626 research outputs found

    Combining evolutionary algorithms and agent-based simulation for the development of urbanisation policies

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    Urban-planning authorities continually face the problem of optimising the allocation of green space over time in developing urban environments. To help in these decision-making processes, this thesis provides an empirical study of using evolutionary approaches to solve sequential decision making problems under uncertainty in stochastic environments. To achieve this goal, this work is underpinned by developing a theoretical framework based on the economic model of Alonso and the associated methodology for modelling spatial and temporal urban growth, in order to better understand the complexity inherent in this kind of system and to generate and improve relevant knowledge for the urban planning community. The model was hybridised with cellular automata and agent-based model and extended to encompass green space planning based on urban cost and satisfaction. Monte Carlo sampling techniques and the use of the urban model as a surrogate tool were the two main elements investigated and applied to overcome the noise and uncertainty derived from dealing with future trends and expectations. Once the evolutionary algorithms were equipped with these mechanisms, the problem under consideration was defined and characterised as a type of adaptive submodular. Afterwards, the performance of a non-adaptive evolutionary approach with a random search and a very smart greedy algorithm was compared and in which way the complexity that is linked with the configuration of the problem modifies the performance of both algorithms was analysed. Later on, the application of very distinct frameworks incorporating evolutionary algorithm approaches for this problem was explored: (i) an ‘offline’ approach, in which a candidate solution encodes a complete set of decisions, which is then evaluated by full simulation, and (ii) an ‘online’ approach which involves a sequential series of optimizations, each making only a single decision, and starting its simulations from the endpoint of the previous run

    Hybrid Simulation-based Planning Framework for Agri-Fresh Produce Supply Chain

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    The ever-increasing demand for fresh and healthy products raises the economic importance of managing Agri-Fresh Produce Supply Chain (AFPSC) effectively. However, the literature review has indicated that many challenges undermine efficient planning for AFPSCs. Stringent regulations on production and logistics activities, production seasonality and high yield variations (quantity and quality), and products vulnerability to multiple natural stresses, alongside with their critical shelf life, impact the planning process. This calls for developing smart planning and decision-support tools which provides higher efficiency for such challenges. Modelling and simulation (M&S) approaches for AFPSC planning problems have a proven record in offering safe and economical solutions. Increase in problem complexity has urged the use of hybrid solutions that integrate different approaches to provide better understanding of the system dynamism in an environment characterised by multi-firm and multi-dimensional relationships. The proposed hybrid simulation-based planning framework for AFPSCs has addressed internal decision-making mechanisms, rules and control procedures to support strategic, tactical and operational planning decisions. An exploratory study has been conducted using semi-structured interviews with twelve managers from different agri-fresh produce organisations. The aim of this study is to understand management practices regarding planning and to gain insights on current challenges. Discussions with managers on planning issues such as resources constraints, outsourcing, capacity, product sensitivity, quality, and lead times have formed the foundation of process mapping. As a result, conceptual modelling process is then used to model supply chain planning activities. These conceptual models are inclusive and reflective to system complexity and decision sensitivity. Verification of logic and accuracy of the conceptual models has been done by few directors in AFPSC before developing a hybrid simulation model. Hybridisation of Discrete Event Simulation (DES), System Dynamics (SD), and Agent-Based Modelling (ABM) has offered flexibility and precision in modelling this complex supply chain. DES provides operational models that include different entities of AFPSC, and SD minds investments decisions according to supply and demand implications, while ABM is concerned with modelling variations of human behaviour and experience. The proposed framework has been validated using Table Grapes Supply Chain (TGSC) case study. Decision makers have appreciated the level of details included in the solution at different planning levels (i.e., operational, tactical and strategic). Results show that around 58% of wasted products can be saved if correct hiring policy is adopted in the management of seasonal labourer recruitment. This would also factor in more than 25% improved profits at packing house entity. Moreover, an anticipation of different supply and demand scenarios demonstrated that inefficiency of internal business processes might undermine the whole business from gaining benefits of market growth opportunities

    Envisioning Digital Europe 2030: Scenarios for ICT in Future Governance and Policy Modelling

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    The report Envisioning Digital Europe 2030 is the result of research conducted by the Information Society Unit of IPTS as part of the CROSSROAD Project - A Participative Roadmap on ICT research on Electronic Governance and Policy Modelling (www.crossroad-eu.net ). After outlining the purpose and scope of the report and the methodological approach followed, the report presents the results of a systematic analysis of societal, policy and research trends in the governance and policy modelling domain in Europe. These analyses are considered central for understanding and roadmapping future research on ICT for governance and policy modelling. The study further illustrates the scenario design framework, analysing current and future challenges in ICT for governance and policy modelling, and identifying the key impact dimensions to be considered. It then presents the scenarios developed at the horizon 2030, including the illustrative storyboards representative of each scenario and the prospective opportunities and risks identified for each of them. The scenarios developed are internally consistent views of what the European governance and policy making system could have become by 2030 and of what the resulting implications for citizens, business and public services would be. Finally, the report draws conclusions and presents the proposed shared vision for Digital Europe 2030, offering also a summary of the main elements to be considered as an input for the future development of the research roadmap on ICT for governance and policy modelling.JRC.DDG.J.4-Information Societ

    Review of macroeconomic approaches to modelling Wellbeing, Inclusion, and Sustainability

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    In response to the urgent global challenges of climate change and rising inequality, the need to re-evaluate our traditional economic models and adopt new approaches focused on sustainability, wellbeing, and inclusion has become evident. The current economic paradigms, based on equilibrium thinking and GDP-centric measurements, have proven inadequate in addressing the intricate interplay between economic, social, and environmental dimensions. As we embark on a transformative journey towards a sustainable and equitable future, it is crucial to adopt diverse modelling approaches to provide policymakers and stakeholders with informed decision-making tools. This report delves into the analysis of five different macroeconomic model types (general equilibrium models, macro-econometric & input-output models, stockflow-consistent models, integrated assessment models, and system dynamics models), evaluating their respective strengths and weaknesses to propose an integrated framework that encompasses the multifaceted nature of our world. A key recommendation is to improve existing models by enhancing their dynamics and feedback loops between dimensions and systems, thus better reflecting the interactions and effects of different social and economic policies. Striking a balance between complexity and transparency is essential, ensuring that models remain flexible and capable of linking with models with greater detail but narrower focus. The report emphasizes the incorporation of WISE accounts (detailed data on Wellbeing, Inclusion, Sustainability, and Economy that will be collected and harmonized during the project) into macroeconomic models as an opportunity to overcome the challenge of data availability, which poses a significant obstacle in modelling endeavours. Robust and reliable data sources are crucial to the success of any model and require continual improvement in data collection processes. To broaden our understanding of the dynamics of WISE dimensions and the potential impacts of policies, integrating alternative perspectives, such as heterodox economics, can offer valuable insights. Co-creating quantitative analysis with stakeholders enhances ownership and uptake of the models and may help with bridging the gap between research and policy implementation. Furthermore, an integrated modelling framework that accounts for the non-linear interactions between human and earth systems is necessary to properly assess policies tackling 21st century challenges in the context of WISE dimensions. This integrated model should draw upon the data of WISE accounts and synergize elements of Input-Output models, System-Dynamics, and Stock-Flow consistent models to provide a structured tool for policymakers and researchers in shaping a sustainable and inclusive future

    Horizon 2020: Work Programme 2016 - 2017.

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    13. Europe in a changing world – inclusive, innovative and reflective Societie
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