The Internal Combustion Engines (ICEs) have played a significant role in transportation system to date and are expected to retain a significant market share through to 2050, according to the U.S. Energy Information Administration. Improving the efficiency of the ICEs is one of the most promising and cost-effective approaches to increasing highways vehicle’s fuel economy. The tools to address critical barriers to commercializing higher efficiency, lower emissions, advanced ICEs for passenger and commercial vehicles are increasingly important in the rapidly evolving automotive sector. In this research, a model based optimization strategy is developed for trade-off analysis of parts in Spark Ignition Internal Combustion Engines (SI-ICE). The trade-off analysis tool has been used as a complement to engine mapping to determine the operating region of an engine where a new part could lead to improvements in fuel efficiency, performance, and emissions. To build the engine models, a Design of Experiment (DoE) was developed for performing the engine tests. For each spark plug set, the engine tests were conducted twice with an acceptable control of the parameters that affect engine outputs. The engine torque, Break Specific Fuel Consumption (BSFC) and break specific NOx emission were considered as the engine responses. Engine models were built according to the two-stage modeling strategy by means of black box modeling techniques. The accuracies of the models were 96%, 95% and 92% for the engine torque, BSFC and NOx outputs respectively. For the optimization part, determination of the optimal spark timing for each spark plug was formulated as a multi-objective optimization problem searching for compromises among opposing objectives, i.e. engine torque, emission and fuel consumption. The optimization outputs were in form of Pareto fronts, enabling the selection of the best solutions in terms of different objectives by considering the higher level information. The resulted Pareto fronts of the two spark plugs were compared at different operating points of the Ford Coyote engine and results showed that the two plugs are comparable. The marginal difference was at low load and low speed condition, where the newly designed spark plug was better than the conventional design. / Thesis / Master of Applied Science (MASc)

The complete design process for a new nuclear power plant concept is costly, long, complicated, and the work is generally split between several specialized groups. These design groups separately do their best to design the portion of the reactor that falls in their expertise according to the design criteria before passing the design to the subsequent design group. Ultimately, the work of each design group is combined, with significant iteration between groups striving to facilitate the integration of each of the heavily interdependent systems. Such complex interaction between experts leads to three significant problems: (1) the issues associated with knowledge management, (2) the lack of design optimization, and (3) the failure to discover the hidden interdependencies between different design parameters that may exist. Some prior work has been accomplished in both developing common frame of reference (CFR) support systems to aid in the design process and applying optimization to nuclear system design.The purpose of this work is to use multi-objective optimization to address the second and third problems above on a small subset of reactor design scenarios. Multi-objective optimization generates several design optima in the form of a Pareto front, which portrays the optimal trade-off between design objectives. As a major part of this work, a system design optimization tool is created, namely the Optimization and Preference Tool for the Improvement of Nuclear Systems (OPTIONS). The OPTIONS tool is initially applied to several individual nuclear systems: the power conversion system (PCS) of the Integral, Inherently Safe Light Water Reactor (I²S-LWR), the Kalina cycle being proposed as the PCS for a LWR, the PERCS (or Passive Endothermic Reaction Cooling System), and the core loop of the Zion plant. Initial sensitivity analysis work and the application of the Non-dominated Sorting Particle Swarm Optimization (NSPSO) method provides a Pareto front of design optima for the PCS of the I²S-LWR, while bringing to light some hidden pressure interdependencies for generating steam using a flash drum. A desire to try many new PCS configurations leads to the development of an original multi-objective optimization method, namely the Mixed-Integer Non-dominated Sorting Genetic Algorithm (MI-NSGA). With this method, the OPTIONS tool provides a novel and improved Pareto front with additional optimal PCS configurations. Then, the simpler NSGA method is used to optimize the Kalina cycle, the PERCS, and the Zion core loop, providing each problem with improved designs and important objective trade-off information. Finally, the OPTIONS tool uses the MI-NSGA method to optimize the integration of three systems (Zion core loop, PERCS, and Rankine cycle PCS) while increasing efficiency, decreasing costs, and improving performance. In addition, the tool is outfitted to receive user preference input to improve the convergence of the optimization to a Pareto front.

The phenomenal density growth in semiconductors has resulted in the availability of billions of transistors on a single die. The time-to-design is shrinking continuously due to aggressive competition. Also, the integration of many discrete components on a single chip is growing at a rapid pace. Designing such heterogeneous systems in short duration is becoming difficult with existing technology. Field-Programmable Gate Arrays offer a good alternative in both productivity and heterogeneity issues. However, there are many obstacles that need to be addressed to make them a viable option. One such obstacle is the lack of early design space exploration tools and techniques for FPGA designs. This thesis develops techniques to evaluate systematically, the available design options before the actual system implementation. The aspect which makes this problem interesting, yet complicated, is that a system-level optimization is not linearly summable. The discrete components of a system, benchmarked as best in all design parameters — speed, area and power, need not add up to the best possible system. This work addresses the problem in two ways. In the first approach, we demonstrate that by working at higher levels of abstraction, one can achieve orders of improvement in productivity. Designing a system directly from its behavioral description is an on-going effort in industry. Instead of focusing on design aspects, we use these methods to develop quick prototypes and estimate the design parameters. Design space exploration needs relative comparison among available choices and not accurate values of design parameters. It is shown that the proposed method can do an acceptable job in this regard. The second approach is about evolving statistical techniques for estimating the design parameters and then algorithmically searching the design space. Specifically, a high level power estimation model is developed for FPGA designs. While existing techniques develop power model for discrete components separately, this work evaluates the option of generic power model for multiple components. / Master of Science

<p>U ovoj disertaciji je kvantitativno određen odnosa energetske<br />efikasnosti i pouzdanosti u bežičnim senzorskim mrežama na<br />fizičkom sloju i sloju kontrole pristupa medijumu. Pronađene<br />su optimalne vrednosti ovog odnosa u smislu vi&scaron;eciljne<br />optimizacije sa Pareto pristupom, bez preferenci.</p> / <p>In this dissertation we quantify energy-efficiency and reliability<br />trade-off in wireless sensor networks at physical and medium<br />access control layers. We find the trade-off optimal solutions in<br />the sense of multi-objective Pareto optimality, without<br />preferences.</p>

Η εύρεση γεννητικών δέντρων ελάχιστου-κόστους αποτελεί ένα κλασικό επιστημονικό πρόβλημα με σημαντικές εφαρμογές στη σχεδίαση δικτύων. Δοθέντος ενός γραφήματος, όπου κάθε πλευρά σχετίζεται με ένα βάρος (κριτήριο) το πρόβλημα της εύρεσης ενός Ελάχιστου Γεννητικού Δέντρου ανέρχεται στο πρόβλημα της εύρεσης ενός γεννητικού δέντρου με το ελάχιστο συνολικό κόστος. Το πρόβλημα ΕΓΔ έχει αποτελέσει αντικείμενο ενδιαφέροντος πολλών μελετητών με αποτέλεσμα την ανάπτυξη αλγορίθμων πολυωνυμικού-χρόνου, όπως είναι ο αλγόριθμος του Prim, του Sollin και του Kruskal. Στον πραγματικό κόσμο όμως υπάρχουν περιπτώσεις όπου πρέπει να λάβουμε ταυτόχρονα υπόψη πολλά κριτήρια προκειμένου να καθορίσουμε ένα ΕΓΔ. Αυτό συμβαίνει γιατί κάθε πλευρά του γραφήματος σχετίζεται με παραπάνω από ένα κόστη. Για παράδειγμα, στη σχεδίαση ενός τηλεπικοινωνιακού δικτύου, πέρα από το κόστος κατασκευής των συνδέσεων μεταξύ των πόλεων ή των τερματικών μας ενδιαφέρουν και άλλοι παράγοντες. Ο χρόνος που απαιτείται για την κατασκευή, η δυσκολία και πολυπλοκότητα της κατασκευής, η καθυστέρηση μετάδοσης της πληροφορίας αλλά και η αξιοπιστία του συστήματος αποτελούν σημαντικούς παράγοντες που πρέπει να ληφθούν υπόψη στην σχεδίαση του δικτύου. Αλλά και στην καθημερινή ζωή, πολλές φορές χρειάζεται να ληφθούν σημαντικές αποφάσεις οι οποίες εξαρτώνται από περισσότερα από ένα κριτήρια. Παραδείγματος χάριν, άνθρωποι που ταξιδεύουν θέλουν να βελτιστοποιήσουν τη διανυόμενη απόσταση, το κόστος, και το χρόνο μετακίνησης. Το ζητούμενο είναι πως μπορεί να οδηγηθεί κανείς στη λήψη μιας βέλτιστης για αυτόν απόφασης, που κάτω από δεδομένες συνθήκες μπορεί να είναι περισσότερες από μία. Δηλαδή, δεν οδηγούμαστε σε μία μοναδική βέλτιστη λύση αλλά σε ένα σύνολο από «βέλτιστες» λύσεις και ο ενδιαφερόμενος, ανάλογα με τα ιδιαίτερα χαρακτηριστικά του προβλήματος, κάνει την τελική επιλογή. Το πρόβλημα ΕΓΔ, στο οποίο ζητείται η ελαχιστοποίηση περισσοτέρων του ενός κριτηρίων είναι γνωστό ως το πρόβλημα ΕΓΔ πολλαπλών κριτηρίων (multi-criteria minimum spanning tree problem). Η συνεισφορά της παρούσας διπλωματικής λοιπόν αποτελείται από δύο μέρη: Το πρώτο, εστιάζεται στην κριτική επισκόπηση και περιγραφή των υπαρχόντων μεθόδων επίλυσης του προβλήματος ΕΓΔ δύο κριτηρίων. Το δεύτερο, αφορά την υλοποίηση και πειραματική αξιολόγηση δύο βασικών αλγορίθμων για την επίλυση του εν λόγω προβλήματος. Συγκεκριμένα, υλοποιήθηκε η τροποποιημένη εκδοχή (για το πρόβλημα ΕΓΔ πολλαπλών κριτηρίων) του αλγορίθμου του Prim καθώς και μία προσεγγιστική μέθοδος επίλυσης του προβλήματος ΕΓΔ πολλαπλών κριτηρίων. / The minimum spanning tree problem (MST) is of high importance in network optimization. Given a connected graph G where each edge has a weight, the goal is to find the spanning tree with the least cost among all spanning trees of G. Due to its many practical applications, the MST problem has been studied in depth and many efficient polynomial-time algorithms have been developed by Sollin, Kruskal, Prim etc. But in real life, there cases where one has to take simultaneously into consideration many criteria in order to determine a MST because there are multiple weights defined on each edge of the graph. For example, when designing the layout of a telecommunication network, besides the cost for connections between cities or terminals we are interested in other factors too. The time for communication and construction, the difficulty of the construction or the reliability of the system are also important factors and need to be taken into consideration. But also in everyday life, in many cases we need to take decisions that depend on multiple criteria. For instance, people who travel want to minimize simultaneously the cost, the distance and the time. The problem is that in these cases there is not only one optimal solution but rather a set of optimal solutions and the decision maker depending on the characteristics of each case will make the final call. The MST problem in which we want to minimize more than one criteria is known as the multi-criteria minimum spanning tree problem. The contribution of this thesis is composed of two parts. The first part focuses on the critical survey and description of various methods for solving the bi-criteria case of the MST problem. The other part focuses on the implementation and the experimental evaluation of two known and important algorithms. More precisely, we have implemented the modified version of the Prim’s algorithm (for the multi-criteria MST problem) and one approximate algorithm as proposed by Hamacher & Ruhe.

Les techniques de groupement technologique sont aujourd’hui utilisées dans de nombreux ateliers de fabrication; elles consistent à décomposer les systèmes industriels en sous-systèmes ou cellules constitués de pièces et de machines. Trouver le groupement technologique le plus efficace est formulé en recherche opérationnelle comme un problème de formation de cellules. La résolution de ce problème permet de tirer plusieurs avantages tels que la réduction des stocks et la simplification de la programmation. Plusieurs critères peuvent être définis au niveau des contraintes du problème tel que le flot intercellulaire,l’équilibrage de charges intracellulaires, les coûts de sous-traitance, les coûts de duplication des machines, etc. Le problème de formation de cellules est un problème d'optimisation NP-difficile. Par conséquent les méthodes exactes ne peuvent être utilisées pour résoudre des problèmes de grande dimension dans un délai raisonnable. Par contre des méthodes heuristiques peuvent générer des solutions de qualité inférieure, mais dans un temps d’exécution raisonnable. Dans ce mémoire, nous considérons ce problème dans un contexte bi-objectif spécifié en termes d’un facteur d’autonomie et de l’équilibre de charge entre les cellules. Nous présentons trois types de méthodes métaheuristiques pour sa résolution et nous comparons numériquement ces métaheuristiques. De plus, pour des problèmes de petite dimension qui peuvent être résolus de façon exacte avec CPLEX, nous vérifions que ces métaheuristiques génèrent des solutions optimales. / Group technology techniques are now widely used in many manufacturing systems. Those techniques aim to decompose industrial systems into subsystems or cells of parts and machines. The problem of finding the most effectivegroup technology is formulated in operations research as the Cell Formation Problem. Several criteria can be used to specify the optimal solution such as flood intercellular, intracellular load balancing, etc. Solving this problem leads to several advantages such as reducing inventory and simplifying programming. The Cell Formation Problem is an NP-hard problem; therefore, exact methods cannot be used to solve large problems within a reasonabletime, whereas heuristics can generate solutions of lower quality, but in a reasonable execution time. We suggest in this work, three different metaheuristics to solve the cell formation problem having two objectives functions: cell autonomy and load balancing between the cells.We compare numerically these metaheuristics. Furthermore, for problems of smaller dimension that can be solved exactly with CPLEX, we verify that the metaheuristics can reach the optimal value.

Les techniques de groupement technologique sont aujourd’hui utilisées dans de nombreux ateliers de fabrication; elles consistent à décomposer les systèmes industriels en sous-systèmes ou cellules constitués de pièces et de machines. Trouver le groupement technologique le plus efficace est formulé en recherche opérationnelle comme un problème de formation de cellules. La résolution de ce problème permet de tirer plusieurs avantages tels que la réduction des stocks et la simplification de la programmation. Plusieurs critères peuvent être définis au niveau des contraintes du problème tel que le flot intercellulaire,l’équilibrage de charges intracellulaires, les coûts de sous-traitance, les coûts de duplication des machines, etc. Le problème de formation de cellules est un problème d'optimisation NP-difficile. Par conséquent les méthodes exactes ne peuvent être utilisées pour résoudre des problèmes de grande dimension dans un délai raisonnable. Par contre des méthodes heuristiques peuvent générer des solutions de qualité inférieure, mais dans un temps d’exécution raisonnable. Dans ce mémoire, nous considérons ce problème dans un contexte bi-objectif spécifié en termes d’un facteur d’autonomie et de l’équilibre de charge entre les cellules. Nous présentons trois types de méthodes métaheuristiques pour sa résolution et nous comparons numériquement ces métaheuristiques. De plus, pour des problèmes de petite dimension qui peuvent être résolus de façon exacte avec CPLEX, nous vérifions que ces métaheuristiques génèrent des solutions optimales. / Group technology techniques are now widely used in many manufacturing systems. Those techniques aim to decompose industrial systems into subsystems or cells of parts and machines. The problem of finding the most effectivegroup technology is formulated in operations research as the Cell Formation Problem. Several criteria can be used to specify the optimal solution such as flood intercellular, intracellular load balancing, etc. Solving this problem leads to several advantages such as reducing inventory and simplifying programming. The Cell Formation Problem is an NP-hard problem; therefore, exact methods cannot be used to solve large problems within a reasonabletime, whereas heuristics can generate solutions of lower quality, but in a reasonable execution time. We suggest in this work, three different metaheuristics to solve the cell formation problem having two objectives functions: cell autonomy and load balancing between the cells.We compare numerically these metaheuristics. Furthermore, for problems of smaller dimension that can be solved exactly with CPLEX, we verify that the metaheuristics can reach the optimal value.

Calculate the Pareto frontier with minimum arm energy ripple and conduction loss of an MMC when the second and fourth harmonic of the circulating current is used as free parameters. ParetoMMC attempts to solve min F(X, lambda), X where F(X, lambda) = E_ripple(X)*lambda + P_loss*(1 - lambda). X denotes the amplitudes and phases of the second and fourth harmonic of the circulating current. lambda is the weighting scalar in the range 0 <= lamda <= 1. The MMC dc side is connected to a dc voltage source, while the ac side is a symmetric three phase voltage with isolated star point. A third harmonic in the common mode voltage is assumed.:ParetoMMC.m F_eval.m LICENSE.GNU_AGPLv3

<p>Process operability represents the ability of a process plant to operate satisfactorily away from the nominal operating or design condition, where flexibility and dynamic operability are two important attributes of operability considered in this thesis. Today's companies are facing numerous challenges, many as a result of volatile market conditions. Key to sustainable profitable operation is a robust process supply chain. Within a wider business context, flexibility and responsiveness, i.e. dynamic operability, are regarded as key qualifications of a robust process supply chain.</p> <p>The first part of this thesis develops methodologies to rigorously evaluate the dynamic operability and flexibility of a process supply chain. A model is developed which describes the response dynamics of a multi-product, multi-echelon supply chain system. Its incorporation within a dynamic operability analysis framework is shown, where a bi-criterion, two-stage stochastic programming approach is applied for the treatment of demand uncertainty, and for estimating the Pareto frontier between an economic and responsiveness criterion. Two case studies are presented to demonstrate the effect of supply chain design features on responsiveness. This thesis has also extended current paradigms for process flexibility analysis to supply chains. The flexibility analysis framework, where a steady-state supply chain model is considered, evaluates the ability to sustain feasible steady-state operation for a range of demand uncertainty.</p> <p>The second part of this thesis develops a decision-support tool for supply chain management (SCM), by means of a robust model predictive control (MPC) strategy. An effective decision-support tool can fully leverage the qualifications from the operability analysis. The MPC formulation proposed in this thesis: (i) captures uncertainty in model parameters and demand by stochastic programming, (ii) accommodates hybrid process systems with decisions governed by logical conditions/rulesets, (iii) addresses multiple supply chain performance metrics including customer service and economics, and (iv) considers both open-loop and closed-loop prediction of uncertainty propagation. The developed robust framework is applied for the control of a multi-echelon, multi-product supply chain, and provides a substantial reduction in the occurrence of back orders when compared with a nominal MPC framework.</p> / Master of Applied Science (MASc)

Energy consumption rates have been dramatically increasing on a global scale within the last few decades. A significant role in this increase is subjected by the recent high temperature levels especially at summer time which caused a rapid increase in the air conditioning demands. Such phenomena can be clearly observed in developing countries, especially those in hot climate regions, where people depend mainly on conventional air conditioning systems. These systems often show poor performance and thus negatively impact the environment which in turn contributes to global warming phenomena. In recent years, the demand for urban or district cooling technologies and networks has been increasing significantly as an alternative to conventional systems due to their higher efficiency and improved ecological impact. However, to obtain an efficient design for district cooling systems is a complex task that requires considering a wide range of cooling technologies, various network layout configuration possibilities, and several energy resources to be integrated. Thus, critical decisions have to be made regarding a variety of opportunities, options and technologies. The main objective of this thesis is to develop a tool to obtain preliminary design configurations and operation patterns for district cooling energy systems by performing roughly detailed optimizations and further, to introduce a decision-making approach to help decision makers in evaluating the economic aspects and environmental performance of urban cooling systems at an early design stage. Different aspects of the subject have been investigated in the literature by several researchers. A brief survey of the state of the art was carried out and revealed that mathematical programming models were the most common and successful technique for configuring and designing cooling systems for urban areas. As an outcome of the survey, multi objective optimization models were decided to be utilized to support the decision-making process. Hence, a multi objective optimization model has been developed to address the complicated issue of decision-making when designing a cooling system for an urban area or district. The model aims to optimize several elements of a cooling system such as: cooling network, cooling technologies, capacity and location of system equipment. In addition, various energy resources have been taken into consideration as well as different solar technologies such as: trough solar concentrators, vacuum solar collectors and PV panels. The model was developed based on the mixed integer linear programming method (MILP) and implemented using GAMS language. Two case studies were investigated using the developed model. The first case study consists of seven buildings representing a residential district while the second case study was a university campus district dominated by non-residential buildings. The study was carried out for several groups of scenarios investigating certain design parameters and operation conditions such as: Available area, production plant location, cold storage location constraints, piping prices, investment cost, constant and variable electricity tariffs, solar energy integration policy, waste heat availability, load shifting strategies, and the effect of outdoor temperature in hot regions on the district cooling system performance. The investigation consisted of three stages, with total annual cost and CO2 emissions being the first and second single objective optimization stages. The third stage was a multi objective optimization combining the earlier two single objectives. Later on, non-dominated solutions, i.e. Pareto solutions, were generated by obtaining several multi objective optimization scenarios based on the decision-makers’ preferences. Eventually, a decision-making approach was developed to help decision-makers in selecting a specific solution that best fits the designers’ or decision makers’ desires, based on the difference between the Utopia and Nadir values, i.e. total annual cost and CO2 emissions obtained at the single optimization stages. / Die Energieverbrauchsraten haben in den letzten Jahrzehnten auf globaler Ebene dramatisch zugenommen. Diese Erhöhung ist zu einem großen Teil in den jüngst hohen Temperaturniveaus, vor allem in der Sommerzeit, begründet, die einen starken Anstieg der Nachfrage nach Klimaanlagen verursachen. Solche Ereignisse sind deutlich in Entwicklungsländern zu beobachten, vor allem in heißen Klimaregionen, wo Menschen vor allem konventionelle Klimaanlagensysteme benutzen. Diese Systeme verfügen meist über eine ineffiziente Leistungsfähigkeit und wirken sich somit negativ auf die Umwelt aus, was wiederum zur globalen Erwärmung beiträgt. In den letzten Jahren ist die Nachfrage nach Stadt- oder Fernkältetechnologien und -Netzwerken als Alternative zu konventionellen Systemen aufgrund ihrer höheren Effizienz und besseren ökologischen Verträglichkeit satrk gestiegen. Ein effizientes Design für Fernkühlsysteme zu erhalten, ist allerdings eine komplexe Aufgabe, die die Integration einer breite Palette von Kühltechnologien, verschiedener Konfigurationsmöglichkeiten von Netzwerk-Layouts und unterschiedlicher Energiequellen erfordert. Hierfür ist das Treffen kritischer Entscheidungen hinsichtlich einer Vielzahl von Möglichkeiten, Optionen und Technologien unabdingbar. Das Hauptziel dieser Arbeit ist es, ein Werkzeug zu entwickeln, das vorläufige Design-Konfigurationen und Betriebsmuster für Fernkälteenergiesysteme liefert, indem aureichend detaillierte Optimierungen durchgeführt werden. Zudem soll auch ein Ansatz zur Entscheidungsfindung vorgestellt werden, der Entscheidungsträger in einem frühen Planungsstadium bei der Bewertung städtischer Kühlungssysteme hinsichtlich der wirtschaftlichen Aspekte und Umweltleistung unterstützen soll. Unterschiedliche Aspekte dieser Problemstellung wurden in der Literatur von verschiedenen Forschern untersucht. Eine kurze Analyse des derzeitigen Stands der Technik ergab, dass mathematische Programmiermodelle die am weitesten verbreitete und erfolgreichste Methode für die Konfiguration und Gestaltung von Kühlsystemen für städtische Gebiete sind. Ein weiteres Ergebnis der Analyse war die Festlegung von Mehrzieloptimierungs-Modelles für die Unterstützung des Entscheidungsprozesses. Darauf basierend wurde im Rahmen der vorliegenden Arbeit ein Mehrzieloptimierungs-Modell für die Lösung des komplexen Entscheidungsfindungsprozesses bei der Gestaltung eines Kühlsystems für ein Stadtgebiet oder einen Bezirk entwickelt. Das Modell zielt darauf ab, mehrere Elemente des Kühlsystems zu optimieren, wie beispielsweise Kühlnetzwerke, Kühltechnologien sowie Kapazität und Lage der Systemtechnik. Zusätzlich werden verschiedene Energiequellen, auch solare wie Solarkonzentratoren, Vakuum-Solarkollektoren und PV-Module, berücksichtigt. Das Modell wurde auf Basis der gemischt-ganzzahlig linearen Optimierung (MILP) entwickelt und in GAMS Sprache implementiert. Zwei Fallstudien wurden mit dem entwickelten Modell untersucht. Die erste Fallstudie besteht aus sieben Gebäuden, die ein Wohnviertel darstellen, während die zweite Fallstudie einen Universitätscampus dominiert von Nichtwohngebäuden repräsentiert. Die Untersuchung wurde für mehrere Gruppen von Szenarien durchgeführt, wobei bestimmte Designparameter und Betriebsbedingungen überprüft werden, wie zum Beispiel die zur Verfügung stehende Fläche, Lage der Kühlanlage, örtliche Restriktionen der Kältespeicherung, Rohrpreise, Investitionskosten, konstante und variable Stromtarife, Strategie zur Einbindung der Solarenergie, Verfügbarkeit von Abwärme, Strategien der Lastenverschiebung, und die Wirkung der Außentemperatur in heißen Regionen auf die Leistung des Kühlsystems. Die Untersuchung bestand aus drei Stufen, wobei die jährlichen Gesamtkosten und die CO2-Emissionen die erste und zweite Einzelzieloptimierungsstufe darstellen. Die dritte Stufe war ein Pareto-Optimierung, die die beiden ersten Ziele kombiniert. Im Anschluss wurden nicht-dominante Lösungen, also Pareto-Lösungen, erzeugt, indem mehrere Pareto-Optimierungs-Szenarien basierend auf den Präferenzen der Entscheidungsträger abgebildet wurden. Schließlich wurde ein Ansatz zur Entscheidungsfindung entwickelt, um Entscheidungsträger bei der Auswahl einer bestimmten Lösung zu unterstützen, die am besten den Präferenzen des Planers oder des Entscheidungsträgers enstpricht, basierend auf der Differenz der Utopia und Nadir Werte, d.h. der jährlichen Gesamtkosten und CO2-Emissionen, die Ergebnis der einzelnen Optimierungsstufen sind.