intelligence, pages 730-741, Cairns, Australia, 2004. Tie breaking in clause weighting local search for SAT. In Proceedings of the 18th Australian Joint Conference on Artificial Intelligence, volume 3809 of Lecture Notes in Artificial Intelligence, pages 70–81, Sydney, Australia, 2005. Backbone guided dynamic local search for propositional satisfiability. In Proceedings of the Ninth International Symposium on Artificial Intelligence and Mathematics, AI&M, Fort Lauderdale, Florida, 2006.

Recent research has shown that it is often preferable to encode real-world problems as propositional satisfiability (SAT) problems and then solve using a general purpose SAT solver. However, much of the valuable information and structure of these realistic problems is flattened out and hidden inside the corresponding Conjunctive Normal Form (CNF) encodings of the SAT domain. Recently, systematic SAT solvers have been progressively improved and are now able to solve many highly structured practical problems containing millions of clauses. In contrast, state-of-the-art Stochastic Local Search (SLS) solvers still have difficulty in solving structured problems, apparently because they are unable to exploit hidden structure as well as the systematic solvers. In this thesis, we study and evaluate different ways to effectively recognise, model and efficiently exploit useful structures hidden in realistic problems. A summary of the main contributions is as follows: 1. We first investigate an off-line processing phase that applies resolution-based pre-processors to input formulas before running SLS solvers on these problems. We report an extensive empirical examination of the impact of SAT pre-processing on the performance of contemporary SLS techniques. It emerges that while all the solvers examined do indeed benefit from pre-processing, the effects of different pre-processors are far from uniform across solvers and across problems. Our results suggest that SLS solvers need to be equipped with multiple pre-processors if they are ever to match the performance of systematic solvers on highly structured problems. [Part of this study was published at the AAAI-05 conference]. 2. We then look at potential approaches to bridging the gap between SAT and constraint satisfaction problem (CSP) formalisms. One approach has been to develop a many-valued SAT formalism (MV-SAT) as an intermediate paradigm between SAT and CSP, and then to translate existing highly efficient SAT solvers to the MV-SAT domain. In this study, we follow a different route, developing SAT solvers that can automatically recognise CSP structure hidden in SAT encodings. This allows us to look more closely at how constraint weighting can be implemented in the SAT and CSP domains. Our experimental results show that a SAT-based mechanism to handle weights, together with a CSP-based method to instantiate variables, is superior to other combinations of SAT and CSP-based approaches. In addition, SLS solvers based on this many-valued weighting approach outperform other existing approaches to handle many-valued CSP structures. [Part of this study was published at the AAAI-05 conference]. 3. Finally, we propose and evaluate six different schemes to encode temporal reasoning problems, in particular the Interval Algebra (IA) networks, into SAT CNF formulas. We then empirically examine the performance of local search as well as systematic solvers on the new temporal SAT representations, in comparison with solvers that operate on native IA representations. Our empirical results show that zChaff (a state-of-the-art complete SAT solver) together with the best IA-to-SAT encoding scheme, can solve temporal problems significantly faster than existing IA solvers working on the equivalent native IA networks. [Part of this study was published at the CP-05 workshop].