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

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

On the role of metaheuristic optimization in bioinformatics

Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics

Metaheuristic Algorithms for Spatial Multi-Objective Decision Making

Spatial decision making is an everyday activity, common to individuals and organizations. However, recently there is an increasing interest in the importance of spatial decision-making systems, as more decision-makers with concerns about sustainability, social, economic, environmental, land use planning, and transportation issues discover the benefits of geographical information. Many spatial decision problems are regarded as optimization problems, which involve a large set of feasible alternatives, multiple conflicting objectives that are difficult and complex to solve. Hence, Multi-Objective Optimization methods (MOO)—metaheuristic algorithms integrated with Geographical Information Systems (GIS) are appealing to be powerful tools in these regards, yet their implementation in spatial context is still challenging. In this thesis, various metaheuristic algorithms are adopted and improved to solve complex spatial problems. Disaster management and urban planning are used as case studies of this thesis.These case studies are explored in the four papers that are part of this thesis. In paper I, four metaheuristic algorithms have been implemented on the same spatial multi-objective problem—evacuation planning, to investigate their performance and potential. The findings show that all tested algorithms were effective in solving the problem, although in general, some had higher performance, while others showed the potential of being flexible to be modified to fit better to the problem. In the same context, paper II identified the effectiveness of the Multi-objective Artificial Bee Colony (MOABC) algorithm when improved to solve the evacuation problem. In paper III, we proposed a multi-objective optimization approach for urban evacuation planning that considered three spatial objectives which were optimized using an improved Multi-Objective Cuckoo Search algorithm (MOCS). Both improved algorithms (MOABC and MOCS) proved to be efficient in solving evacuation planning when compared to their standard version and other algorithms. Moreover, Paper IV proposed an urban land-use allocation model that involved three spatial objectives and proposed an improved Non-dominated Sorting Biogeography-based Optimization algorithm (NSBBO) to solve the problem efficiently and effectively.Overall, the work in this thesis demonstrates that different metaheuristic algorithms have the potential to change the way spatial decision problems are structured and can improve the transparency and facilitate decision-makers to map solutions and interactively modify decision preferences through trade-offs between multiple objectives. Moreover, the obtained results can be used in a systematic way to develop policy recommendations. From the perspective of GIS - Multi-Criteria Decision Making (MCDM) research, the thesis contributes to spatial optimization modelling and extended knowledge on the application of metaheuristic algorithms. The insights from this thesis could also benefit the development and practical implementation of other Artificial Intelligence (AI) techniques to enhance the capabilities of GIS for tackling complex spatial multi-objective decision problems in the future

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Operations Management

Global competition has caused fundamental changes in the competitive environment of the manufacturing and service industries. Firms should develop strategic objectives that, upon achievement, result in a competitive advantage in the market place. The forces of globalization on one hand and rapidly growing marketing opportunities overseas, especially in emerging economies on the other, have led to the expansion of operations on a global scale. The book aims to cover the main topics characterizing operations management including both strategic issues and practical applications. A global environmental business including both manufacturing and services is analyzed. The book contains original research and application chapters from different perspectives. It is enriched through the analyses of case studies

An approach for the integration of intelligent maintenance systems and collaborative decentralized spare parts supply chains

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Produção, Florianópolis, 2017.As cadeias de suprimento de peças de reposição são particularmente desafiadas pela baixa previsibilidade da demanda, necessidade de altos níveis de serviço e custo de estoque. Para lidar com esses desafios, através de uma estratégia de sincronização dos participantes de cadeias de suprimentos, o planejamento colaborativo pode ser combinado aos sistemas inteligentes de manutenção que auxiliam na predição das falhas. Porém, modelos hierárquicos de integração têm pouca aceitação dos atores da cadeia, que não desejam compartilhar dados estratégicos. Modelos descentralizados promovem a viabilidade do planejamento colaborativo, entretanto ainda não foram aplicados em cadeias de suprimentos de peças de reposição. Neste contexto, no presente trabalho, uma análise bibliométrica com revisão bibliográfica sobre colaboração e planejamento da cadeia de suprimentos é aplicada com o intuito de verificar os principais conceitos, direções e oportunidades de pesquisa. Foram identificadas lacunas na aplicação de modelos descentralizados em casos reais, bem como uma falta de suporte para a escolha de métodos de resolução. Dessa forma, o objetivo desse trabalho é propor um procedimento estruturado para embasar a aplicação de planejamento colaborativo descentralizado em cadeias de suprimentos de peças de reposição. Para isso, uma tabela de características é construída com o intuito de apoiar a escolha do método de resolução de problemas lineares. Na sequência, é desenvolvido e testado um procedimento estruturado para adequar um conceito de planejamento colaborativo decentralizado a cadeias de suprimentos de peças de reposição integradas a sistemas inteligentes de manutenção. Esse procedimento estruturado desenvolvido é aplicado em um caso teste e como resultado um modelo adequado de planejamento operacional colaborativo é proposto para o aprimoramento dessa cadeia de suprimento. O planejamento descentralizado obteve melhores resultados que uma abordagem clássica de gestão mesmo em cenários de alta variação na demanda, como ocorre nas cadeias de suprimentos de peças de reposição.Abstract : Spare parts supply chains are particularly challenged by the low predictability of the demand, the need for high service levels and the cost of inventory. To address these challenges through a strategy of synchronizing supply chain participants, collaborative planning can be combined with intelligent maintenance systems that help predict failures. However, hierarchical models of integration have little acceptance from actors in the chain, who do not wish to share strategic data. Decentralized models promote the feasibility of collaborative planning but have not yet been applied in spare parts supply chains. In this context, in the present work, a bibliometric analysis with a bibliographic review on collaboration and supply chain planning is applied in order to verify the main concepts, directions and research opportunities. Research opportunities were identified in the application of decentralized models in real cases, and there was a general lack of information to support the choice of a solving method. Thus, the objective of this work is to propose a structured procedure to support the application of decentralized collaborative planning in supply chains of spare parts. For this, a table of characteristics is constructed to support the choice of the method for solving linear problems. A structured procedure is then developed and tested to tailor a decentralized collaborative planning concept to a spare parts supply chain integrated with intelligent maintenance systems. This developed structured procedure is applied in a test case and, as a result, an adequate model of collaborative operational planning is proposed for the improvement of this supply chain. Decentralized planning was identified as having achieved better results than a classical management approach even in scenarios of high demand variation, such as in spare parts supply chains

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available