Submitted by Geandra Rodrigues (geandrar@gmail.com) on 2018-06-19T13:13:53Z No. of bitstreams: 1 rodrigobarbosadesantis.pdf: 2597054 bytes, checksum: b19542ca0e9312572d8ffa5896d735db (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2018-06-27T11:12:01Z (GMT) No. of bitstreams: 1 rodrigobarbosadesantis.pdf: 2597054 bytes, checksum: b19542ca0e9312572d8ffa5896d735db (MD5) / Made available in DSpace on 2018-06-27T11:12:01Z (GMT). No. of bitstreams: 1 rodrigobarbosadesantis.pdf: 2597054 bytes, checksum: b19542ca0e9312572d8ffa5896d735db (MD5) Previous issue date: 2018-03-26 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Falta de materiais é um problema comum na cadeia de suprimentos, impactando o nível de serviço e eficiência de um sistema de inventário. A identificação de materiais com grande riscos de falta antes da ocorrência do evento pode apresentar uma enorme oportunidade de melhoria no desempenho geral de uma empresa. No entanto, a complexidade deste tipo de problema é alta, devido ao desbalanceamento das classes de itens faltantes e não faltantes no inventário, que podem chegar a razões de 1 para 100. No presente trabalho, algoritmos de classificação são investigados para proposição de um modelo preditivo para preencher esta lacuna na literatura. Algumas métricas específicas como a área abaixo das curvas de Característica Operacionais do Receptor e de Precisão-Abrangência, bem como técnicas de amostragem e comitês de aprendizado são aplicados nesta tarefa. O modelo proposto foi testado em dois estudos de caso reais, nos quais verificou-se que adoção da ferramenta pode contribuir com o aumento do nível de serviço em uma cadeia de suprimentos. / Material backorder (or stockout) is a common supply chain problem, impacting the inventory system service level and effectiveness. Identifying materials with the highest chances of shortage prior its occurrence can present a high opportunity to improve the overall company’s performance. However, the complexity of this sort of problem is high, due to class imbalance between missing items and not missing ones in inventory, which can achieve proportions of 1 to 100. In this work, machine learning classifiers are investigated in order to fulfill this gap in literature. Specific metrics such as area under the Receiver Operator Characteristic and precision-recall curves, sampling techniques and ensemble learning are employed to this particular task. The proposed model was tested in two real case-studies, in which it was verified that the use of the tool may contribute with the improvemnet of the service level in the supply chain.

Submitted by Marcia Silva (marcia@latec.uff.br) on 2016-02-03T18:56:50Z No. of bitstreams: 1 Dissert Queila da Costa.pdf: 4793341 bytes, checksum: c506d3b0f1dbda0591f766090f48bde6 (MD5) / Made available in DSpace on 2016-02-03T18:56:50Z (GMT). No. of bitstreams: 1 Dissert Queila da Costa.pdf: 4793341 bytes, checksum: c506d3b0f1dbda0591f766090f48bde6 (MD5) Previous issue date: 2012-03-20 / Este trabalho tem como objetivo principal propor um método de planejamento e controle da produção de projetos de cenografia, o qual proporcione melhoria no gerenciamento das atividades (projetos) desempenhadas pelo setor responsável pelas construções/produções dos cenários e cidades cenográficas de uma empresa de entretenimento. Neste aspecto, como opção metodológica, apresenta, avalia, sugere e discute uma proposição através de um case vivencial realizado em uma empresa do segmento de telecomunicações de grande porte. Assim, o presente estudo mapeia os processos de planejamento e controle da produção, relacionando as propostas com as boas práticas de gerenciamento de projetos e gestão do conhecimento. Ao longo desta pesquisa evidenciou-se a contribuição de novas possibilidades e oportunidades através do redesenho na metodologia da produção, a qual adere a outros modelos de negócios, distintos do segmento de entretenimento/cenografia, apresentados nesta dissertação. / This work has as its main objective to propose a method of production planning and control of projects of scenography, which provide improved management of activities (projects) performed by the sector responsible for the construction/productions of scenographic cities and scenarios of an entertainment company. In this respect, as methodological option, features, evaluates, and discusses a proposal through an experiential case held in a telecommunications company. Thus, the present study maps the processes of production planning and control, relating the proposals with the best practices of project management and knowledge management. Throughout this research was the rendition of new possibilities and opportunities through the redesign in production methodology which adheres to other business models, distinguished entertainment segment/scenography, presented in this dissertation.

No description available.

A indústria da construção civil vem assumindo um papel determinante no desenvolvimento econômico do país e vivenciado um crescimento exponencial que exige das empresas uma dedicação crescente na busca por novas tecnologias construtivas e ferramentas que auxiliem na gestão de empreendimentos. Sistemas construtivos racionalizados que repercutam na melhoria dos produtos com a redução de custos são essenciais para manter a competitividade no setor. O sistema construtivo de vedações verticais em concreto armado moldadas in loco confere uma alternativa na produção de habitações em larga escala caracterizada pela redução de custos e alta produtividade originando uma cadeia produtiva que auxilia no planejamento e controle da produção em obras. O presente trabalho tem como objetivo contribuir para o planejamento e o controle da produção de edificações que utilizam em seu processo o sistema construtivo anteriormente citado, propondo um modelo de organização da produção baseado na redução da variabilidade dos processos produtivos por meio do fluxo contínuo da produção, que proporcione o trabalho ininterrupto no canteiro de obras. A proposta associa os princípios que sustentam a filosofia produtiva da Lean Constrution, e recomenda um modelo para o planejamento operacional de curto prazo, que defina os fluxos produtivos pela aplicação de ritmos constantes nas atividades mediante o nivelamento dos recursos de mão-de-obra envolvidos no processo produtivo com a introdução de células produtivas que proporcionam lotes otimizados de trabalho, constituindo células de trabalho. / The construction industry has a decisive role in economic development of the country and experienced exponential growth requires dedication growing companies in the search for constructive new technologies and tools that will assist them in managing exposures. Constructive rationalized systems that reverberate in improvement of products with reducing costs is critical to remain competitive in the industry. The constructive system using cast-in-place concrete vertical panels confers an alternative in the production of large-scale housing characterized by reduced costs and high productivity that provides a productive chain which assists in the planning and production control in workplaces. This work aims to contribute to the planning and production control buildings that use its constructive system previously cited, proposing a model organization of production based on reducing variability in productive processes through streaming production, providing uninterrupted work on site. The proposal combines the principles supporting the production philosophy of Lean Constrution, and recommends a template for the short-term operational planning, defining workflows by applying patterns appearing in activities by the leveling of labour resources involved in the production process with the introduction of work cells that provide small batches of work.

This master’s thesis analyses problems which are related to planning of orders in form of projects in custom manufacturing in a particular company. The basic for this work is the evaluation of analysis of actual planning of orders situation. On the basis of this analysis is proposed further recommendation for optimalization of project planning in custom Manufacturing whose creating was the main content this master’s thesis.

More and more of our physical world today is being monitored and controlled by so-called cyber-physical systems (CPSs). These are compositions of networked autonomous cyber and physical agents such as sensors, actuators, computational elements, and humans in the loop. Today, CPSs are still relatively small-scale and very limited compared to CPSs to be witnessed in the future. Future CPSs are expected to be far more complex, large-scale, wide-spread, and mission-critical, and found in a variety of domains such as transportation, medicine, manufacturing, and energy, where they will bring many advantages such as the increased efficiency, sustainability, reliability, and security. To unleash their full potential, CPSs need to be equipped with, among other features, the support for automated planning and control, where computing agents collaboratively and continuously plan and control their actions in an intelligent and well-coordinated manner to secure and optimize a physical process, e.g., electricity flow in the power grid. In today’s CPSs, the control is typically automated, but the planning is solely performed by humans. Unfortunately, it is intractable and infeasible for humans to plan every action in a future CPS due to the complexity, scale, and volatility of a physical process. Due to these properties, the control and planning has to be continuous and automated in future CPSs. Humans may only analyse and tweak the system’s operation using the set of tools supporting prescriptive analytics that allows them (1) to make predictions, (2) to get the suggestions of the most prominent set of actions (decisions) to be taken, and (3) to analyse the implications as if such actions were taken. This thesis considers the planning and control in the context of a large-scale multi-agent CPS. Based on the smart-grid use-case, it presents a so-called PrescriptiveCPS – which is (the conceptual model of) a multi-agent, multi-role, and multi-level CPS automatically and continuously taking and realizing decisions in near real-time and providing (human) users prescriptive analytics tools to analyse and manage the performance of the underlying physical system (or process). Acknowledging the complexity of CPSs, this thesis provides contributions at the following three levels of scale: (1) the level of a (full) PrescriptiveCPS, (2) the level of a single PrescriptiveCPS agent, and (3) the level of a component of a CPS agent software system. At the CPS level, the contributions include the definition of PrescriptiveCPS, according to which it is the system of interacting physical and cyber (sub-)systems. Here, the cyber system consists of hierarchically organized inter-connected agents, collectively managing instances of so-called flexibility, decision, and prescription models, which are short-lived, focus on the future, and represent a capability, an (user’s) intention, and actions to change the behaviour (state) of a physical system, respectively. At the agent level, the contributions include the three-layer architecture of an agent software system, integrating the number of components specially designed or enhanced to support the functionality of PrescriptiveCPS. At the component level, the most of the thesis contribution is provided. The contributions include the description, design, and experimental evaluation of (1) a unified multi-dimensional schema for storing flexibility and prescription models (and related data), (2) techniques to incrementally aggregate flexibility model instances and disaggregate prescription model instances, (3) a database management system (DBMS) with built-in optimization problem solving capability allowing to formulate optimization problems using SQL-like queries and to solve them “inside a database”, (4) a real-time data management architecture for processing instances of flexibility and prescription models under (soft or hard) timing constraints, and (5) a graphical user interface (GUI) to visually analyse the flexibility and prescription model instances. Additionally, the thesis discusses and exemplifies (but provides no evaluations of) (1) domain-specific and in-DBMS generic forecasting techniques allowing to forecast instances of flexibility models based on historical data, and (2) powerful ways to analyse past, current, and future based on so-called hypothetical what-if scenarios and flexibility and prescription model instances stored in a database. Most of the contributions at this level are based on the smart-grid use-case. In summary, the thesis provides (1) the model of a CPS with planning capabilities, (2) the design and experimental evaluation of prescriptive analytics techniques allowing to effectively forecast, aggregate, disaggregate, visualize, and analyse complex models of the physical world, and (3) the use-case from the energy domain, showing how the introduced concepts are applicable in the real world. We believe that all this contribution makes a significant step towards developing planning-capable CPSs in the future. / Mehr und mehr wird heute unsere physische Welt überwacht und durch sogenannte Cyber-Physical-Systems (CPS) geregelt. Dies sind Kombinationen von vernetzten autonomen cyber und physischen Agenten wie Sensoren, Aktoren, Rechenelementen und Menschen. Heute sind CPS noch relativ klein und im Vergleich zu CPS der Zukunft sehr begrenzt. Zukünftige CPS werden voraussichtlich weit komplexer, größer, weit verbreiteter und unternehmenskritischer sein sowie in einer Vielzahl von Bereichen wie Transport, Medizin, Fertigung und Energie – in denen sie viele Vorteile wie erhöhte Effizienz, Nachhaltigkeit, Zuverlässigkeit und Sicherheit bringen – anzutreffen sein. Um ihr volles Potenzial entfalten zu können, müssen CPS unter anderem mit der Unterstützung automatisierter Planungs- und Steuerungsfunktionalität ausgestattet sein, so dass Agents ihre Aktionen gemeinsam und kontinuierlich auf intelligente und gut koordinierte Weise planen und kontrollieren können, um einen physischen Prozess wie den Stromfluss im Stromnetz sicherzustellen und zu optimieren. Zwar sind in den heutigen CPS Steuerung und Kontrolle typischerweise automatisiert, aber die Planung wird weiterhin allein von Menschen durchgeführt. Leider ist diese Aufgabe nur schwer zu bewältigen, und es ist für den Menschen schlicht unmöglich, jede Aktion in einem zukünftigen CPS auf Basis der Komplexität, des Umfangs und der Volatilität eines physikalischen Prozesses zu planen. Aufgrund dieser Eigenschaften müssen Steuerung und Planung in CPS der Zukunft kontinuierlich und automatisiert ablaufen. Der Mensch soll sich dabei ganz auf die Analyse und Einflussnahme auf das System mit Hilfe einer Reihe von Werkzeugen konzentrieren können. Derartige Werkzeuge erlauben (1) Vorhersagen, (2) Vorschläge der wichtigsten auszuführenden Aktionen (Entscheidungen) und (3) die Analyse und potentiellen Auswirkungen der zu fällenden Entscheidungen. Diese Arbeit beschäftigt sich mit der Planung und Kontrolle im Rahmen großer Multi-Agent-CPS. Basierend auf dem Smart-Grid als Anwendungsfall wird ein sogenanntes PrescriptiveCPS vorgestellt, welches einem Multi-Agent-, Multi-Role- und Multi-Level-CPS bzw. dessen konzeptionellem Modell entspricht. Diese PrescriptiveCPS treffen und realisieren automatisch und kontinuierlich Entscheidungen in naher Echtzeit und stellen Benutzern (Menschen) Prescriptive-Analytics-Werkzeuge und Verwaltung der Leistung der zugrundeliegenden physischen Systeme bzw. Prozesse zur Verfügung. In Anbetracht der Komplexität von CPS leistet diese Arbeit Beiträge auf folgenden Ebenen: (1) Gesamtsystem eines PrescriptiveCPS, (2) PrescriptiveCPS-Agenten und (3) Komponenten eines CPS-Agent-Software-Systems. Auf CPS-Ebene umfassen die Beiträge die Definition von PrescriptiveCPS als ein System von wechselwirkenden physischen und cyber (Sub-)Systemen. Das Cyber-System besteht hierbei aus hierarchisch organisierten verbundenen Agenten, die zusammen Instanzen sogenannter Flexibility-, Decision- und Prescription-Models verwalten, welche von kurzer Dauer sind, sich auf die Zukunft konzentrieren und Fähigkeiten, Absichten (des Benutzers) und Aktionen darstellen, die das Verhalten des physischen Systems verändern. Auf Agenten-Ebene umfassen die Beiträge die Drei-Ebenen-Architektur eines Agentensoftwaresystems sowie die Integration von Komponenten, die insbesondere zur besseren Unterstützung der Funktionalität von PrescriptiveCPS entwickelt wurden. Der Schwerpunkt dieser Arbeit bilden die Beiträge auf der Komponenten-Ebene, diese umfassen Beschreibung, Design und experimentelle Evaluation (1) eines einheitlichen multidimensionalen Schemas für die Speicherung von Flexibility- and Prescription-Models (und verwandten Daten), (2) der Techniken zur inkrementellen Aggregation von Instanzen eines Flexibilitätsmodells und Disaggregation von Prescription-Models, (3) eines Datenbankmanagementsystem (DBMS) mit integrierter Optimierungskomponente, die es erlaubt, Optimierungsprobleme mit Hilfe von SQL-ähnlichen Anfragen zu formulieren und sie „in einer Datenbank zu lösen“, (4) einer Echtzeit-Datenmanagementarchitektur zur Verarbeitung von Instanzen der Flexibility- and Prescription-Models unter (weichen oder harten) Zeitvorgaben und (5) einer grafische Benutzeroberfläche (GUI) zur Visualisierung und Analyse von Instanzen der Flexibility- and Prescription-Models. Darüber hinaus diskutiert und veranschaulicht diese Arbeit beispielhaft ohne detaillierte Evaluation (1) anwendungsspezifische und im DBMS integrierte Vorhersageverfahren, die die Vorhersage von Instanzen der Flexibility- and Prescription-Models auf Basis historischer Daten ermöglichen, und (2) leistungsfähige Möglichkeiten zur Analyse von Vergangenheit, Gegenwart und Zukunft auf Basis sogenannter hypothetischer „What-if“-Szenarien und der in der Datenbank hinterlegten Instanzen der Flexibility- and Prescription-Models. Die meisten der Beiträge auf dieser Ebene basieren auf dem Smart-Grid-Anwendungsfall. Zusammenfassend befasst sich diese Arbeit mit (1) dem Modell eines CPS mit Planungsfunktionen, (2) dem Design und der experimentellen Evaluierung von Prescriptive-Analytics-Techniken, die eine effektive Vorhersage, Aggregation, Disaggregation, Visualisierung und Analyse komplexer Modelle der physischen Welt ermöglichen und (3) dem Anwendungsfall der Energiedomäne, der zeigt, wie die vorgestellten Konzepte in der Praxis Anwendung finden. Wir glauben, dass diese Beiträge einen wesentlichen Schritt in der zukünftigen Entwicklung planender CPS darstellen. / Mere og mere af vores fysiske verden bliver overvåget og kontrolleret af såkaldte cyber-fysiske systemer (CPSer). Disse er sammensætninger af netværksbaserede autonome IT (cyber) og fysiske (physical) agenter, såsom sensorer, aktuatorer, beregningsenheder, og mennesker. I dag er CPSer stadig forholdsvis små og meget begrænsede i forhold til de CPSer vi kan forvente i fremtiden. Fremtidige CPSer forventes at være langt mere komplekse, storstilede, udbredte, og missionskritiske, og vil kunne findes i en række områder såsom transport, medicin, produktion og energi, hvor de vil give mange fordele, såsom øget effektivitet, bæredygtighed, pålidelighed og sikkerhed. For at frigøre CPSernes fulde potentiale, skal de bl.a. udstyres med støtte til automatiseret planlægning og kontrol, hvor beregningsagenter i samspil og løbende planlægger og styrer deres handlinger på en intelligent og velkoordineret måde for at sikre og optimere en fysisk proces, såsom elforsyningen i elnettet. I nuværende CPSer er styringen typisk automatiseret, mens planlægningen udelukkende er foretaget af mennesker. Det er umuligt for mennesker at planlægge hver handling i et fremtidigt CPS på grund af kompleksiteten, skalaen, og omskifteligheden af en fysisk proces. På grund af disse egenskaber, skal kontrol og planlægning være kontinuerlig og automatiseret i fremtidens CPSer. Mennesker kan kun analysere og justere systemets drift ved hjælp af det sæt af værktøjer, der understøtter præskriptive analyser (prescriptive analytics), der giver dem mulighed for (1) at lave forudsigelser, (2) at få forslagene fra de mest fremtrædende sæt handlinger (beslutninger), der skal tages, og (3) at analysere konsekvenserne, hvis sådanne handlinger blev udført. Denne afhandling omhandler planlægning og kontrol i forbindelse med store multi-agent CPSer. Baseret på en smart-grid use case, præsenterer afhandlingen det såkaldte PrescriptiveCPS hvilket er (den konceptuelle model af) et multi-agent, multi-rolle, og multi-level CPS, der automatisk og kontinuerligt tager beslutninger i nær-realtid og leverer (menneskelige) brugere præskriptiveanalyseværktøjer til at analysere og håndtere det underliggende fysiske system (eller proces). I erkendelse af kompleksiteten af CPSer, giver denne afhandling bidrag til følgende tre niveauer: (1) niveauet for et (fuldt) PrescriptiveCPS, (2) niveauet for en enkelt PrescriptiveCPS agent, og (3) niveauet for en komponent af et CPS agent software system. På CPS-niveau, omfatter bidragene definitionen af PrescriptiveCPS, i henhold til hvilken det er det system med interagerende fysiske- og IT- (under-) systemer. Her består IT-systemet af hierarkisk organiserede forbundne agenter der sammen styrer instanser af såkaldte fleksibilitet (flexibility), beslutning (decision) og præskriptive (prescription) modeller, som henholdsvis er kortvarige, fokuserer på fremtiden, og repræsenterer en kapacitet, en (brugers) intention, og måder til at ændre adfærd (tilstand) af et fysisk system. På agentniveau omfatter bidragene en tre-lags arkitektur af et agent software system, der integrerer antallet af komponenter, der er specielt konstrueret eller udbygges til at understøtte funktionaliteten af PrescriptiveCPS. Komponentniveauet er hvor afhandlingen har sit hovedbidrag. Bidragene omfatter beskrivelse, design og eksperimentel evaluering af (1) et samlet multi- dimensionelt skema til at opbevare fleksibilitet og præskriptive modeller (og data), (2) teknikker til trinvis aggregering af fleksibilitet modelinstanser og disaggregering af præskriptive modelinstanser (3) et database management system (DBMS) med indbygget optimeringsproblemløsning (optimization problem solving) der gør det muligt at formulere optimeringsproblemer ved hjælp af SQL-lignende forespørgsler og at løse dem "inde i en database", (4) en realtids data management arkitektur til at behandle instanser af fleksibilitet og præskriptive modeller under (bløde eller hårde) tidsbegrænsninger, og (5) en grafisk brugergrænseflade (GUI) til visuelt at analysere fleksibilitet og præskriptive modelinstanser. Derudover diskuterer og eksemplificerer afhandlingen (men giver ingen evalueringer af) (1) domæne-specifikke og in-DBMS generiske prognosemetoder der gør det muligt at forudsige instanser af fleksibilitet modeller baseret på historiske data, og (2) kraftfulde måder at analysere tidligere-, nutids- og fremtidsbaserede såkaldte hypotetiske hvad-hvis scenarier og fleksibilitet og præskriptive modelinstanser gemt i en database. De fleste af bidragene på dette niveau er baseret på et smart-grid brugsscenarie. Sammenfattende giver afhandlingen (1) modellen for et CPS med planlægningsmulighed, (2) design og eksperimentel evaluering af præskriptive analyse teknikker der gør det muligt effektivt at forudsige, aggregere, disaggregere, visualisere og analysere komplekse modeller af den fysiske verden, og (3) brugsscenariet fra energiområdet, der viser, hvordan de indførte begreber kan anvendes i den virkelige verden. Vi mener, at dette bidrag udgør et betydeligt skridt i retning af at udvikle CPSer til planlægningsbrug i fremtiden.

Die Anforderungen des Marktes in Bezug auf Flexibilität und Geschwindigkeit, welchen sich Produktionsunternehmen stellen müssen, nehmen immer weiter zu. Die Kunden wollen in jeder Hinsicht individuell bedient werden. Sie wollen das Produkt nach ihren Vorstellungen konfigurieren können, es in kurzer Zeit zur Verfügung gestellt bekommen und trotzdem bei Bedarf individuelle Änderungen integrieren können. Aus diesem Grund muss ein Produktionsunternehmen sowohl in der Planungs- als auch in der Ausführungsphase flexibel auf Änderungen, die vom Kunden ausgelöst werden oder durch interne Ereignisse erforderlich sind, reagieren können. Studien unter produzierenden Unternehmen haben gezeigt, dass im Fall der Notwendigkeit nachträglicher Änderungen das Produktionsplanungs- und steuerungssystem oft nicht miteinbezogen wird, insbesondere dann, wenn die Änderungen sehr prozessnah stattfinden. Da Änderungen jedoch heutzutage keine Seltenheit mehr sind, führt dies dazu, dass die im System gehaltenen Daten häufig von dem tatsächlichen Produkti-onsablauf abweichen. Eine im Rahmen der Arbeit durchgeführte Analyse führender ERP-Systeme und MES hat gezeigt, dass dieses Problem auf Systemunzulänglichkeiten zurückzuführen ist. Auf Basis der Systemuntersuchung schlägt die Arbeit deshalb ein Konzept für ein flexibles Produktionspla-nungs- und -steuerungssystem vor: das PPS II-System. Dieses System soll gewährleisten, dass nachträgliche Änderungen, die durch interne oder externe Ereignisse ausgelöst werden, in die Produktionsplanung integriert werden können. Die Architektur des PPS II-Systems basiert auf der Idee, die starre Trennung zwischen den Planungs- und Steuerungssystemen der Fertigung aufzulösen. Um dies zu gewährleisten, besteht das PPS II-System aus lose gekoppelten Services, deren Zusammenwirken das Verhalten des Systems beschreibt. Die Funktionalität des PPS II-Systems orientiert sich am Konzept der prozessnahen Gestaltungsentscheidung, welches die Ausführungszeit, die zu verwendenden Materialien und die einzusetzenden Ressourcen auf Basis verschiedener Abstraktionen erst unmittelbar vor Produktionsbeginn bestimmt. Eine umfangreiche theoretische und praktische Evaluierung bestätigt, dass das PPS II-System auf diese Weise sehr flexibel auf Änderungen reagieren kann. / Today, production companies face big challenges, in particular with regards to flexibility and speed. Their customers want to be served individually in every respect. They want to configure the product individually and receive it as fast as possible. However, they also want to be able to integrate late changes. For this reason, a production company must be able to react to changes in the planning phase as well as in the production phase in a very flexible way. Such changes might be initiated by the customers or they might be necessary due to internal events. Surveys, which analyzed the processes of production companies, have revealed that their production planning and control systems are often not involved if late changes are necessary. This is particularly true, if the changes occur shortly before the production starts. Therefore, system data deviate from the real production procedure often already in the planning phase. An analysis of leading ERP systems and MES carried out in this thesis has shown that the reason for the problems is the limited flexibility of these systems. On the basis of the analysis, this thesis proposes a new concept for a flexible production and control system: the PPC II system. This system should ensure that late changes triggered by internal or external events can be integrated in the production schedule. The architecture of the PPC II system is based on the idea to eliminate the separation between the planning system and the control system. To reach this goal, the system consists of loosely coupled services that are flexibly orches-trated to control production processes. The PPC II system realizes the concept of late order freeze. Process-relevant decisions for the execution time, the used materials, and the resources are made shortly before production starts. A comprehensive theoretical and practical evaluation verifies that the PPC II system is able to react flexible to changes.

The human brain is equipped with the ability to plan ahead, i.e. to mentally simulate the expected consequences of candidate actions to select the one with the most desirable expected long-term outcome. Insufficient planning can lead to maladaptive behaviour and may even be a contributory cause of important societal problems such as the depletion of natural resources or man-made climate change. Understanding the cognitive and neural mechanisms of forward planning and its regulation are therefore of great importance and could ultimately give us clues on how to better align our behaviour with long-term goals. Apart from its potential beneficial effects, planning is time-consuming and therefore associated with opportunity costs. It is assumed that the brain regulates the investment into planning based on a cost-benefit analysis, so that planning only takes place when the perceived benefits outweigh the costs. But how can the brain know in advance how beneficial or costly planning will be? One potential solution is that people learn from experience how valuable planning would be in a given situation. It is however largely unknown how the brain implements such learning, especially in environments with large state spaces. This dissertation tested the hypothesis that humans construct and use so-called control contexts to efficiently adjust the degree of planning to the demands of the current situation. Control contexts can be seen as abstract state representations, that conveniently cluster together situations with a similar demand for planning. Inferring context thus allows to prospectively adjust the control system to the learned demands of the global context. To test the control context hypothesis, two complex sequential decision making tasks were developed. Each of the two tasks had to fulfil two important criteria. First, the tasks should generate both situations in which planning had the potential to improve performance, as well as situations in which a simple strategy was sufficient. Second, the tasks had to feature rich state spaces requiring participants to compress their state representation for efficient regulation of planning. Participants’ planning was modelled using a parametrized dynamic programming solution to a Markov Decision Process, with parameters estimated via hierarchical Bayesian inference. The first study used a 15-step task in which participants had to make a series of decisions to achieve one or multiple goals. In this task, the computational costs of accurate forward planning increased exponentially with the length of the planning horizon. We therefore hypothesized that participants identify ‘distance from goal’ as the relevant contextual feature to guide their regulation of forward planning. As expected we found that participants predominantly relied on a simple heuristic when still far from the goal but progressively switched towards forward planning when the goal approached. In the second study participants had to sustainably invest a limited but replenishable energy resource, that was needed to accept offers, in order to accumulate a maximum number of points in the long run. The demand for planning varied across the different situations of the task, but due to the large number of possible situations (n = 448) it would be difficult for the participants to develop an expectation for each individual situation of how beneficial planning would be. We therefore hypothesized, that to regulate their forward planning participants used a compressed tasks representation, clustering together states with similar demands for planning. Consistent with this, reaction times (operationalising planning duration) increased with trial-by-trial value-conflict (operationalising approximate planning demand), but this increase was more pronounced in a context with generally high demand for planning. We further found that fMRI activity in the dorsal anterior cingulate cortex (dACC) increased with conflict, but this increase was more pronounced in a context with generally high demand for planning as well. Taken together, the results suggest that the dACC integrates representations of planning demand on different levels of abstraction to regulate prospective information sampling in an efficient and situation-appropriate way. This dissertation provides novel insights into the question how humans adapt their planning to the demands of the current situation. The results are consistent with the view that the regulation of planning is based on an integrated signal of the expected costs and benefits of planning. Furthermore, the results of this dissertation provide evidence that the regulation of planning in environments with real-world complexity critically relies on the brain’s powerful ability to construct and use abstract hierarchical representations.

Sjukvården i Sverige står inför flertalet stora utmaningar i dag. Med en växande och åldrande befolkning ökar vårdbehovet i både komplexitet och volym. Samtidigt är resurserna begränsade vilket leder till att sjukvården måste bli mer effektiv i sitt vårderbjudande. Sjukvården har därför börjat titta på management-teorier från andra branscher, främst bilindustrin. Ambulanta vårdgivare, enheter som utför vårdtjänster utan egna inneliggande patienter, är centrala i produktivitets och effektiviseringsförbättringar i de system de agerar i. Denna studie har undersökt hur forskningens idéer kring produktionsplanering och kontroll kan användas av en ambulant vårdgivare. Detta examensarbete är baserat på en fallstudie genomförd på röntgenavdelningen på Danderyds sjukhus i Stockholms län. Fallstudien har utgjorts av framförallt kvalitativ datainsamling genom semistrukturerade intervjuer understödda av kvantitativ produktionsdata och en benchmarkingstudie på röntgenmottagningen på Universitetssjukhuset i Linköping. Det empiriska bidraget i fallstudien har inkluderat en kartläggning av arbetsprocessen i röntgenmottagningen på Danderyds sjukhus. Processen kunde delas upp i tre faser: 1. Förberedelser, 2. Undersökning, och 3. Diagnosticering och dokumentering. Utifrån den kartlagda processen och en utförlig litteraturstudie om produktionsplanering och kontroll kunde tre huvudsakliga slutsatser dras: (1) implementering av produktionsplanering och kontroll måste utgå från en grundlig förståelse för processerna hos vårdgivaren och syfta till att verka för både kliniska och operationella mål, (2) produktionsplanerings- och kontrollaktiviteter måste anpassas till vårdgivarens omgivning, och (3) kontinuerlig återkoppling från kvalitets och produktivitetsmål är oumbärliga för framgångsrik utnyttjning av produktionsplanerings och kontroll i en ambulant vårdgivare. Detta examensarbete kommer hjälpa ambulanta vårdgivare att möte de utmaningar och produktivitetskrav dessa står inför i Sverige genom att utnyttja potentialen med produktionsplanering och kontroll. / The healthcare sector in Sweden faces several challenges today: a growing and ageing population increases demand in terms of both volume and complexity whereas monetary resources available do not follow suit, forcing care givers to become more efficient in their operation. The healthcare sector has for this reason in recent years become more and more inclined to implement management theories developed in other industries, primarily the automotive industry. Ambulatory care service units, who provide care services on an outpatient basis, are vital in productivity improvements in the system they are acting in but have been somewhat neglected by both policy makers and academia. This study has therefore investigated how production planning and control theories, principles and methods can be utilised in an ambulatory care service unit in a major emergency hospital in Sweden. This thesis is based on a case study conducted at the radiology department at Danderyds sjukhus, an emergency hospital located in Stockholm County Council. The case study mainly consisted of qualitative data gathering using semi-structured interviews with aid from quantitative data on department performance. The empirical contributions of the case study included a process mapping of a generic process flow prevalent in all subunits of the department in the radiology department. The main process could be divided into three main phases: 1. Preparation, 2. Examination, and 3. Diagnosing and documentation. Using the mapped out process and a thorough literature review on production planning and control three main conclusions could be drawn: (1) an implementation of production planning and control should begin with a deep understanding of process flows in the unit and aim to promote both operational and medical objectives, (2) production planning and control activities should be adapted to the environment the unit is acting in, and (3) continuous feedback from performance measurements is vital to successful production planning and control initiatives. This study will help ambulatory care service units to meet the challenge of increased demand they currently face in Sweden by utilising the potential in production planning and control.

<p dir="ltr">Path-tracking control is an integral part of motion planning in autonomous vehicles, in which the vehicle's lateral and longitudinal positions are controlled by a control system that will provide acceleration and steering angle commands to ensure accurate tracking of longitudinal and lateral movements in reference to a pre-defined trajectory. Extensive research has been conducted to address the growing need for efficient algorithms in this area. In this dissertation, a scenario and machine learning-based data-driven control approach is proposed for a path-tracking controller. Firstly, a Deep Reinforcement Learning model is developed to facilitate the control of longitudinal speed. A Deep Deterministic Policy Gradient algorithm is employed as the primary algorithm in training the reinforcement learning model. The main objective of this model is to maintain a safe distance from a lead vehicle (if present) or track a velocity set by the driver. Secondly, a lateral steering controller is developed using Neural Networks to control the steering angle of the vehicle with the main goal of following a reference trajectory. Then, a path-planning algorithm is developed using a hybrid A* planner. Finally, the longitudinal and lateral control models are coupled together to obtain a complete path-tracking controller that follows a path generated by the hybrid A* algorithm at a wide range of vehicle speeds. The state-of-the-art path-tracking controller is also built using Model Predictive Control and Stanley control to evaluate the performance of the proposed model. The results showed the effectiveness of both proposed models in the same scenario, in terms of velocity error, lateral yaw angle error, and lateral distance error. The results from the simulation show that the developed hybrid A* algorithm has good performance in comparison to the state-of-the-art path planning algorithms.</p>