522 research outputs found
Serial-batch scheduling – the special case of laser-cutting machines
The dissertation deals with a problem in the field of short-term production planning, namely the scheduling of laser-cutting machines. The object of decision is the grouping of production orders (batching) and the sequencing of these order groups on one or more machines (scheduling). This problem is also known in the literature as "batch scheduling problem" and belongs to the class of combinatorial optimization problems due to the interdependencies between the batching and the scheduling decisions. The concepts and methods used are mainly from production planning, operations research and machine learning
Mathematical Multi-Objective Optimization of the Tactical Allocation of Machining Resources in Functional Workshops
In the aerospace industry, efficient management of machining capacity is crucial to meet the required service levels to customers and to maintain control of the tied-up working capital. We introduce new multi-item, multi-level capacitated resource allocation models with a medium--to--long--term planning horizon. The model refers to functional workshops where costly and/or time- and resource-demanding preparations (or qualifications) are required each time a product needs to be (re)allocated to a machining resource. Our goal is to identify possible product routings through the factory which minimize the maximum excess resource loading above a given loading threshold while incurring as low qualification costs as possible and minimizing the inventory.In Paper I, we propose a new bi-objective mixed-integer (linear) optimization model for the Tactical Resource Allocation Problem (TRAP). We highlight some of the mathematical properties of the TRAP which are utilized to enhance the solution process. In Paper II, we address the uncertainty in the coefficients of one of the objective functions considered in the bi-objective TRAP. We propose a new bi-objective robust efficiency concept and highlight its benefits over existing robust efficiency concepts. In Paper III, we extend the TRAP with an inventory of semi-finished as well as finished parts, resulting in a tri-objective mixed-integer (linear) programming model. We create a criterion space partitioning approach that enables solving sub-problems simultaneously. In Paper IV, using our knowledge from our previous work we embarked upon a task to generalize our findings to develop an approach for any discrete tri-objective optimization problem. The focus is on identifying a representative set of non-dominated points with a pre-defined desired coverage gap
Deep Reinforcement Learning for Robotic Tasks: Manipulation and Sensor Odometry
Research in robotics has frequently focused on artificial intelligence (AI). To increase the effectiveness of the learning process for the robot, numerous studies have been carried out. To be more effective, robots must be able to learn effectively in a shorter amount of time and with fewer resources. It has been established that reinforcement learning (RL) is efficient for aiding a robot's learning. In this dissertation, we proposed and optimized RL algorithms to ensure that our robots learn well. Research into driverless or self-driving automobiles has exploded in the last few years. A precise estimation of the vehicle's motion is crucial for higher levels of autonomous driving functionality. Recent research has been done on the development of sensors to improve the localization accuracy of these vehicles. Recent sensor odometry research suggests that Lidar Monocular Visual Odometry (LIMO) can be beneficial for determining odometry. However, the LIMO algorithm has a considerable number of errors when compared to ground truth, which motivates us to investigate ways to make it far more accurate. We intend to use a Genetic Algorithm (GA) in our dissertation to improve LIMO's performance. Robotic manipulator research has also been popular and has room for development, which piqued our interest. As a result, we researched robotic manipulators and applied GA to Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER) (GA+DDPG+HER). Finally, we kept researching DDPG and created an algorithm named AACHER. AACHER uses HER and many independent instances of actors and critics from the DDPG to increase a robot's learning effectiveness. AACHER is used to evaluate the results in both custom and existing robot environments.In the first part of our research, we discuss the LIMO algorithm, an odometry estimation technique that employs a camera and a Lidar for visual localization by tracking features from their measurements. LIMO can estimate sensor motion via Bundle Adjustment based on reliable keyframes. LIMO employs weights of the vegetative landmarks and semantic labeling to reject outliers. LIMO, like many other odometry estimating methods, has the issue of having a lot of hyperparameters that need to be manually modified in response to dynamic changes in the environment to reduce translational errors. The GA has been proven to be useful in determining near-optimal values of learning hyperparameters. In our study, we present and propose the application of the GA to maximize the performance of LIMO's localization and motion estimates by optimizing its hyperparameters. We test our approach using the well-known KITTI dataset and demonstrate how the GA supports LIMO to lower translation errors in various datasets. Our second contribution includes the use of RL. Robots using RL can select actions based on a reward function. On the other hand, the choice of values for the learning algorithm's hyperparameters could have a big impact on the entire learning process. We used GA to find the hyperparameters for the Deep Deterministic Policy Gradient (DDPG) and Hindsight Experience Replay (HER). We proposed the algorithm GA+DDPG+HER to optimize learning hyperparameters and applied it to the robotic manipulation tasks of FetchReach, FetchSlide, FetchPush, FetchPick\&Place, and DoorOpening. With only a few modifications, our proposed GA+DDPG+HER was also used in the AuboReach environment. Compared to the original algorithm (DDPG+HER), our experiments show that our approach (GA+DDPG+HER) yields noticeably better results and is substantially faster. In the final part of our dissertation, we were motivated to use and improve DDPG. Many simulated continuous control problems have shown promising results for the DDPG, a unique Deep Reinforcement Learning (DRL) technique. DDPG has two parts: Actor learning and Critic learning. The performance of the DDPG technique is therefore relatively sensitive and unstable because actor and critic learning is a considerable contributor to the robot’s total learning. Our dissertation suggests a multi-actor-critic DDPG for reliable actor-critic learning as an improved DDPG to further enhance the performance and stability of DDPG. This multi-actor-critic DDPG is further combined with HER, called AACHER. The average value of numerous actors/critics is used to replace the single actor/critic in the traditional DDPG approach for improved resistance when one actor/critic performs poorly. Numerous independent actors and critics can also learn from the environment in general. In all the actor/critic number combinations that are evaluated, AACHER performs better than DDPG+HER
Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities
Evolutionary algorithms (EA), a class of stochastic search methods based on
the principles of natural evolution, have received widespread acclaim for their
exceptional performance in various real-world optimization problems. While
researchers worldwide have proposed a wide variety of EAs, certain limitations
remain, such as slow convergence speed and poor generalization capabilities.
Consequently, numerous scholars actively explore improvements to algorithmic
structures, operators, search patterns, etc., to enhance their optimization
performance. Reinforcement learning (RL) integrated as a component in the EA
framework has demonstrated superior performance in recent years. This paper
presents a comprehensive survey on integrating reinforcement learning into the
evolutionary algorithm, referred to as reinforcement learning-assisted
evolutionary algorithm (RL-EA). We begin with the conceptual outlines of
reinforcement learning and the evolutionary algorithm. We then provide a
taxonomy of RL-EA. Subsequently, we discuss the RL-EA integration method, the
RL-assisted strategy adopted by RL-EA, and its applications according to the
existing literature. The RL-assisted procedure is divided according to the
implemented functions including solution generation, learnable objective
function, algorithm/operator/sub-population selection, parameter adaptation,
and other strategies. Finally, we analyze potential directions for future
research. This survey serves as a rich resource for researchers interested in
RL-EA as it overviews the current state-of-the-art and highlights the
associated challenges. By leveraging this survey, readers can swiftly gain
insights into RL-EA to develop efficient algorithms, thereby fostering further
advancements in this emerging field.Comment: 26 pages, 16 figure
Machine learning algorithms for efficient process optimisation of variable geometries at the example of fabric forming
Für einen optimalen Betrieb erfordern moderne Produktionssysteme eine sorgfältige Einstellung der eingesetzten Fertigungsprozesse. Physikbasierte Simulationen können die Prozessoptimierung wirksam unterstützen, jedoch sind deren Rechenzeiten oft eine erhebliche Hürde. Eine Möglichkeit, Rechenzeit einzusparen sind surrogate-gestützte Optimierungsverfahren (SBO1). Surrogates sind recheneffiziente, datengetriebene Ersatzmodelle, die den Optimierer im Suchraum leiten. Sie verbessern in der Regel die Konvergenz, erweisen sich aber bei veränderlichen Optimierungsaufgaben, etwa häufigen Bauteilanpassungen nach Kundenwunsch, als unhandlich.
Um auch solche variablen Optimierungsaufgaben effizient zu lösen, untersucht die vorliegende Arbeit, wie jüngste Fortschritte im Maschinenlernen (ML) – im Speziellen bei neuronalen Netzen – bestehende SBO-Techniken ergänzen können. Dabei werden drei Hauptaspekte betrachtet: erstens, ihr Potential als klassisches Surrogate für SBO, zweitens, ihre Eignung zur effiziente Bewertung der Herstellbarkeit neuer Bauteilentwürfe und drittens, ihre Möglichkeiten zur effizienten Prozessoptimierung für variable Bauteilgeometrien. Diese Fragestellungen sind grundsätzlich technologieübergreifend anwendbar und werden in dieser Arbeit am Beispiel der Textilumformung untersucht.
Der erste Teil dieser Arbeit (Kapitel 3) diskutiert die Eignung tiefer neuronaler Netze als Surrogates für SBO. Hierzu werden verschiedene Netzarchitekturen untersucht und mehrere Möglichkeiten verglichen, sie in ein SBO-Framework einzubinden. Die Ergebnisse weisen ihre Eignung für SBO nach: Für eine feste Beispielgeometrie minimieren alle Varianten erfolgreich und schneller als ein Referenzalgorithmus (genetischer Algorithmus) die Zielfunktion.
Um die Herstellbarkeit variabler Bauteilgeometrien zu bewerten, untersucht Kapitel 4 anschließend, wie Geometrieinformationen in ein Prozess-Surrogate eingebracht werden können. Hierzu werden zwei ML-Ansätze verglichen, ein merkmals- und ein rasterbasierter Ansatz. Der merkmalsbasierte Ansatz scannt ein Bauteil nach einzelnen, prozessrelevanten Geometriemerkmalen, der rasterbasierte Ansatz hingegen interpretiert die Geometrie als Ganzes. Beide Ansätze können das Prozessverhalten grundsätzlich erlernen, allerdings erweist sich der rasterbasierte Ansatz als einfacher übertragbar auf neue Geometrievarianten. Die Ergebnisse zeigen zudem, dass hauptsächlich die Vielfalt und weniger die Menge der Trainingsdaten diese Übertragbarkeit bestimmt.
Abschließend verbindet Kapitel 5 die Surrogate-Techniken für flexible Geometrien mit variablen Prozessparametern, um eine effiziente Prozessoptimierung für variable Bauteile zu erreichen. Hierzu interagiert ein ML-Algorithmus in einer Simulationsumgebung mit generischen Geometriebeispielen und lernt, welche Geometrie, welche Umformparameter erfordert. Nach dem Training ist der Algorithmus in der Lage, auch für nicht-generische Bauteilgeometrien brauchbare Empfehlungen auszugeben. Weiter zeigt sich, dass die Empfehlungen mit ähnlicher Geschwindigkeit wie die klassische SBO zum tatsächlichen Prozessoptimum konvergieren, jedoch kein bauteilspezifisches A-priori-Sampling nötig ist. Einmal trainiert, ist der entwickelte Ansatz damit effizienter.
Insgesamt zeigt diese Arbeit, wie ML-Techniken gegenwärtige SBOMethoden erweitern und so die Prozess- und Produktoptimierung zu frühen Entwicklungszeitpunkten effizient unterstützen können. Die Ergebnisse der Untersuchungen münden in Folgefragen zur Weiterentwicklung der Methoden, etwa die Integration physikalischer Bilanzgleichungen, um die Modellprognosen physikalisch konsistenter zu machen
What does explainable AI explain?
Machine Learning (ML) models are increasingly used in industry, as well as in scientific research and social contexts. Unfortunately, ML models provide only partial solutions to real-world problems, focusing on predictive performance in static environments. Problem aspects beyond prediction, such as robustness in employment, knowledge generation in science, or providing recourse recommendations to end-users, cannot be directly tackled with ML models.
Explainable Artificial Intelligence (XAI) aims to solve, or at least highlight, problem aspects beyond predictive performance through explanations. However, the field is still in its infancy, as fundamental questions such as “What are explanations?”, “What constitutes a good explanation?”, or “How relate explanation and understanding?” remain open. In this dissertation, I combine philosophical conceptual analysis and mathematical formalization to clarify a prerequisite of these difficult questions, namely what XAI explains: I point out that XAI explanations are either associative or causal and either aim to explain the ML model or the modeled phenomenon. The thesis is a collection of five individual research papers that all aim to clarify how different problems in XAI are related to these different “whats”.
In Paper I, my co-authors and I illustrate how to construct XAI methods for inferring associational phenomenon relationships. Paper II directly relates to the first; we formally show how to quantify uncertainty of such scientific inferences for two XAI methods – partial dependence plots (PDP) and permutation feature importance (PFI). Paper III discusses the relationship between counterfactual explanations and adversarial examples; I argue that adversarial examples can be described as counterfactual explanations that alter the prediction but not the underlying target variable. In Paper IV, my co-authors and I argue that algorithmic recourse recommendations should help data-subjects improve their qualification rather than to game the predictor. In Paper V, we address general problems with model agnostic XAI methods and identify possible solutions
Toward circularity : life cycle-based approach in waste management
Our current “throwaway” lifestyle places great strain on the environment; resources that enter the economy remain for only a short period and are quickly disposed of. This dissertation aims to evaluate the economic and environmental impacts of shifting toward more circular economy (CE) practices that advocate value retention for as long as possible within the economy. The research was carried out by conceptualizing CE and solving real cases focusing on the product end-of-life (EoL) stage. Life cycle assessment (LCA) was the main tool used to assess environmental impacts of different circular scenarios. The tool was paired with life cycle costing (LCC) to evaluate economic performances. Three cases in Finland were assessed: shifting toward source-separated biowaste collection, establishing an agricultural plastics waste recycling system, and waste-to-energy optimization. It was found that CE covers multiple aspects within the value chain; thus, its adoption model can occur at any stage of the value chain, thereby enabling various stakeholders to be more circular through different actions. The cases suggested that being more circular at the EoL stage may improve value retention through secondary material production, waste treatment by-products, and energy recovery. Shifting toward circularity was shown to be economically and environmentally viable. The dissertation illustrated the importance of stakeholders’ collaboration because a circular approach could affect all actors within the supply chain, including manufacturing, the energy sector, and society. The study showed that it is important to quantify environmental impacts of products or services, and to date, LCA remains the most suitable tool for quantifying results and evaluating options. In addition, a combination with LCC will provide more comprehensive results to anticipate any trade-off between environmental and economic aspects. CE must start somewhere, so let it start with organizations evaluating their environmental performance to identify better alternatives, define targets, and foster circularity in the long run.Nykyinen kertakäyttöelämäntapa aiheuttaa painetta ympäristölle. Monia raaka-aineita, joita käytetään taloudessa, hyödynnetään vain lyhyen aikaa ja hävitetään nopeasti. Tämän väitöskirjan tavoitteena on arvioida taloudellisia ja ympäristövaikutuksia yritysten siirtymisessä kohti kiertotalouden (CE) käytäntöjä, joiden avulla pyritään arvon säilyttämiseen mahdollisimman pitkään. Tutkimus toteutettiin tarkastelemalla kiertotalouden käsitteitä ja esittämällä ratkaisumalleja tapaustutkimuksiin, joissa keskityttiin tuotteen elinkaaren loppuvaiheeseen (EoL). Elinkaariarviointi (LCA) oli näissä tärkein työkalu erilaisten kiertoskenaarioiden ympäristövaikutusten arvioinnissa. Tämä työkalu yhdistettiin elinkaarikustannuslaskentaan (LCC) taloudellisen suorituskyvyn arvioimiseksi. Kolme tapaustutkimusta toteutettiin Suomessa: (1) siirtyminen biojätteen lajittelukeräykseen, (2) maatalouden muovijätteen kierrätysjärjestelmän suunnittelu ja (3) jätteen energian optimointi. Tulokset osoittivat, että kiertotalouden avulla voidaan kattaa useita arvoketjun näkökohtia; käyttöönotto voidaan toteuttaa millä tahansa arvoketjun tasolla, ja eri sidosryhmät voivat lisätä kiertoa eri toimien kautta. Tulokset viittaavat siihen, että kierron lisääminen EoL-vaiheessa voisi parantaa arvon säilyttämistä uusiomateriaalituotannon, jätteenkäsittelyn sivutuotteiden ja energian talteenoton avulla. Tyyppitapausten perusteella yritysten siirtyminen kiertotalouskäytäntöihin osoittautui sekä taloudellisesti ja ympäristön kannalta kannattavaksi. Työn tulokset ovat havainnollistaneet sidosryhmien yhteistyön tärkeyttä. Kierron rakentaminen voi vaikuttaa kaikkiin toimitusketjun toimijoihin, mukaan lukien valmistus, energiantuotanto ja yhteiskunta laajemmin. Tutkimus osoitti, että tuotteiden tai palveluiden ympäristövaikutusten kvantitatiivinen mittaaminen on tärkeää, ja LCA on edelleen sopivin väline tulosten kvantifiointiin ja erilaisten vaihtoehtojen keskinäiseen arviointiin. Elinkaarilaskelmaan yhdistettynä elinkaarikustannuslaskentaan saadaan aikaan kattavampia tuloksia, joilla voidaan vertailla ympäristö- ja talousnäkökohtien mahdollisia ristiriitoja. Kiertotaloustyö on aloitettava jostain, ja se voi alkaa siitä, että organisaatiot mittaavat ympäristötehokkuuttaan rakentaakseen parempia vaihtoehtoja, määritelläkseen tavoitteitaan ja edistääkseen kiertojen kehittymistä pitkällä aikavälillä.fi=vertaisarvioitu|en=peerReviewed
Framework para a aplicação de algoritmos genéticos a sistemas avançados de planejamento e programação da produção (APS)
Orientador: Prof. Dr. Fernando DeschampsCoorientador: Prof. Dr. Alessandro MarquesDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Manufatura. Defesa : Curitiba, 30/03/2023Inclui referências: p. 117-122Resumo: O planejamento e a programação são funções essenciais para que os sistemas industriais operem de maneira eficaz, visando atender às demandas de produção dentro do prazo estabelecido. Diversos estudos na literatura propõem modelos matemáticos de Sistemas Avançados de Planejamento e Programação da Produção (APS) para diferentes cenários, levando em conta configurações variadas de recursos e limitações. No entanto, somente recentemente os APS, que utilizam esses modelos, têm sido amplamente discutidos e utilizados, mas ainda não exploraram completamente os conceitos de digitalização e Indústria 4.0. Este trabalho tem como objetivo desenvolver um framework de um Sistemas Avançados de Planejamento e Programação da Produção para o ambiente de manufatura, utilizando sistemas computacionais de inteligência artificial e aplicando conceitos da Indústria 4.0. Inicialmente, é apresentada uma revisão sistemática da literatura realizada por meio da aplicação do método ProKnow-C em sistemas APS. Em seguida, é mostrado o desenvolvimento de um MVP (Minimum Viable Product) de um sistema de APS por meio da abordagem Design Science Research (DSR), buscando gerar mais flexibilidade e autonomia para estes sistemas, dentro dos conceitos de Indústria 4.0. A abordagem DSR foi explorada em suas etapas, resultando no desenvolvimento de um framework para um sistema comercial de APS, com a aplicação do Algoritmo Genético de Classificação Não Dominada II (NSGA-II). São apresentadas as definições das funções objetivas, um estudo sobre os principais sistemas de APS disponíveis no mercado, a estruturação do sistema, o detalhamento do algoritmo no back-end e os resultados obtidos na simulação por meio do desenvolvimento de protótipos e frontend. Com a utilização da metodologia DSR e a aplicação do algoritmo NSGA-II, busca-se contribuir para o desenvolvimento de sistemas de APS mais eficazes e inovadores, que atendam às demandas da Indústria 4.0.Abstract: Planning and scheduling are essential functions for industrial systems to operate effectively, aiming to meet production demands within established deadlines. Several studies in the literature propose mathematical models of Advanced Planning and Scheduling (APS) Systems for different scenarios, taking into account various configurations of resources and limitations. However, only recently have APS systems using these models been widely discussed and used, but they have not fully explored the concepts of digitization and Industry 4.0. This work aims to develop a framework for an Advanced Planning and Scheduling System for the manufacturing environment, using artificial intelligence computational systems and applying concepts of Industry 4.0. Initially, a systematic literature review is presented through the ProKnow-C method applied to APS systems. Then, the development of an MVP (Minimum Viable Product) of an APS system is shown through the Design Science Research (DSR) approach, seeking to generate more flexibility and autonomy for these systems within the Industry 4.0 concepts. The DSR approach was explored in its stages, resulting in the development of a framework for a commercial APS system, with the application of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Objective function definitions, a study on the main APS systems available in the market, system structuring, algorithm detailing in the back-end, and results obtained in the simulation through the development of prototypes and front-end are presented. By using the DSR methodology and applying the NSGA-II algorithm, we aim to contribute to the development of more effective and innovative APS systems that meet the demands of Industry 4.0
Bio-inspired optimization in integrated river basin management
Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM.
In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin.
Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices.
It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms
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