Att utveckla en innovativ organisation. "Är du färdig snart?"

Följande faktorer är avgörande för Rambölls innovationssträvanden: Branschlogik. Ramböll verkar i en bransch där aktörerna försöker positionera sig med kvalité, men där priset är den främsta konkurrensfaktorn på en homogen marknad. Strategi. Ramböll försöker framstå som innovativt men detta når inte ner till medarbetarna, som i första hand styrs av effektivitetskrav. Vinst- och effektivitetskrav påverkar verksamhet i större utsträckning än innovationsambitioner. Ledningen bör göra ett medvetet val om vilken strategi som skall användas

Entity management in p2p networks with voronoi topology

Detta arbete undersöker hur skalbarheten i ett peer-to-peer-nätverk (P2P) byggt med voronoi-based overlay network (VON) som topologi, påverkas av entitetshantering och felhantering, när det används i nätverkslösnigen till ett realtidsspel med många spelare. Under arbetet skapades en testplattform som används under ett experiment för att utvärdera huruvida aspekter såsom ansvarsuppdelning och nodkraschhantering påverkar antalet meddelanden som behöver skickas och därigenom skalbarheten i nätverket. Experimentet undersöker flera fall, med olika mycket betoning på entitetshantering och felhantering och resultaten visar att nätverket behåller sin skalbarhet och att totala antalet meddelanden som skickas håller sig mestadels opåverkad mellan fallen, trots hanteringen

Knowledge discovery for interactive decision support and knowledge-driven optimization

Multi-objective optimization involves the simultaneous optimization of several objective functions. In real-world problems, these objectives are often in conflict, giving rise to trade-offs in the optimal solutions from the optimization process. All these solutions are equally viable, with no single solution being better or worse than the others. Typically, decision makers have certain preferences that guide the selection of a final solution for practical implementation. While most multi-criteria decision analysis methods focus on the performance of solutions in the objective space, it is important to note that practically relevant knowledge is often found in the design space. Access to this knowledge can provide decision makers with meaningful insights into the problem and the optimization process, leading to more informed decision-making. This thesis develops and employs methods for knowledge discovery in the context of multi-objective optimization. By emphasizing explicit knowledge representations, this thesis investigates how extracted knowledge can be processed and presented to decision-makers in an interactive manner for insightful decision support. This thesis also explores how extracted knowledge from preferred solutions can be integrated into the algorithms or the multi-objective optimization problem itself, to improve the convergence behavior of optimization algorithms. This approach, called Knowledge-Driven Optimization (KDO), can be implemented either offline or online. Offline KDO involves incorporating knowledge obtained from previous optimization runs into future problem scenarios of a similar nature, restricting the search process to preferred regions of the objective space. A main challenge with such approaches is the storage and retrieval of relevant past knowledge, as well as modifications to the optimization problem formulation. In contrast, online KDO involves integrating knowledge discovery methods with optimization algorithms and utilizing the knowledge obtained during their runtime to enhance the search process, driving algorithms towards better convergence in preferred regions of the objective space. This approach necessitates the development of new search operators capable of incorporating and exploiting various forms of knowledge. In both offline and online KDO, the veracity and accuracy of the extracted knowledge are critical factors. The thesis validates the effectiveness of the developed methods using various benchmark and engineering optimization test problems, and use-cases from the manufacturing industry. A particular focus is given to generating explicit knowledge that is both meaningful to human decision makers, and can easily be processed algorithmically. The main contributions of this thesis are methods for discovering relevant knowledge about the convergence characteristics of problems, a decision support system for interactive knowledge discovery, and algorithms for realizing both offline and online KDO by incorporating knowledge into the optimization process.Flermålsoptimering hanterar samtidig optimering av flera målfunktioner, vilka i praktiska optimeringsproblem ofta är i konflikt, vilket ger upphov till avvägningar i de optimala lösningarna från optimeringsprocessen. Alla dessa lösningar är lika värdefulla, och ingen lösning är bättre eller sämre än någon annan. Typiskt sett har beslutsfattare också preferenser som styr valet av en slutlig lösning att implementera i praktiken. De flesta metoder för analys av flera kriterier fokuserar på prestandan hos en uppsättning lösningar i målrymden, det är dock viktigt att notera att praktiskt relevant kunskap ofta finns i designrymden till lösningarna. Tillgång till denna kunskap kan ge beslutsfattare betydelsefulla insikter till både problemet och optimeringsprocessen, vilket leder till mer informerat beslutstagande. Denna avhandling utvecklar och använder metoder för kunskapsutvinning i sammanhanget av flermålsoptimering. Genom ett särskilt fokus på explicit kunskap, undersöker denna avhandling hur utvunnen kunskap kan bearbetas och presenteras för beslutsfattare på ett interaktivt sätt för förbättrat beslutsstöd. Det undersöks också hur utvunnen kunskap från tidigare lösningar kan integreras i algoritmer för flermålsoptimerings eller direkt i optimeringsproblem för att avlasta beräkning av nya lösningar i optimeringsprocessen. Sådana metoder, kallade kunskapsdriven optimering (KDO), kan implementeras antingen offline eller online. Offline KDO innebär att integrera kunskap som erhållits från tidigare optimeringar, i framtida, liknande problem, vilket avlastar sökprocessen till preferensrika regioner i målrymden. En huvudsaklig utmaning med offline KDO är lagring och återhämtning av relevant tidigare kunskap, samt modifieringar av formuleringar till optimeringsproblem. I kontrast innefattar online KDO att integrera metoder för kunskapsutvinning tillsammans med optimeringsalgoritmer, och att utnyttja den resulterande kunskapen under optimeringen, för att förbättra sökprocessen och driva algoritmerna mot snabbare ankomst i preferensrika regioner i målrymden. Sådana metoder kräver utveckling av nya sökoperatorer kapabla att integrera och utnyttja olika former av utvunnen kunskap. I både offline och online KDO är det viktigt att den integrerade kunskapen beskriver beslutfattarens preferenser noggrant. Denna avhandling validerar effektiviteten hos de utvecklade metoderna med hjälp av olika benchmark-optimeringsproblem, praktiska tekniska testproblem och fallstudier från tillverkningsindustrin. Ett särskilt fokus har lagts på utvinning av explicit kunskap som både är meningsfull för beslutsfattare och som enkelt kan bearbetas algoritmiskt. Denna avhandlings huvudsakliga bidrag består av metoder för utvinning av relevant kunskap om sökbeteendet för problem, ett beslutstödssystem för interaktiv kunskapsutvinning, samt algoritmer för att förverkliga både offline och online KDO genom att integrera kunskap i optimeringsprocessen

Knowledge discovery for interactive decision support and knowledge-driven optimization

Multi-objective optimization involves the simultaneous optimization of several objective functions. In real-world problems, these objectives are often in conflict, giving rise to trade-offs in the optimal solutions from the optimization process. All these solutions are equally viable, with no single solution being better or worse than the others. Typically, decision makers have certain preferences that guide the selection of a final solution for practical implementation. While most multi-criteria decision analysis methods focus on the performance of solutions in the objective space, it is important to note that practically relevant knowledge is often found in the design space. Access to this knowledge can provide decision makers with meaningful insights into the problem and the optimization process, leading to more informed decision-making. This thesis develops and employs methods for knowledge discovery in the context of multi-objective optimization. By emphasizing explicit knowledge representations, this thesis investigates how extracted knowledge can be processed and presented to decision-makers in an interactive manner for insightful decision support. This thesis also explores how extracted knowledge from preferred solutions can be integrated into the algorithms or the multi-objective optimization problem itself, to improve the convergence behavior of optimization algorithms. This approach, called Knowledge-Driven Optimization (KDO), can be implemented either offline or online. Offline KDO involves incorporating knowledge obtained from previous optimization runs into future problem scenarios of a similar nature, restricting the search process to preferred regions of the objective space. A main challenge with such approaches is the storage and retrieval of relevant past knowledge, as well as modifications to the optimization problem formulation. In contrast, online KDO involves integrating knowledge discovery methods with optimization algorithms and utilizing the knowledge obtained during their runtime to enhance the search process, driving algorithms towards better convergence in preferred regions of the objective space. This approach necessitates the development of new search operators capable of incorporating and exploiting various forms of knowledge. In both offline and online KDO, the veracity and accuracy of the extracted knowledge are critical factors. The thesis validates the effectiveness of the developed methods using various benchmark and engineering optimization test problems, and use-cases from the manufacturing industry. A particular focus is given to generating explicit knowledge that is both meaningful to human decision makers, and can easily be processed algorithmically. The main contributions of this thesis are methods for discovering relevant knowledge about the convergence characteristics of problems, a decision support system for interactive knowledge discovery, and algorithms for realizing both offline and online KDO by incorporating knowledge into the optimization process.Flermålsoptimering hanterar samtidig optimering av flera målfunktioner, vilka i praktiska optimeringsproblem ofta är i konflikt, vilket ger upphov till avvägningar i de optimala lösningarna från optimeringsprocessen. Alla dessa lösningar är lika värdefulla, och ingen lösning är bättre eller sämre än någon annan. Typiskt sett har beslutsfattare också preferenser som styr valet av en slutlig lösning att implementera i praktiken. De flesta metoder för analys av flera kriterier fokuserar på prestandan hos en uppsättning lösningar i målrymden, det är dock viktigt att notera att praktiskt relevant kunskap ofta finns i designrymden till lösningarna. Tillgång till denna kunskap kan ge beslutsfattare betydelsefulla insikter till både problemet och optimeringsprocessen, vilket leder till mer informerat beslutstagande. Denna avhandling utvecklar och använder metoder för kunskapsutvinning i sammanhanget av flermålsoptimering. Genom ett särskilt fokus på explicit kunskap, undersöker denna avhandling hur utvunnen kunskap kan bearbetas och presenteras för beslutsfattare på ett interaktivt sätt för förbättrat beslutsstöd. Det undersöks också hur utvunnen kunskap från tidigare lösningar kan integreras i algoritmer för flermålsoptimerings eller direkt i optimeringsproblem för att avlasta beräkning av nya lösningar i optimeringsprocessen. Sådana metoder, kallade kunskapsdriven optimering (KDO), kan implementeras antingen offline eller online. Offline KDO innebär att integrera kunskap som erhållits från tidigare optimeringar, i framtida, liknande problem, vilket avlastar sökprocessen till preferensrika regioner i målrymden. En huvudsaklig utmaning med offline KDO är lagring och återhämtning av relevant tidigare kunskap, samt modifieringar av formuleringar till optimeringsproblem. I kontrast innefattar online KDO att integrera metoder för kunskapsutvinning tillsammans med optimeringsalgoritmer, och att utnyttja den resulterande kunskapen under optimeringen, för att förbättra sökprocessen och driva algoritmerna mot snabbare ankomst i preferensrika regioner i målrymden. Sådana metoder kräver utveckling av nya sökoperatorer kapabla att integrera och utnyttja olika former av utvunnen kunskap. I både offline och online KDO är det viktigt att den integrerade kunskapen beskriver beslutfattarens preferenser noggrant. Denna avhandling validerar effektiviteten hos de utvecklade metoderna med hjälp av olika benchmark-optimeringsproblem, praktiska tekniska testproblem och fallstudier från tillverkningsindustrin. Ett särskilt fokus har lagts på utvinning av explicit kunskap som både är meningsfull för beslutsfattare och som enkelt kan bearbetas algoritmiskt. Denna avhandlings huvudsakliga bidrag består av metoder för utvinning av relevant kunskap om sökbeteendet för problem, ett beslutstödssystem för interaktiv kunskapsutvinning, samt algoritmer för att förverkliga både offline och online KDO genom att integrera kunskap i optimeringsprocessen

Interactive knowledge discovery and knowledge visualization for decision support in multi-objective optimization

In many practical applications, the end-goal of multi-objective optimization is to select an implementable solution that is close to the Pareto-optimal front while satisfying the decision maker’s preferences. The decision making process is challenging since it involves the manual consideration of all solutions. The field of multi-criteria decision making offers many methods that help the decision maker in this process. However, most methods only focus on analyzing the solutions’ objective values. A more informed decision generally requires the additional knowledge of how different preferences affect the variable values. One difficulty in realizing this is that while the preferences are often expressed in the objective space, the knowledge required to implement a preferred solution exists in the decision space. In this paper, we propose a decision support system that allows interactive knowledge discovery and knowledge visualization to support practitioners by simultaneously considering preferences in the objective space and their impact in the decision space. The knowledge discovery step can use either of two recently proposed data mining techniques for extracting decision rules that conform to given preferences, while the extracted knowledge is visualized via a novel graph-based approach that allows the discovery of important variables, their values and their interactions with other variables. The result is an intuitive and interactive decision support system that aids the entire decision making process — from solution visualization to knowledge visualization. We demonstrate the usefulness of this system on benchmark optimization problems up to 10 objectives and real-world problems with up to six objectives.CC BY 4.0Corresponding author: Henrik Smedberg. E-mail addresses: [email protected] (H. Smedberg), [email protected] (S. Bandaru).The authors acknowledge the financial support received from KK-stiftelsen (The Knowledge Foundation, Stockholm, Sweden) under the Research Profile 2018 project Virtual Factories with Knowledge-Driven Optimization. For more information, please visit www.virtualfactories.se/</p

Enabling Knowledge Discovery in Multi-Objective Optimizations of Worker Well-Being and Productivity

Usually, optimizing productivity and optimizing worker well-being are separate tasks performed by engineers with different roles and goals using different tools. This results in a silo effect which can lead to a slow development process and suboptimal solutions, with one of the objectives, either productivity or worker well-being, being given precedence. Moreover, studies often focus on finding the best solutions for a particular use case, and once solutions have been identified and one has been implemented, the engineers move on to analyzing the next use case. However, the knowledge obtained from previous use cases could be used to find rules of thumb for similar use cases without needing to perform new optimizations. In this study, we employed the use of data mining methods to obtain knowledge from a real-world optimization dataset of multi-objective optimizations of worker well-being and productivity with the aim to identify actionable insights for the current and future optimization cases. Using different analysis and data mining methods on the database revealed rules, as well as the relative importance of the design variables of a workstation. The generated rules have been used to identify measures to improve the welding gun workstation design.CC BY 4.0Correspondence: [email protected]: This work has received support from ITEA3/Vinnova in the project MOSIM (2018-02227), and from Stiftelsen för Kunskaps- och Kompetensutveckling within the Synergy Virtual Ergonomics (SVE) project (2018-0167) and the Virtual Factories–Knowledge-Driven Optimization (VF-KDO) research profile (2018-0011). This support is gratefully acknowledged.</p

Interaction Gaps in PhD Education and ICT as a Way Forward: Results from a Study in Sweden

Many research studies have highlighted the low completion rate and slow progress in PhD education. Universities strive to improve throughput and quality in their PhD education programs. In this study, the perceived problems of PhD education are investigated from PhD students' points of view, and how an Information and Communication Technology Support System (ICTSS) may alleviate these problems. Data were collected through an online open questionnaire sent to the PhD students at the Department of (the institution's name has been removed during the double-blind review) with a 59% response rate. The results revealed a number of problems in the PhD education and highlighted how online technology can support PhD education and facilitate interaction and communication, affect the PhD students' satisfaction, and have positive impacts on PhD students' stress. A system was prototyped, in order to facilitate different types of online interaction through accessing a set of online and structured resources and specific communication channels. Although the number of informants was not large, the result of the study provided some rudimentary ideas that refer to interaction problems and how an online ICTSS may facilitate PhD education by providing distance and collaborative learning, and PhD students' self-managed communication

Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems

Current market requirements force manufacturing companies to face production changes more often than ever before. Reconfigurable manufacturing systems (RMS) are considered a key enabler in today's manufacturing industry to cope with such dynamic and volatile markets. The literature confirms that the use of simulation-based multi-objective optimization offers a promising approach that leads to improvements in RMS. However, due to the dynamic behavior of real-world RMS, applying conventional optimization approaches can be very time-consuming, specifically when there is no general knowledge about the quality of solutions. Meanwhile, Pareto-optimal solutions may share some common design principles that can be discovered with data mining and machine learning methods and exploited by the optimization. In this study, the authors investigate a novel knowledge-driven optimization (KDO) approach to speed up the convergence in RMS applications. This approach generates generalized knowledge from previous scenarios, which is then applied to improve the efficiency of the optimization of new scenarios. This study applied the proposed approach to a multi-part flow line RMS that considers scalable capacities while addressing the tasks assignment to workstations and the buffer allocation problems. The results demonstrate how a KDO approach leads to convergence rate improvements in a real-world RMS case

Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems

Current market requirements force manufacturing companies to face production changes more often than ever before. Reconfigurable manufacturing systems (RMS) are considered a key enabler in today's manufacturing industry to cope with such dynamic and volatile markets. The literature confirms that the use of simulation-based multi-objective optimization offers a promising approach that leads to improvements in RMS. However, due to the dynamic behavior of real-world RMS, applying conventional optimization approaches can be very time-consuming, specifically when there is no general knowledge about the quality of solutions. Meanwhile, Pareto-optimal solutions may share some common design principles that can be discovered with data mining and machine learning methods and exploited by the optimization. In this study, the authors investigate a novel knowledge-driven optimization (KDO) approach to speed up the convergence in RMS applications. This approach generates generalized knowledge from previous scenarios, which is then applied to improve the efficiency of the optimization of new scenarios. This study applied the proposed approach to a multi-part flow line RMS that considers scalable capacities while addressing the tasks assignment to workstations and the buffer allocation problems. The results demonstrate how a KDO approach leads to convergence rate improvements in a real-world RMS case.CC BY 4.0Correspondence: [email protected] work was funded by the Knowledge Foundation (KKS), Sweden, through the KKS Profile Virtual Factories with Knowledge-Driven Optimization, VF-KDO, Grant No. 2018-0011.(This article belongs to the Special Issue Evolutionary Multi-objective Optimization: An Honorary Issue Dedicated to Professor Kalyanmoy Deb)</p

An Enhanced Simulation-Based Multi-Objective Optimization Approach with Knowledge Discovery for Reconfigurable Manufacturing Systems

In today’s uncertain and competitive market, where manufacturing enterprises are subjected to increasingly shortened product lifecycles and frequent volume changes, reconfigurable manufacturing system (RMS) applications play significant roles in the success of the manufacturing industry. Despite the advantages offered by RMSs, achieving high efficiency constitutes a challenging task for stakeholders and decision makers when they face the trade-off decisions inherent in these complex systems. This study addresses work task and resource allocations to workstations together with buffer capacity allocation in an RMS. The aim is to simultaneously maximize throughput and to minimize total buffer capacity under fluctuating production volumes and capacity changes while considering the stochastic behavior of the system. An enhanced simulation-based multi-objective optimization (SMO) approach with customized simulation and optimization components is proposed to address the abovementioned challenges. Apart from presenting the optimal solutions subject to volume and capacity changes, the proposed approach supports decision makers with knowledge discovery to further understand RMS design. In particular, this study presents a customized SMO approach combined with a novel flexible pattern mining method for optimizing an RMS and conducts post-optimal analyses. To this extent, this study demonstrates the benefits of applying SMO and knowledge discovery methods for fast decision support and production planning of an RMS.CC BY 4.0(This article belongs to the Special Issue Multi-Objective Optimization and Decision Support Systems)Received: 15 February 2023 / Revised: 15 March 2023 / Accepted: 17 March 2023 / Published: 21 March 2023The authors thank the Knowledge Foundation, Sweden (KKS) for funding this research through the KKS Profile Virtual Factories with Knowledge-Driven Optimization, VF-KDO, grant number 2018-0011.</p