The solar energy consumer agent decision (SECAD) model : addressing complexity through GIS-integrated agent-based modeling

textThis thesis presents a step-by-step implementation of the Solar Energy Consumer Agent Decision (SECAD) model: an empirically-grounded multi-agent model of residential solar photovoltaic (PV) adoption with an integrated geospatial topology. Solar PV diffusion is a complex system with geographic heterogeneity, uncertain information, high financial risk, and important social interaction and feedback effects between consumers. A key limitation for agentbased models in human socio-technical systems is the integration of empirical patterns in the model structure, initialization, and validation efforts. This limitation is addressed though highly granular and interlocking data-streams from the geographic, social network, financial, demographic, and decision-making process of real households in the study. The fitted and validation model is used to simulate implementation of potential policies to inform decision-makers: i) Targeted informational dissemination campaigns, ii) Tiered rebates, iii) Locational pricing, and iv) Alternative rebate schedules. Informational campaigns can increase cumulative installations by as much as 12%, but vary greatly in their effectiveness based on which agents are targeted. Simulations suggest that by lowering the cost barrier to lower wealth households through a slightly higher rebate (+$0.25/Watt), the mean difference in wealth between solar adopters and non adopters could be reduced by 22.6$0.25 higher offering increased the percentage of adopters in the target area from less than 1% to over 10%. Relative to flatter rebate schedules, sharply decreasing schedules are effective in terms of motivating adoption but inefficient in small markets. It is our hope that this work will provide a working example for other agent-based models of human socio-technical systems as well as provide insight into the likely outcomes of novel policy-levers such as those described above.Energy and Earth ResourcesPublic Affair

Ranking Influential Nodes of Fake News Spreading on Mobile Social Networks

Online fake news can generate a negative impact on both users and society. Due to the concerns with spread of fake news and misinformation, assessing the network influence of online users has become an important issue. This study quantifies the influence of nodes by proposing an algorithm based on information entropy theory. Dynamic process of influence of nodes is characterized on mobile social networks (MSNs). Weibo (i.e., the Chinese version of microblogging) users are chosen to build the real network and quantified influence of them is analyzed according to the model proposed in this paper. MATLAB is employed to simulate and validate the model. Results show the comprehensive influence of nodes increases with the rise of two factors: the number of nodes connected to them and the frequency of their interaction. Indirect influence of nodes becomes stronger than direct influence when the network scope rises. This study can help relevant organizations effectively oversee the spread of online fake news on MSNs

Development of transportation and supply chain problems with the combination of agent-based simulation and network optimization

Demand drives a different range of supply chain and logistics location decisions, and agent-based modeling (ABM) introduces innovative solutions to address supply chain and logistics problems. This dissertation focuses on an agent-based and network optimization approach to resolve those problems and features three research projects that cover prevalent supply chain management and logistics problems. The first case study evaluates demographic densities in Norway, Finland, and Sweden, and covers how distribution center (DC) locations can be established using a minimizing trip distance approach. Furthermore, traveling time maps are developed for each scenario. In addition, the Nordic area consisting of those three countries is analyzed and five DC location optimization results are presented. The second case study introduces transportation cost modelling in the process of collecting tree logs from several districts and transporting them to the nearest collection point. This research project presents agent-based modelling (ABM) that incorporates comprehensively the key elements of the pick-up and delivery supply chain model and designs the components as autonomous agents communicating with each other. The modelling merges various components such as GIS routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. The entire pick-up and delivery operation are modeled by ABM and modeling outcomes are provided by time series charts such as the number of trucks in use, facilities inventory and travel distance. In addition, various scenarios of simulation based on potential facility locations and truck numbers are evaluated and the optimal facility location and fleet size are identified. In the third case study, an agent-based modeling strategy is used to address the problem of vehicle scheduling and fleet optimization. The solution method is employed to data from a real-world organization, and a set of key performance indicators are created to assess the resolution's effectiveness. The ABM method, contrary to other modeling approaches, is a fully customized method that can incorporate extensively various processes and elements. ABM applying the autonomous agent concept can integrate various components that exist in the complex supply chain and create a similar system to assess the supply chain efficiency.Tuotteiden kysyntä ohjaa erilaisia toimitusketju- ja logistiikkasijaintipäätöksiä, ja agenttipohjainen mallinnusmenetelmä (ABM) tuo innovatiivisia ratkaisuja toimitusketjun ja logistiikan ongelmien ratkaisemiseen. Tämä väitöskirja keskittyy agenttipohjaiseen mallinnusmenetelmään ja verkon optimointiin tällaisten ongelmien ratkaisemiseksi, ja sisältää kolme tapaustutkimusta, jotka voidaan luokitella kuuluvan yleisiin toimitusketjun hallinta- ja logistiikkaongelmiin. Ensimmäinen tapaustutkimus esittelee kuinka käyttämällä väestötiheyksiä Norjassa, Suomessa ja Ruotsissa voidaan määrittää strategioita jakelukeskusten (DC) sijaintiin käyttämällä matkan etäisyyden minimoimista. Kullekin skenaariolle kehitetään matka-aikakartat. Lisäksi analysoidaan näistä kolmesta maasta koostuvaa pohjoismaista aluetta ja esitetään viisi mahdollista sijaintia optimointituloksena. Toinen tapaustutkimus esittelee kuljetuskustannusmallintamisen prosessissa, jossa puutavaraa kerätään useilta alueilta ja kuljetetaan lähimpään keräyspisteeseen. Tämä tutkimusprojekti esittelee agenttipohjaista mallinnusta (ABM), joka yhdistää kattavasti noudon ja toimituksen toimitusketjumallin keskeiset elementit ja suunnittelee komponentit keskenään kommunikoiviksi autonomisiksi agenteiksi. Mallinnuksessa yhdistetään erilaisia komponentteja, kuten GIS-reititys, mahdolliset tilojen sijainnit, satunnaiset puunhakupaikat, kaluston mitoitus, matkan pituus sekä monimuotokuljetukset. ABM:n avulla mallinnetaan noutojen ja toimituksien koko ketju ja tuloksena saadaan aikasarjoja kuvaamaan käytössä olevat kuorma-autot, sekä varastomäärät ja ajetut matkat. Lisäksi arvioidaan erilaisia simuloinnin skenaarioita mahdollisten laitosten sijainnista ja kuorma-autojen lukumäärästä sekä tunnistetaan optimaalinen toimipisteen sijainti ja tarvittava autojen määrä. Kolmannessa tapaustutkimuksessa agenttipohjaista mallinnusstrategiaa käytetään ratkaisemaan ajoneuvojen aikataulujen ja kaluston optimoinnin ongelma. Ratkaisumenetelmää käytetään dataan, joka on peräisin todellisesta organisaatiosta, ja ratkaisun tehokkuuden arvioimiseksi luodaan lukuisia keskeisiä suorituskykyindikaattoreita. ABM-menetelmä, toisin kuin monet muut mallintamismenetelmät, on täysin räätälöitävissä oleva menetelmä, joka voi sisältää laajasti erilaisia prosesseja ja elementtejä. Autonomisia agentteja soveltava ABM voi integroida erilaisia komponentteja, jotka ovat olemassa monimutkaisessa toimitusketjussa ja luoda vastaavan järjestelmän toimitusketjun tehokkuuden arvioimiseksi yksityiskohtaisesti.fi=vertaisarvioitu|en=peerReviewed

A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization

Influence Maximization (IM) is a classical combinatorial optimization problem, which can be widely used in mobile networks, social computing, and recommendation systems. It aims at selecting a small number of users such that maximizing the influence spread across the online social network. Because of its potential commercial and academic value, there are a lot of researchers focusing on studying the IM problem from different perspectives. The main challenge comes from the NP-hardness of the IM problem and \#P-hardness of estimating the influence spread, thus traditional algorithms for overcoming them can be categorized into two classes: heuristic algorithms and approximation algorithms. However, there is no theoretical guarantee for heuristic algorithms, and the theoretical design is close to the limit. Therefore, it is almost impossible to further optimize and improve their performance. With the rapid development of artificial intelligence, the technology based on Machine Learning (ML) has achieved remarkable achievements in many fields. In view of this, in recent years, a number of new methods have emerged to solve combinatorial optimization problems by using ML-based techniques. These methods have the advantages of fast solving speed and strong generalization ability to unknown graphs, which provide a brand-new direction for solving combinatorial optimization problems. Therefore, we abandon the traditional algorithms based on iterative search and review the recent development of ML-based methods, especially Deep Reinforcement Learning, to solve the IM problem and other variants in social networks. We focus on summarizing the relevant background knowledge, basic principles, common methods, and applied research. Finally, the challenges that need to be solved urgently in future IM research are pointed out.Comment: 45 page

Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems

This book explores the critical role of acquisition, application, enhancement, and management of knowledge and human competence in the context of the largely digital and data/information dominated modern world. Whilst humanity owes much of its achievements to the distinct capability to learn from observation, analyse data, gain insights, and perceive beyond original realities, the systematic treatment of knowledge as a core capability and driver of success has largely remained the forte of pedagogy. In an increasingly intertwined global community faced with existential challenges and risks, the significance of knowledge creation, innovation, and systematic understanding and treatment of human competence is likely to be humanity's greatest weapon against adversity. This book was conceived to inform the decision makers and practitioners about the best practice pertinent to many disciplines and sectors. The chapters fall into three broad categories to guide the readers to gain insight from generic fundamentals to discipline-specific case studies and of the latest practice in knowledge and competence management

Harnessing Knowledge, Innovation and Competence in Engineering of Mission Critical Systems

This book explores the critical role of acquisition, application, enhancement, and management of knowledge and human competence in the context of the largely digital and data/information dominated modern world. Whilst humanity owes much of its achievements to the distinct capability to learn from observation, analyse data, gain insights, and perceive beyond original realities, the systematic treatment of knowledge as a core capability and driver of success has largely remained the forte of pedagogy. In an increasingly intertwined global community faced with existential challenges and risks, the significance of knowledge creation, innovation, and systematic understanding and treatment of human competence is likely to be humanity's greatest weapon against adversity. This book was conceived to inform the decision makers and practitioners about the best practice pertinent to many disciplines and sectors. The chapters fall into three broad categories to guide the readers to gain insight from generic fundamentals to discipline-specific case studies and of the latest practice in knowledge and competence management

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Deep Learning based Densenet Convolution Neural Network for Community Detection in Online Social Networks

Online Social Networks (OSNs) have become increasingly popular, with hundreds of millions of users in recent years. A community in a social network is a virtual group with shared interests and activities that they want to communicate. OSN and the growing number of users have also increased the need for communities. Community structure is an important topological property of OSN and plays an essential role in various dynamic processes, including the diffusion of information within the network. All networks have a community format, and one of the most continually addressed research issues is the finding of communities. However, traditional techniques didn't do a better community of discovering user interests. As a result, these methods cannot detect active communities.  To tackle this issues, in this paper presents Densenet Convolution Neural Network (DnetCNN) approach for community detection. Initially, we gather dataset from Kaggle repository. Then preprocessing the dataset to remove inconsistent and missing values. In addition to User Behavior Impact Rate (UBIR) technique to identify the user URL access, key term and page access. After that, Web Crawling Prone Factor Rate (WCPFR) technique is used find the malicious activity random forest and decision method. Furthermore, Spider Web Cluster Community based Feature Selection (SWC2FS) algorithm is used to choose finest attributes in the dataset. Based on the attributes, to find the community group using Densenet Convolution Neural Network (DnetCNN) approach. Thus, the experimental result produce better performance than other methods