När ett tillverkande företag vill öka sin marknadsandel behöver de i allmänhet öka sin produktion. För att kunna göra detta på ett kostnadseffektivt sätt är det viktigt att veta vilka faktorer som begränsar produktionssystemet, dessa benämns ofta som flaskhalsar. Ett produktionssystem är dock inte ett statiskt system vilket innebär att flaskhalsarna i ett system inte heller är statiska. De kan flytta på sig på både lång och kort sikt.   Syftet med denna fallstudie är att undersöka flaskhalsarna i ett produktionssystem och hur dessa förflyttar sig, vilka förbättringar som kan göras för att förbättra flödet i produktionssystemet. Data om produktionssystemet har samlats in med studier av databas, tidsstudie och samtal/intervjuer. Dessa data har nyttjats i en simuleringsmodell som framtagits med hjälp av FACTS-analyzer. Denna modell har studerats samt använts för att genomföra experiment. Bland annat har flermålsoptimering med SCORE metoden använts för att hitta systemets primära och sekundära flaskhalsar.   Studien visade att en station på svetsavdelningen var den huvudsakliga flaskhalsen i produktionssystemet och att den sekundära flaskhalsen i systemet låg i avdelningen efter, måleriavdelningen. Studien tog också fram en optimerad förbättrings plan för fabriken upp till 260 tillverkade båtar på ett år. / When a manufacturing company intends to increase their market share, they generally need to increase their production as well. In order to achieve this in a cost-effective manner they must be aware of which factors are limiting the production system, these factors are generally known as bottlenecks. A production system is however not a static system, this means that the bottlenecks won’t be static either. They can move both in the long term and short term.   The aim of this case study is to examine the bottlenecks in a system and their movement. As well as which improvements could be applied in order to improve the flow in the production system. Data concerning the production system has been collected through studies of internal databases, time studies and interviews. This data has been used in a simulation which has been constructed with FACTS-analyzer. The model has been studied and used for experimentation, for example, multigoal-optimization with the SCORE-method, which has been used in order to find the primary and secondary bottlenecks of the system.   The study shows that a station in the welding department was the main bottleneck in the productions system and the secondary bottleneck is in the department after, the paint shop. The study also found an optimized improvement plan for the factory up to 260 boats produced each year

As a novel beyond fifth-generation (5G) concept, cell-free massive MIMO (multiple-input multiple-output) recently has become a promising physical-layer technology where an enormous number of distributed access points (APs), coordinated by a central processing unit (CPU), cooperate to coherently serve a large number of user equipments (UEs) in the same time/frequency resource. However, denser AP deployment in cell-free networks as well as an exponentially growing number of mobile UEs lead to higher power consumption. What is more, similar to conventional cellular networks, cell-free massive MIMO networks are dimensioned to provide the required quality of service (QoS) to the UEs under heavy traffic load conditions, and thus they might be underutilized during low traffic load periods, leading to inefficient use of both spectral and energy resources. Aiming at the implementation of energy-efficient cell-free networks, several approaches have been proposed in the literature, which consider different AP switch ON/OFF (ASO) strategies for power minimization. Different from prior works, this thesis focuses on additional factors other than ASO that have an adverse effect not only on total power consumption but also on implementation complexity and operation cost. For instance, too frequent ON/OFF switching in an AP can lead to tapering off the potential power saving of ASO by incurring extra power consumption due to excessive switching. Indeed, frequent switching of APs might also result in thermal fatigue and serious lifetime degeneration. Moreover, time variations in the AP-UE association in favor of energy saving in a dynamic network bring additional signaling and implementation complexity. Thus, in the first part of the thesis, we propose a multi-objective optimization problem that aims to minimize the total power consumption together with AP switching and AP-UE association variations in comparison to the state of the network in the previous state. The proposed problem is cast in mixed integer quadratic programming form and solved optimally. Our simulation results show that by limiting AP switching (node switching) and AP-UE association reformation switching (link switching), the total power consumption from APs only slightly increases but the number of average switching drops significantly regardless of node switching or link switching. It achieves a good balance on the trade-off between radio power consumption and the side effects excessive switching will bring. In the second part of the thesis, we consider a larger cell-free massive MIMO network by dividing the total area into disjoint network-centric clusters, where the APs in each cluster are connected to a separate CPU. In each cluster, cell-free joint transmission is locally implemented to achieve a scalable network implementation. Motivated by the outcomes of the first part, we reshape our dynamic network simulator to keep the active APs for a given spatial traffic pattern the same as long as the mean arrival rates of the UEs are constant. Moreover, the initially formed AP-UE association for a particular UE is not allowed to change. In that way, we make the number of node and link switching zero throughout the considered time interval. For this dynamic network, we propose a deep reinforcement learning (DRL) framework that learns the policy of maximizing long-term energy efficiency (EE) for a given spatially-varying traffic density. The active AP density of each network-centric cluster and the boundaries of the clusters are learned by the trained agent to maximize the EE. The DRL algorithm is shown to learn a non-trivial joint cluster geometry and AP density with at least 7% improvement in terms of EE compared to the heuristically-developed benchmarks. / Som ett nytt koncept bortom den femte generationen (5G) har cellfri massiv MIMO (multiple input multiple output) nyligen blivit en lovande teknik för det fysiska lagret där ett enormt antal distribuerade åtkomstpunkter (AP), som samordnas av en central processorenhet (CPU), samarbetar för att på ett sammanhängande sätt betjäna ett stort antal användarutrustningar (UE) i samma tids- och frekvensresurs. En tätare utplacering av AP:er i cellfria nät samt ett exponentiellt växande antal mobila användare leder dock till högre energiförbrukning. Dessutom är cellfria massiva MIMO-nät, i likhet med konventionella cellulära nät, dimensionerade för att ge den erforderliga tjänstekvaliteten (QoS) till enheterna under förhållanden med hög trafikbelastning, och därför kan de vara underutnyttjade under perioder med låg trafikbelastning, vilket leder till ineffektiv användning av både spektral- och energiresurser. För att genomföra energieffektiva cellfria nät har flera metoder föreslagits i litteraturen, där olika ASO-strategier (AP switch ON/OFF) beaktas för att minimera energiförbrukningen. Till skillnad från tidigare arbeten fokuserar den här avhandlingen på andra faktorer än ASO som har en negativ effekt inte bara på den totala energiförbrukningen utan också på komplexiteten i genomförandet och driftskostnaden. Till exempel kan alltför frekventa ON/OFF-omkopplingar i en AP leda till att ASO:s potentiella energibesparingar avtar genom extra energiförbrukning på grund av överdriven omkoppling. Frekventa omkopplingar av AP:er kan också leda till termisk trötthet och allvarlig försämring av livslängden. Dessutom medför tidsvariationer i AP-UE-associationen till förmån för energibesparingar i ett dynamiskt nät ytterligare signalering och komplexitet i genomförandet. I den första delen av avhandlingen föreslår vi därför ett optimeringsproblem med flera mål som syftar till att minimera den totala energiförbrukningen tillsammans med växling av AP och variationer i AP-UE-associationen i jämförelse med nätets tillstånd i det föregående läget. Det föreslagna problemet är en blandad helhetsmässig kvadratisk programmering och löses optimalt. Våra simuleringsresultat visar att genom att begränsa växling av AP (node switching) och växling av AP-UE-association (link switching) ökar den totala energiförbrukningen från AP:erna endast något, men antalet genomsnittliga växlingar ökar, oavsett om det rör sig om node switching eller link switching. Det ger en bra balans mellan radiokraftförbrukning och de bieffekter som överdriven växling medför. I den andra delen av avhandlingen tar vi hänsyn till ett större cellfritt massivt MIMO-nätverk genom att dela upp det totala området i disjunkta nätverkscentrerade kluster, där AP:erna i varje kluster är anslutna till en separat CPU. I varje kluster genomförs cellfri gemensam överföring lokalt för att uppnå en skalbar nätverksimplementering. Motiverat av resultaten i den första delen omformar vi vår dynamiska nätverkssimulator så att de aktiva AP:erna för ett givet rumsligt trafikmönster är desamma så länge som den genomsnittliga ankomsthastigheten för de enskilda enheterna är konstant. Dessutom tillåts inte den ursprungligen bildade AP-UE-associationen för en viss användare att förändras. På så sätt gör vi antalet nod- och länkbyten till noll under hela det aktuella tidsintervallet. För detta dynamiska nätverk föreslår vi ett ramverk för djup förstärkningsinlärning (DRL) som lär sig en strategi för att maximera energieffektiviteten på lång sikt för en given rumsligt varierande trafiktäthet. Den aktiva AP-tätheten i varje nätverkscentrerat kluster och klustrens gränser lärs av den utbildade agenten för att maximera EE. Det visas att DRL-algoritmen lär sig en icke-trivial gemensam klustergeometri och AP-täthet med minst 7% förbättring av EE jämfört med de heuristiskt utvecklade riktmärkena.

<ul> <li>Energy Optimization Stategies</li> <li>Hydraulic Models for Water Distribution Systems</li> <li>Heuristic Multi-objective Optimization Algorithms</li> <li>Multi-objective Optimization Problems</li> <li>System Constraints</li> <li>Encoding Techniques</li> <li>Optimal Pumping Operations</li> <li>Sovling Real-World Optimization Problems </li> </ul> / <p>The water supply industry is a very important element of a modern economy; it represents a key element of urban infrastructure and is an integral part of our modern civilization. Billions of dollars per annum are spent internationally in pumping operations in rural water distribution systems to treat and reliably transport water from source to consumers.</p> <p>In this dissertation, a new multi-objective optimization approach referred to as energy optimization strategy is proposed for minimizing electrical energy consumption for pumping, the cost, pumps maintenance cost, and the cost of maximum power peak, while optimizing water quality and operational reliability in rural water distribution systems. Minimizing the energy cost problem considers the electrical energy consumed for regular operation and the cost of maximum power peak. Optimizing operational reliability is based on the ability of the network to provide service in case of abnormal events (e.g., network failure or fire) by considering and managing reservoir levels. Minimizing pumping costs also involves consideration of network and pump maintenance cost that is imputed by the number of pump switches. Water quality optimization is achieved through the consideration of chlorine residual during water transportation.</p> <p>An Adaptive Parallel Clustering-based Multi-objective Particle Swarm Optimization (APC-MOPSO) algorithm that combines the existing and new concept of Pareto-front, operating-mode specification, selecting-best-efficiency-point technique, searching-for-gaps method, and modified K-Means clustering has been proposed. APC-MOPSO is employed to optimize the above-mentioned set of multiple objectives in operating rural water distribution systems.</p> <p>Saskatoon West is, a rural water distribution system, owned and operated by Sask-Water (i.e., is a statutory Crown Corporation providing water, wastewater and related services to municipal, industrial, government, and domestic customers in the province of Saskatchewan). It is used to provide water to the city of Saskatoon and surrounding communities. The system has six main components: (1) the pumping stations, namely Queen Elizabeth and Aurora; (2) The raw water pipeline from QE to Agrium area; (3) the treatment plant located within the Village of Vanscoy; (4) the raw water pipeline serving four major consumers, including PCS Cogen, PCS Cory, Corman Park, and Agrium; (5) the treated water pipeline serving a domestic community of Village of Vanscoy; and (6) the large Agrium community storage reservoir.</p> <p>In this dissertation, the Saskatoon West WDS is chosen to implement the proposed energy optimization strategy. Given the data supplied by Sask-Warer, the scope of this application has resulted in savings of approximately 7 to 14% in energy costs without adversely affecting the infrastructure of the system as well as maintaining the same level of service provided to the Sask-Water’s clients.</p> <p>The implementation of the energy optimization strategy on the Saskatoon West WDS over 168 hour (i.e., one-week optimization period of time) resulted in savings of approximately 10% in electrical energy cost and 4% in the cost of maximum power peak. Moreover, the results showed that the pumping reliability is improved by 3.5% (i.e., improving its efficiency, head pressure, and flow rate). A case study is used to demonstrate the effectiveness of the multi-objective formulations and the solution methodologies, including the formulation of the system-operational optimization problem as five objective functions. Beside the reduction in the energy costs, water quality, network reliability, and pumping characterization are all concurrently enhanced as shown in the collected results. The benefits of using the proposed energy optimization strategy as replacement for many existing optimization methods are also demonstrated.</p> / Doctor of Science (PhD)

This research effort develops a comprehensive computational framework to support the parametric optimal design of uncertain dynamical systems. Uncertainty comes from various sources, such as: system parameters, initial conditions, sensor and actuator noise, and external forcing. Treatment of uncertainty in design is of paramount practical importance because all real-life systems are affected by it; not accounting for uncertainty may result in poor robustness, sub-optimal performance and higher manufacturing costs. Contemporary methods for the quantification of uncertainty in dynamical systems are computationally intensive which, so far, have made a robust design optimization methodology prohibitive. Some existing algorithms address uncertainty in sensors and actuators during an optimal design; however, a comprehensive design framework that can treat all kinds of uncertainty with diverse distribution characteristics in a unified way is currently unavailable. The computational framework uses Generalized Polynomial Chaos methodology to quantify the effects of various sources of uncertainty found in dynamical systems; a Least-Squares Collocation Method is used to solve the corresponding uncertain differential equations. This technique is significantly faster computationally than traditional sampling methods and makes the construction of a parametric optimal design framework for uncertain systems feasible. The novel framework allows to directly treat uncertainty in the parametric optimal design process. Specifically, the following design problems are addressed: motion planning of fully-actuated and under-actuated systems; multi-objective robust design optimization; and optimal uncertainty apportionment concurrently with robust design optimization. The framework advances the state-of-the-art and enables engineers to produce more robust and optimally performing designs at an optimal manufacturing cost. / Ph. D.

During the assembly process of battery packs for electric vehicles, it is crucial to ensure that the cell-to-busbar joints can be produced with high quality, good reliability, and with minimal impact on the individual battery cells. This thesis project investigates the influence of different process parameters on the joint quality for laser welded dissimilar material cell-to-busbar joints. Nickel plated copper and steel plates, joined in an overlap configuration, are used as a simplified geometry, representing a cell-to-busbar joint. By the utilization of artificial neural network-based meta models, trained on numerical results from computational fluid dynamics simulations of the laser welding process, the joint quality is predicted and evaluated. The present thesis investigates how a set of optimized process parameters can be identified for the considered laser welded dissimilar material cell-to-busbar joints, in order to simultaneously maximize the interface width for the joints, and minimize the formation of undercuts and resulting in-process temperatures. NSGA-II is used to efficiently search for trade-off solutions, in an meta model-based multi-objective optimization approach, where the meta models are used to approximate the objectives, corresponding to the joint quality obtained from computational fluid dynamics simulations. With this, the time for one objective evaluation is reduced from approximately 9 hours, when the objectives are evaluated directly from computational fluid dynamics simulations, to only tenths of a second. With the proposed optimization approach, the Pareto-optimal front of trade-off solutions is identified, leading to the selection of three optimal solutions for validation. The validity of the proposed optimization approach, and the selected optimal solutions, are confirmed by means of both physical laser welding experiments and computational fluid dynamics simulations. It is shown that the selected optimal solutions, corresponding to three parameter setups, can be used to produce joints with large interface width and low in-process temperatures, without achieving a full penetration in the lower plate of the joint.

The use of simulation programs in production environments is crucial due to the many advantages they offer, such as facilitating calculations of production capacity, cycle times, and optimal buffer sizes. These tools allow for the simulation of various scenarios and cost-effective testing of different strategies, making them invaluable for companies striving to improve production and performance.  The purpose of this work is to conduct a case study using FACTS Analyzer and then compare the results with another simulation software that supports 3D simulations. By conducting the same case study in both programs, we can draw conclusions about the factors companies should consider when acquiring simulation software. The data will be collected through qualitative methods such as observations and interviews, complemented by quantitative methods through experiments within the case study. The results show that FACTS Analyzer and the chosen program Plant Simulation serve different purposes but within similar areas. FACTS Analyzer offers a simple and effective 2D simulation experience that is particularly useful for students and companies planning production. On the other hand, Plant Simulation provides extensive capabilities to model everything from individual production lines to entire facilities in a clear 3D environment, making it easier for people without in-depth knowledge of the field to understand the simulations. In conclusion, the study shows that both programs can complement each other by offering unique capabilities in different domains, providing companies with a competitive advantage by optimizing their production and planning processes.

Les Partenariats Publics Privés (PPPs) peuvent être un outil efficace pour optimiser et moderniser la commande publique dans un contexte où les besoins en investissement public ne cessent d’accroître. Les fréquences importantes de renégociation et les difficultés à estimer correctement les revenus futurs demeurent un défi majeur lors de la structuration financière des PPPs. Ce travail propose d’incorporer des clauses financières flexibles afin de remédier à ce problème. L’approche développée se base sur les théories d’options réelles et d’optimisation multi-objectif. Dans un premier temps, une méthodologie adéquate pour la gestion des risques est développée. La volatilité du projet est déterminée par le biais de la simulation de Monte Carlo et un déflateur stochastique est introduit afin de conduire les différentes valorisations d’options sous la probabilité historique. Ce travail développe dans un second temps, quatre formes de flexibilité qui permettent de réajuster l'équilibre financier du projet, si le revenu est insuffisant. Enfin une approche d’optimisation multi-objectif est développée afin de permettre de visualiser les différents compromis auxquels l’introduction de la flexibilité donne lieu. / Public private partnerships can be a solution to the dilemma of how to do more with less available funds that public entities are constantly financing in the last decades. If implemented properly, Public Private Partnerships can contribute to the modernization of public service provision and can constitute efficient vehicles for the delivery of optimal value for money. The high incidence of renegotiation as well as the difficulty of accurately predicting the future demand on the projects is a matter of concern when it comes to the financial structuring of Public Private Partnerships. This work proposes a real-option- based optimization framework to boost the financial viability of the projects. This is done by introducing flexible financial clauses. First, an adequate framework for risk management, where volatility is derived by Monte Carlo simulation and the valuation is made without switching to the risk neutral measure, is presented. Four families of flexible clauses are, afterwards, investigated. Such clauses are triggerred, if the revenue level of the projet is not sufficient to guarnatee its financiel viability. Finally, this work develops a multi-objective optimization approach in order to assess the different trade-offs that the introduction of flexibility leads to. The proposed optimization problem is solved via multi-objective evolutionary algorithms.

Cette thèse présente le dimensionnement d’une machine électrique à rotor disque pour la traction de véhicules hybrides. Un état de l’art complet sur les machines électriques à rotor disque permet de montrer que la machine à flux axial à rotor central est la plus adaptée. Différentes géométries du circuit magnétique sont successivement étudiées et comparées par simulations par éléments finis. Pour maximiser le rendement de la machine, les pertes d’origine électromagnétique sont étudiées. Un modèle analytique des pertes par effet de peau dans les conducteurs de cuivre est proposé et validé par éléments finis. Un second modèle analytique estime les pertes par courant induits dans les aimants permanents pour tous les points de travail de la machine se basant sur uniquement trois simulations par éléments finis permettant ainsi un gain de temps important lors du dimensionnement. Deux méthodes de dimensionnement sont comparées : le dimensionnement manuel par essais/erreurs et l’optimisation multi-objectifs. Cette dernière méthode automatise le dimensionnement et permet une optimisation plus pointue et une forte amélioration des performances. Dans ce cas, la densité de couple a par exemple été augmentée de 29%. La machine à flux axial dimensionnée est comparée avec une machine à flux radial pour véhicules hybrides. Cette comparaison révèle le potentiel de la machine à flux axial avec notamment une densité de couple augmentée de plus de 20%. Enfin, deux prototypes ont été construits et mesurés pour valider les simulations. / This PhD presents the design of a disc rotor electric machine for the traction of hybrid vehicles. A complete state of the art enables the selection of the internal rotor axial flux machine which is the most suited to this application. Different geometries of the magnetic circuit are successively studied and compared with finite elements simulations. To maximize the efficiency of the machine losses generated in the magnetic circuit are studied. An analytic model on the skin effect in the copper conductors is proposed and validated with finite elements simulations. A second analytic model estimates the eddy current losses in the permanent magnets for every operating point of the machine based on only three finite element simulations enabling an important time-saving. Two dimensioning methods are compared: the manual dimensioning based on a tries/errors method and the multi-objectives optimization. This last method automates the dimensioning and enables a more refined optimization and a strong improvement of the performances. For example, the torque density has been improved by 29% in that case. The designed axial flux machine is compared to a state of the art radial flux machine for hybrid vehicle. This comparison shows the potential of the axial flux machine with an improvement of the torque density by more than 20%. Finally two prototypes have been built and measured to validate the simulations.

Η πρόσφατη τεχνολογική ανάπτυξη των οπτικών ενισχυτών, πολυπλεκτών/αποπλεκτών, οπτικών διακοπτών καθώς και άλλων οπτικών συσκευών μας οδηγεί στο να ελπίζουμε ότι σύντομα στο μέλλον θα υλοποιηθεί ένα πλήρες οπτικό (all optical), WDM (wavelength division multiplexing) δίκτυο που να ικανοποιεί και την ανάγκη για μεγάλα μεγέθη χωρητικότητας. Σε ένα τέτοιο δίκτυο η μετατροπή του οπτικού σήματος σε ηλεκτρονικό και εκ νέου στο οπτικό (ΟΕΟ) δεν θα χρησιμοποιείται στους ενδιάμεσους κόμβους, και αυτό συμβάλει σε οικονομικότερες υλοποιήσεις των οπτικών δικτύων. Σε ένα WDM δρομολογούμενο δίκτυο, τα δεδομένα μεταφέρονται μέσω ενός οπτικού καναλιού, lightpath, στους κόμβους του δικτύου που συνδέονται με οπτικές ίνες. Στις πλείστες των περιπτώσεων, κατά την άφιξη ενός lightpath σε κάποιο κόμβο, εφαρμόζεται σε αυτό οπτικό-ηλεκτρονική μετατροπή και αντίστροφα, ούτως ώστε το σήμα να αναδημιουργηθεί λόγω των απωλειών που υπέστη κατά την μεταφορά, ή ακόμη για να αναλυθεί από ενδιάμεσες ηλεκτρονικές συσκευές. Στα μη πλήρη οπτικά δίκτυα, η μεταφορά των δεδομένων γίνεται από κόμβο σε κόμβο κατά μήκος του δικτύου, ούτως ώστε το οπτικό σήμα να ενισχύεται και να αναγεννάτε μέσω της OEO επεξεργασίας. Παρ’ όλα αυτά, η κάθε ενδιάμεση ανάλυση του θέματος σε ένα τέτοιο δίκτυο προϋποθέτει πολύ μεγάλα κόστη λόγω των πολλών συσκευών που απαιτούνται για τη OEO επεξεργασία. Το γεγονός αυτό μας οδηγεί στα ημί-πλήρη δίκτυα όπου η ενίσχυση και αναγέννηση του θέματος δε γίνεται σε όλους τους ενδιάμεσους κόμβους αλλά σε μερικούς από αυτούς. Ο τελικός στόχος όμως είναι η απαλοιφή της ηλεκτρονικής μετατροπής και αυτό οδηγεί στην υλοποίηση των πλήρως οπτικών δικτύων. Στα πλήρη οπτικά δίκτυα, ένα σήμα που μεταδίδεται παραμένει, για όλο το lightpath, στο οπτικό επίπεδο. Έτσι, το πλήρες οπτικό δίκτυο μπορεί να απαλείψει την ασύμφορη OEO μετατροπή. Η αναζήτηση των κατάλληλων μονοπατιών με τα κατάλληλα μήκη κύματος που θα ικανοποιούσε ένα πλήρες οπτικό δίκτυο το οποίο δρομολογείται από ligthpaths, ονομάζεται Routing and Wavelength Assignment (RWA) και αποτελεί ένα από τα σημαντικότερα ζητήματα για το σωστό σχεδιασμό των οπτικών δικτύων τέτοιου είδους. Το πρόβλημα γίνεται ιδιαίτερα πολύπλοκο όταν στην τελική απόφαση θα πρέπει να συμπεριληφθούν και τα χαρακτηριστικά του φυσικού επιπέδου του δικτύου, όπως εξασθένιση του σήματος, μη γραμμικά φαινόμενα, διασπορά κ.ά, η συμβολή των οποίων στην τελική δρομολόγηση δεν θεωρείται αμελητέα (Impairment Aware Routing and Wavelength Assignment, ΙΑ-RWA). Σε αυτή την εργασία μελετάται το RWA πρόβλημα και προτείνεται ένας μονού στόχου γενετικός αλγόριθμος (Single Objective Genetic Algorithm - SOGA), ο οποίος επιλύει ικανοποιητικά το πρόβλημα θεωρώντας στατική κίνηση. Επιπλέον τονίζεται η σημασία των φυσικών παραμέτρων του προβλήματος και πως αυτές επηρεάζουν την απόδοση του πλήρους οπτικού δικτυου. Στη συνέχεια προτείνεται ένας νέος, πολλαπλών στόχων γενετικός αλγόριθμος (multi objective genetic algorithm – MOGA) ο οποίος βελτιστοποιεί τις λύσεις του προβλήματος ικανοποιητικά λαμβάνοντας ταυτόχρονα υπόψη, με έμμεσο τρόπο, και τις φυσικές παραμέτρους. Επίσης προτείνεται και ένας μονού στόχου γενετικός αλγόριθμος οποίος χρησιμοποιεί ένα εργαλείο αποτίμηση της ποιότητας μετάδοσης (Q-TOOL) σαν μέτρο κατά τη διαδικασία εύρεσης ικανοποιητικής λύσης. Το υπόλοιπο της εργασίας οργανώνεται ως ακολούθως: Στην ενότητα 2 παρουσιάζεται μια σύντομη αναφορά στα WDM δίκτυα καθώς και η περιγραφή του RWA και IA-RWA προβλήματος, ενώ στην ενότητα 3 παρουσιάζεται η πρόταση επίλυσης του RWA προβληματος με τη χρήση γενετικών αλγορίθμων. Ακολουθεί στην ενότητα 4 η πρότασή μας για επίλυση του IA-RWA προβλήματος με τη χρήση Multi-objective διαδικασιών βελτιστοποίησης, καθώς και η βελτιστοποίηση του προβλήματος με τη χρήση του Q-TOOL. Τέλος στην ενότητα 5 συνοψίζουμε την εργασία και παρουσιάζουμε τα συμπεράσματα. / The recent development of optical amplifiers, multiplexers / de-multiplexers, optical switches and other optical devices leads us to hope that soon in future all optical, WDM (wavelength division multiplexing) networks will be implemented which that will satisfy the needs for large capacity. In such networks a viable conversion of the optical -> Electronic and back to optical (OEO) will not be used at intermediate nodes, and this will contribute to efficient and economical implementation. The search for the appropriate paths with the appropriate wavelengths that meet the requirement in all optical networks is called Routing and Wavelength Assignment (RWA) and is one of the most important issues for proper design of such optical networks. The problem becomes particularly complex when the final decision should include the characteristics of the physical layer of the network, such as attenuation of the signal, nonlinear effects, dispersion, etc., whose contribution to the final result is not considered negligible (Impairment Aware Routing and Wavelength Assignment,IA-RWA). This work studies the RWA problem considering static traffic, and proposes a single-objective genetic algorithm (Single Objective Genetic Algorithm - SOGA), which resolves the problem satisfactorily. Furthermore the work stresses the importance of physical parameters of the problem and how these affect the performance of the all optical networks, and proposes a new, multi-objective genetic algorithm (MOGA) which optimizes the solution of IA-RWA problem adequately taking into account indirectly, and the physical impairments that affect the quality of the signal. In addition, a single objective genetic algorithm is proposed that uses a tool to assess the quality of the transmission signal (Q-TOOL), as a benchmark, in the process of optimization of the solution to the IA-RWA problem.

In the building automation domain, there are gaps among various tasks related to design engineering. As a result created system designs must be adapted to the given requirements on system functionality, which is related to increased costs and engineering effort than planned. For this reason standards are prepared to enable a coordination among these tasks by providing guidelines and unified artifacts for the design. Moreover, a huge variety of prefabricated devices offered from different manufacturers on the market for building automation that realize building automation functions by preprogrammed software components. Current methods for design creation do not consider this variety and design solution is limited to product lines of a few manufacturers and expertise of system integrators. Correspondingly, this results in design solutions of a limited quality. Thus, a great optimization potential of the quality of design solutions and coordination of tasks related to design engineering arises. For given design requirements, the existence of a high number of devices that realize required functions leads to a combinatorial explosion of design alternatives at different price and quality levels. Finding optimal design alternatives is a hard problem to which a new solution method is proposed based on heuristical approaches. By integrating problem specific knowledge into algorithms based on heuristics, a promisingly high optimization performance is achieved. Further, optimization algorithms are conceived to consider a set of flexibly defined quality criteria specified by users and achieve system design solutions of high quality. In order to realize this idea, optimization algorithms are proposed in this thesis based on goal-oriented operations that achieve a balanced convergence and exploration behavior for a search in the design space applied in different strategies. Further, a component model is proposed that enables a seamless integration of design engineering tasks according to the related standards and application of optimization algorithms.