Variable Reduction Strategy Integrated Variable Neighborhood Search and NSGA-II Hybrid Algorithm for Emergency Material Scheduling

Developing a reasonable and efficient emergency material scheduling plan is of great significance to decreasing casualties and property losses. Real-world emergency material scheduling (EMS) problems are typically large-scale and possess complex constraints. An evolutionary algorithm (EA) is one of the effective methods for solving EMS problems. However, the existing EAs still face great challenges when dealing with large-scale EMS problems or EMS problems with equality constraints. To handle the above challenges, we apply the idea of a variable reduction strategy (VRS) to an EMS problem, which can accelerate the optimization process of the used EAs and obtain better solutions by simplifying the corresponding EMS problems. Firstly, we define an emergency material allocation and route scheduling model, and a variable neighborhood search and NSGA-II hybrid algorithm (VNS-NSGAII) is designed to solve the model. Secondly, we utilize VRS to simplify the proposed EMS model to enable a lower dimension and fewer equality constraints. Furthermore, we integrate VRS with VNS-NSGAII to solve the reduced EMS model. To prove the effectiveness of VRS on VNS-NSAGII, we construct two test cases, where one case is based on a multi-depot vehicle routing problem and the other case is combined with the initial 5∙12 Wenchuan earthquake emergency material support situation. Experimental results show that VRS can improve the performance of the standard VNS-NSGAII, enabling better optimization efficiency and a higher-quality solution

Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner

Preventing premature convergence and proving the optimality in evolutionary algorithms

http://ea2013.inria.fr//proceedings.pdfInternational audienceEvolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

Strategic Technology Maturation and Insertion (STMI): a requirements guided, technology development optimization process

This research presents a Decision Support System (DSS) process solution to a problem faced by Program Managers (PMs) early in a system lifecycle, when potential technologies are evaluated for placement within a system design. The proposed process for evaluation and selection of technologies incorporates computer based Operational Research techniques which automate and optimize key portions of the decision process. This computerized process allows the PM to rapidly form the basis of a Strategic Technology Plan (STP) designed to manage, mature and insert the technologies into the system design baseline and identify potential follow-on incremental system improvements. This process is designated Strategic Technology Maturation and Insertion (STMI). Traditionally, to build this STP, the PM must juggle system performance, schedule, and cost issues and strike a balance of new and old technologies that can be fielded to meet the requirements of the customer. To complicate this juggling skill, the PM is typically confronted with a short time frame to evaluate hundreds of potential technology solutions with thousands of potential interacting combinations within the system design. Picking the best combination of new and established technologies, plus selecting the critical technologies needing maturation investment is a significant challenge. These early lifecycle decisions drive the entire system design, cost and schedule well into production The STMI process explores a formalized and repeatable DSS to allow PMs to systematically tackle the problems with technology evaluation, selection and maturation. It gives PMs a tool to compare and evaluate the entire design space of candidate technology performance, incorporate lifecycle costs as an optimizer for a best value system design, and generate input for a strategic plan to mature critical technologies. Four enabling concepts are described and brought together to form the basis of STMI: Requirements Engineering (RE), Value Engineering (VE), system optimization and Strategic Technology Planning (STP). STMI is then executed in three distinct stages: Pre-process preparation, process operation and optimization, and post-process analysis. A demonstration case study prepares and implements the proposed STMI process in a multi-system (macro) concept down select and a specific (micro) single system design that ties into the macro design level decision

Development of an intelligent earthwork optimization system

Tese de Doutoramento em Engenharia Civil.Earthworks are often regarded as one of the most costly and time-consuming components of linear infrastructure constructions (e.g., road, railway and airports). Since actual construction requirements originate higher demands for productivity and safety in earthwork constructions, the optimal usage of every resource in these tasks is paramount. The management of resources in an earthwork construction site is, in great part, a function of the allocation of the available equipment, for which there are a vast number of possible equipment allocation combinations. Simultaneously, while there is often high competitiveness, where the pressure is to provide the least possible costs and durations, contractors and project designers often settle for an allocation solution that is mostly based on their own intuition and accumulated experience. This guarantees neither optimal resource usage, nor a solution associated with minimal cost and duration. The optimal allocation of equipment in earthwork tasks is a complex problem that requires the study of several different aspects, as well as the knowledge of a large number of factors. In fact, earthworks are comprised by a combination of repetitive, sequential, and interdependent activities based on heavy mechanical equipment (i.e., resources), such as excavators, dumper trucks, bulldozers and compactors. In order to optimally allocate the available resources, knowledge regarding their specifications (e.g., capacity, weight, horsepower) and the work conditions to which they will be subjected (e.g., material types, required and available volumes in embankment and excavation fronts, respectively) is essential. This knowledge can be translated into the productivity (i.e., work rate) of each piece of equipment when working under a specific set of conditions. Moreover, since earthwork tasks are inherently sequential and interdependent, the interaction between the allocated equipment must be taken into account. A typical example of this is the need for matching the work rate of an excavator plant with the capacity of a truck plant to haul the excavated material to the embankment fronts. Given the non-trivial characteristics of the earthwork allocation problem, conventional Operation Research (e.g., linear programming) and blind search methods are infeasible. As such, a potential solution is to adopt metaheuristics – modern optimization methods capable of searching large space regions under a reasonable use of computational resources. While this may address the issue of optimizing such a complex problem, the lack of knowledge regarding optimization parameters under different work conditions, such as equipment productivity, calls for a different approach. Bearing in mind the availability of large databases, including in the earthworks area, that have been gathered in recent years by construction companies, technologies like data mining (DM) come forward as ideal tools for solving this problem. Indeed, the learning capabilities of DM algorithms can be applied to databases embodying the productivity of several equipment types when subjected to different work conditions. The extracted knowledge can then be used to estimate the productivity of the available equipment under similar work conditions. Furthermore, as previously referred, since earthwork tasks include the material hauling from excavation to embankment fronts, it also becomes imperative to analyse and optimize the possible transportation networks. In this context, the use of geographic information systems provides an easy method to study the possible trajectories for transportation equipment in a construction site, ultimately allowing for a choice of the best paths to improve the workflow. This work explores the integration of different technologies in order to allow for an optimization of the earthworks process. This is translated in the form of an evolutionary multi-criteria optimization system, capable of searching for the best allocation of the available equipment that minimizes a set of goals (e.g., cost, duration, environmental impact). The results stemming from the application of the system to a case study in a Portuguese earthwork construction site are presented. These comprise the assessment of the system performance, including a comparison between different optimization methods. Furthermore, an analysis regarding the improvement of workflow in the construction site after the implementation of the system is discussed, in the context of several comparisons between original (i.e., obtained by manual design) and optimized allocation solutions. Ultimately, these results illustrate the potential and importance of using this kind of technologies in the management and optimization of earthworks.Em projetos de construção de infraestruturas de transporte lineares (e.g., estradas, vias férreas e aeroportos), as terraplenagens são geralmente consideradas um dos componentes com custos e tempos de execução mais elevados. Tendo em conta que cada vez mais é exigido um aumento na produtividade e segurança no contexto das construções de terraplenagens, torna-se fulcral a otimização de todas as tarefas relacionadas com este processo. A gestão de recursos num estaleiro de terraplenagens é, em grande parte, função da alocação do equipamento mecânico disponível, para a qual existe um número quase infinito de soluções possíveis em cada caso. Simultaneamente, embora se verifique um alto nível de competitividade nesta área, onde o objetivo é obter custos e durações de execução o mais baixos possíveis, o planeamento das tarefas de terraplenagens é em grande parte baseado na experiência acumulada dos engenheiros e especialistas. Porém, tais métodos não garantem nem uma utilização ótima dos recursos disponíveis, nem uma solução associada ao custo e duração de execução mínimos. A alocação ótima de equipamento mecânico em tarefas de terraplenagens é um problema complexo que requer o estudo de vários aspectos distintos, assim como o conhecimento de um elevado número de fatores. De facto, estas tarefas são demarcadas por combinações de atividades repetitivas, fortemente baseadas no uso de equipamento mecânico (i.e., recursos), tal como escavadoras, dumpers, espalhadores e compactadores. Para que seja possível a sua alocação ótima, é essencial o conhecimento das suas especificações (e.g., capacidade, peso, potência) e das condições a que estão sujeitos durante a sua atividade (e.g., tipos de material, volumes disponíveis em frentes de escavação e necessários em frentes de aterro). Este conhecimento pode ser traduzido na produtividade de cada equipamento quando sujeito a determinadas condições de trabalho. Para além disso, uma vez que as terraplenagens consistem em tarefas inerentemente sequenciais e interdependentes, a interação entre os equipamentos tem de ser tomada em consideração. Um exemplo típico deste aspecto pode ser ilustrado pela necessidade de sincronizar a produtividade de uma equipa de escavadoras com a de uma equipa de dumpers, para que seja possível um fluxo constande de escavação e transporte de geomateriais das frentes de escavação para as frentes de aterro. Tendo em conta as características não triviais do problema de alocação em terraplenagens, os métodos convencionais de procura de soluções, tais como Investigação Operacional (e.g. programação linear) e busca exaustiva são impraticáveis. Assim, uma potencial solução é a adoção de metaheurísticas – métodos de otimização moderna capazes de efetuar a busca de soluções em espaços de procura extensos com níveis de exigência computacional razoáveis. Embora estes métodos sejam práticos para a otimização de problemas de elevado nível de complexidade, como é o caso das terraplenagens, existe ainda a necessidade de abordar o problema relacionado com a escassez de conhecimento de vários parâmetros necessários à otimização, tais como a produtividade dos equipamentos sujeitos a diferentes condições de trabalho. Considerando os recentes avanços da tecnologia e o aumento da prática de recolha de dados, verifica-se a disponibilidade de extensas bases de dados de construção, incluindo na área de terraplenagens. Neste sentido, tecnologias tais como o data mining (DM) surgem como ferramentas ideais para abordar esse problema. De fato, as capacidades de aprendizagem dos algoritmos de DM podem ser aplicadas às bases de dados existentes com informação relativa à produtividade de vários tipos de equipamento sujeitos a diferentes condições de trabalho. Mediante este processo, o conhecimento extraído pode então ser usado em novos casos para estimar a produtividade de equipamentos em condições semelhantes. Adicionalmente, uma vez que as tarefas de terraplenagens incluem o transporte de materiais de frentes de escavação para frentes de aterro, como previamente referido, torna-se ainda imperativa a análise e otimização das potenciais trajetórias de transporte ao longo do estaleiro. Neste contexto, a utilização de sistemas de informação geográficos providencia um método eficaz de estudo e escolha das melhores trajetórias para o equipamento de transporte, melhorando o fluxo de trabalho no estaleiro. Este trabalho explora a integração de diferentes tecnologias tendo em vista a otimização das tarefas de terraplenagens. Isto concretiza-se sob a forma de um sistema de otimização evolutiva multi-objetivo, capaz de eleger a melhor distribuição dos equipamentos de terraplenagens disponíveis que minimiza um determinado conjunto de objetivos (e.g., custo, duração, impacto ambiental). São apresentados os resultados decorrentes da aplicação do sistema desenvolvido num caso de estudo, associado a um estaleiro de terraplenagens em Portugal. Estes abrangem a avaliação do desempenho do sistema de otimização, incluindo a comparação de vários métodos de otimização. Para além disso, é realizada uma análise relativa ao melhoramento do fluxo de trabalho no estaleiro após a implementação do sistema, sendo enquadrada numa série de comparações entre as soluções originais (i.e., obtidas pelos métodos convencionais de dimensionamento) e as soluções otimizadas correspondentes. Em última análise, estes resultados ilustram o potencial e a importância da utilização deste tipo de tecnologias na gestão e otimização das terraplenagens.Fundação para a Ciência e a Tecnologia (FCT) SFRH/BD/71501/2010

Management, Technology and Learning for Individuals, Organisations and Society in Turbulent Environments

This book presents the collection of fifty papers which were presented in the Second International Conference on BUSINESS SUSTAINABILITY 2011 - Management, Technology and Learning for Individuals, Organisations and Society in Turbulent Environments , held in Póvoa de Varzim, Portugal, from 22ndto 24thof June, 2011.The main motive of the meeting was growing awareness of the importance of the sustainability issue. This importance had emerged from the growing uncertainty of the market behaviour that leads to the characterization of the market, i.e. environment, as turbulent. Actually, the characterization of the environment as uncertain and turbulent reflects the fact that the traditional technocratic and/or socio-technical approaches cannot effectively and efficiently lead with the present situation. In other words, the rise of the sustainability issue means the quest for new instruments to deal with uncertainty and/or turbulence. The sustainability issue has a complex nature and solutions are sought in a wide range of domains and instruments to achieve and manage it. The domains range from environmental sustainability (referring to natural environment) through organisational and business sustainability towards social sustainability. Concerning the instruments for sustainability, they range from traditional engineering and management methodologies towards “soft” instruments such as knowledge, learning, and creativity. The papers in this book address virtually whole sustainability problems space in a greater or lesser extent. However, although the uncertainty and/or turbulence, or in other words the dynamic properties, come from coupling of management, technology, learning, individuals, organisations and society, meaning that everything is at the same time effect and cause, we wanted to put the emphasis on business with the intention to address primarily companies and their businesses. Due to this reason, the main title of the book is “Business Sustainability 2.0” but with the approach of coupling Management, Technology and Learning for individuals, organisations and society in Turbulent Environments. Also, the notation“2.0” is to promote the publication as a step further from our previous publication – “Business Sustainability I” – as would be for a new version of software. Concerning the Second International Conference on BUSINESS SUSTAINABILITY, its particularity was that it had served primarily as a learning environment in which the papers published in this book were the ground for further individual and collective growth in understanding and perception of sustainability and capacity for building new instruments for business sustainability. In that respect, the methodology of the conference work was basically dialogical, meaning promoting dialog on the papers, but also including formal paper presentations. In this way, the conference presented a rich space for satisfying different authors’ and participants’ needs. Additionally, promoting the widest and global learning environment and participation, in accordance with the Conference's assumed mission to promote Proactive Generative Collaborative Learning, the Conference Organisation shares/puts open to the community the papers presented in this book, as well as the papers presented on the previous Conference(s). These papers can be accessed from the conference webpage (http://labve.dps.uminho.pt/bs11). In these terms, this book could also be understood as a complementary instrument to the Conference authors’ and participants’, but also to the wider readerships’ interested in the sustainability issues. The book brought together 107 authors from 11 countries, namely from Australia, Belgium, Brazil, Canada, France, Germany, Italy, Portugal, Serbia, Switzerland, and United States of America. The authors “ranged” from senior and renowned scientists to young researchers providing a rich and learning environment. At the end, the editors hope, and would like, that this book to be useful, meeting the expectation of the authors and wider readership and serving for enhancing the individual and collective learning, and to incentive further scientific development and creation of new papers. Also, the editors would use this opportunity to announce the intention to continue with new editions of the conference and subsequent editions of accompanying books on the subject of BUSINESS SUSTAINABILITY, the third of which is planned for year 2013.info:eu-repo/semantics/publishedVersio