For cost and reliability efficiency, optimal design and operation of pressurized water distribution networks is highly important. However, optimizing such networks is still a challenge since it requires an appropriate determination of: (1) dimension of pipe / pump / tank - decision variables (2) cost / network reliability - objective functions and (3) limits or restrictions within which the network must operate - a given set of constraints. The costs mentioned here consist in general of capital, construction, and operation costs. The reliability of a network mainly refers to the intrinsic capability of providing water with adequate volume and a certain pressure to consumers under normal and extreme conditions. These contradicting objective functions are functions of network configuration regarding component sizes and network layout. Because considerable uncertainties finally render the overall task to a highly complex problem, most recent approaches mainly focus only on finding a trade-off between minimizing cost and maximizing network reliability. To overcome these limitations, a novel model system that simultaneously considers network configuration, its operation and the relevant uncertainties is proposed in this study. For solving this multi-objective design problem, a simulation-based optimization approach has been developed and applied. The approach couples a hydraulic model (Epanet) with the covariance matrix adaptation evolution strategy (CMA-ES) and can be operated in two different modes. These modes are (1) simulation–based Single-objective optimization and (2) simulation-based multi-objective optimization. Single-objective optimization yields the single best solution with respect to cost or network reliability, whereas multi-objective optimization produces a set of non-dominated solutions called Pareto optimal solutions which are trade-offs between cost and reliability. In addition, to prevent a seriously under-designed network, demand uncertainties was also taken into account through a so called “robustness probability” of the network. This consideration may become useful for a more reliable water distribution network. In order to verify the performance of the proposed approach, it was systematically tested on a number of different benchmark water distribution networks ranging from simple to complex. These benchmark networks are either gravity-fed or pumped networks which need to be optimally designed to supply urban or irrigation water demand under specific constraints. The results show that the new approach is able: • to solve optimization problems of pressurized water distribution network design and operation regarding cost and network reliability; • to directly determine the pumping discharge and head, thus allowing to select pumps more adequately; • to simulate time series of tank water level; • to eliminate redundant pipes and pumps to generate an optimal network layout; • to respond well to complex networks other than only to simple networks; • to perform with multiple demand loading; • to produce reliable Pareto optimal solutions regarding multi-objective optimization. In conclusion, the new technique can be successfully applied for optimization problems in pressurized water distribution network design and operation. The new approach has been demonstrated to be a powerful tool for optimal network design not only for irrigation but also for an urban water supply.

This thesis analyses how public decision-makers can rapidly and sustainably decarbonise their regions, considering the challenge that promoting timely decarbonisation represents. The research was divided into three phases and developed using desk studies and interviews with Project Drawdown (PD) experts, Swedish public officials and researchers. In phase one, PD solutions showed a shortcoming in sustainable operationalisation of global decarbonisation solutions. It was found that they alone could not account for sustainability, and that there is dependency on the context of application and on decision-making processes. In phase two, it was found that sustainability could be embedded in decision-making for decarbonisation through decision support. In the Blekinge example case, several challenges were found, for which sustainable decision support was needed. To respond to this challenge, a Decision Support tool (DST) was created, based on multi-criteria decision analysis. This three-step generic tool provides a relevance assessment to prove the suitability of decarbonisation solutions in the given context, a sustainability assessment to address sustainability challenges, and a prioritisation step for strategic decision-making. The tool was validated to be useful by Swedish policymakers and by international experts. This DST has the potential to support policy-making around the world to decarbonise regions rapidly and sustainably.

Europe experienced the destruction of numerous infrastructures during World War II, followed by a reflation and a strong economic growth during the next two decades allowing a more perennial and durable situation. A classical bridge lasting in general around 80 years, one should observe that these constructions built after the war will have to be either replaced either seriously strengthened in a few years. Besides, since the needs also vary over time, transportation infrastructures built during those years might not be adapted to the actual needs anymore – some bridges might thus have to be widened. A case study has been chosen in order to simulate under which conditions the widening of such a bridge can be performed. This road bridge, located in Vierzon in France, is rather simple since it is made of simply supported prestressed concrete beams and of reinforced concrete piers. It has been chosen in particular for its reduced size – three spans of 30 m each and two road lanes – that corresponded well to this project. Based on some data provided when the bridge was initially built and on a visual inspection, this project suggests six technical solutions to double the actual amount of lanes. An evaluation of the performance of the solutions according to three criteria – durations of works, cost of the works, and environmental impact – is made in order to give recommendations regarding the optimal solution. The results show that in spite of being installed quickly, adding steel beams is more expensive and has a greater impact on the environment than adding prestressed concrete beams. Regarding the modification of piers, the solution suggesting widening the existing piers is preferable than adding new extra piers according to all the criteria. Consequently, among all the solutions analysed, the optimal one is also the simplest one. Finally, the limits of the study and some suggestions for improvements are indicated.

There has been an increasing relevance of the cultural sector in the economic and social development of different countries. However, this sector continues without much input from multi-criteria decision-making (MDCM) techniques and sustainability analysis, which are widely used in other sectors. This paper proposes an MCDM model to assess the sustainability of a musical institution’s program. To define the parameters of the proposed model, qualitative interviews with relevant representatives of Catalan cultural institutions and highly recognized professionals in the sector were performed. The content of the 2015–2016 season of the ‘Palau de la Música Catalana’, a relevant Catalan musical institution located in Barcelona, was used as a case study to empirically test the method. The method allows the calculation of a season value index (SVI), which serves to make more sustainable decisions on musical season programs according to the established criteria. The sensitivity analysis carried out for different scenarios shows the robustness of the method. The research suggests that more complex decision settings, such as MCDM methods that are widely used in other sectors, can be easily applied to the sustainable management of any type of cultural institution. To the best of the authors’ knowledge, this method was never applied to a cultural institution and with real data. / Universitat Oberta de Catalunya

The work presented in this thesis targets the analysis and implementation of multi-criteria performance prediction methods for System-on-Chips (SoC).<p>These new SoC architectures offer the opportunity to integrate complete heterogeneous systems into a single chip and can be used to design battery powered handhelds, security critical systems, consumer electronics devices, etc. However, this variety in terms of application usually comes with a lot of different performance objectives like power consumption, yield, design cost, production cost, silicon area and many others. These performance requirements are often very difficult to meet together so that SoC design usually relies on making the right design choices and finding the best performance compromises.<p>In parallel with this architectural paradigm shift, new Very Deep Submicron (VDSM) silicon processes have more and more impact on the performances and deeply modify the way a VLSI system is designed even at the first stages of a design flow.<p>In such a context where many new technological and system related variables enter the game, early exploration of the impact of design choices becomes crucial to estimate the performance of the system to design and reduce its time-to-market.<p>In this context, this thesis presents: <p>- A study of state-of-the-art tools and methods used to estimate the performances of VLSI systems and an original classification based on several features and concepts that they use. Based on this comparison, we highlight their weaknesses and lacks to identify new opportunities in performance prediction.<p>- The definition of new concepts to enable the automatic exploration of large design spaces based on flexible performance criteria and degrees of freedom representing design choices.<p>- The implementation of a couple of two new tools of our own:<p>- Nessie, a tool enabling hierarchical representation of an application along with its platform and automatically performs the mapping and the estimation of their performance.<p>-Yeti, a C++ library enabling the defintion and value estimation of closed-formed expressions and table-based relations. It provides the user with input and model sensitivity analysis capability, simulation scripting, run-time building and automatic plotting of the results. Additionally, Yeti can work in standalone mode to provide the user with an independent framework for model estimation and analysis.<p><p>To demonstrate the use and interest of these tools, we provide in this thesis several case studies whose results are discussed and compared with the literature.<p>Using Yeti, we successfully reproduced the results of a model estimating multi-core computation power and extended them thanks to the representation flexibility of our tool.<p>We also built several models from the ground up to help the dimensioning of interconnect links and clock frequency optimization.<p>Thanks to Nessie, we were able to reproduce the NoC power consumption results of an H.264/AVC decoding application running on a multicore platform. These results were then extended to the case of a 3D die stacked architecture and the performance benefits are then discussed.<p>We end up by highlighting the advantages of our technique and discuss future opportunities for performance prediction tools to explore. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished

L’inférence du modèle de préférence à partir des jugements préférentiels fournis par le décideur, Élicitation des Préférences (EP), est fondamentale au sein de l’Aide Multicritère à la Décision (AMCD), car l’élaboration des recommandations à la fois plausibles, constructives et convaincantes requiert que l’analyste construise un modèle de préférence qui rende compte fidèlement du jugement du décideur. Cependant, l’EP est une mission délicate, parce qu’il s’agit d’attribuer des valeurs aux paramètres du modèle de préférence choisi. Dans ce cadre, plusieurs aspects sont étudiés. Puisque les modèles de préférence étant de plus en plus complexes, on fait alors appel à des algorithmes sophistiqués, et il faut d’autant plus tenir compte de l’aspect computationnel.Ce travail de thèse vise à concevoir des algorithmes afin d’inférer du modèle de préférence à partir des comparaisons par paire (possiblement incohérentes), et de considérer des données de (relativement) grande taille. En particulier, nous nous sommes intéressés à un modèle de rangement multicritère récemment proposé et faisant appel à un certain nombre de points de référence. Ce modèle fait référence à la méthode intitulée “Ranking with Multiple Profiles” (RMP). Plus précisément, nous considérons une version particulière, dite S-RMP. Nos contributions sont divisées en trois parties. Du point de vue théorique, nous nous adressons sur (1) l’interprètabilité des points de référence et (2) la discriminabilité du modèle S-RMP. En termes d’algorithmes, nous présentons, d’abord, (3) un nouveau programme linéaire pour inférer du modèle S-RMP en tenant compte les incohérences et (4) une version robuste améliorée; en outre, (5) une métaheuristique qui procède avec des données massives. (6) Nous menons alors les analyses numériques. (7) Le développement de deux services web est également inclus. En termes d’application, (8) nous présentons une étude de cas. / The inference of preference model from holistic statements provided by the decision maker (DM), namely, Preference Elicitation (PE), is fundamental to Multi-Criteria Decision Aid (MCDA). In order to conduct plausible, constructive and convincing recommendations, the decision analyst should always take the DM’s preference system into account. However, PE might be tricky, as it involves setting appropriately a series of parameter values of the considered model. Various aspects should be considered. Since the preference models are becoming more and more complicated, PE usually relies on sophisticated algorithms, whereas this brings additionally the computational aspect into consideration.This PhD thesis aims at developing new elicitation algorithms dealing with (possibly inconsistent) pairwise comparisons and processing with (relatively) large input datasets. In particular, a recently introduced multi-criteria ranking method making use of a certain number of reference points is considered. It is known as RMP method as abbreviated for Ranking with Multiple reference Points. More specifically, we are interested in one of its Simplified version, namely S-RMP method. Our contributions are divided into three parts. From the theoretical perspective, we are concerned about (1) the interpretation of reference points in such models and (2) the discriminability of S-RMP model. From the algorithmic perspective, we propose firstly (3) a new linear programming formulation for eliciting S-RMP models from inconsistent pairwise comparisons and also (4) an improved robust elicitation algorithm; besides, (5) a metaheuristic for learning S-RMP models from massive data. (6) Numerical analyses are then performed. (7) The development of two web services is also included. From the practical perspective, (8) we present a realistic case study.

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Density has increasingly been used as a strategy by urban planners to increase accessibility and urban sustainability. Particularly, it has been opted as a tool to achieve sustainable development goal 11 ‘inclusive, safe, resilient, and sustainable cities and human settlements’. Though densification is considerably impactful in realising resource efficiency and transport benefits, its effects are too multi-lateral to be used as a linear tool to realise accessibility and sustainability. This is both caused by the assume d knowledge-practice gap between academics and urban planners, as well as the complexity of interspatial collaboration between local and global practitioners. Particularly, the universal application of sustainable development goal 11 and the top-down approaches to evaluate performance in achieving sustainability hinder strategic interspatial integration. Fostering interspatial and interdisciplinary collaboration would require establishing a holistic indicator framework that acts as a foundation for contextualised project appraisal. Relevant topics were identified and validated by expert opinion to establish such an operationalised indicator framework that could be used as a foundation for practitioners to evaluate the effects of densification projects on accessibility and sustainability. To identify what topics planning actors across spatial scales (i.e., local, regional, and national)and between disciplines (i.e., academia and practice) value differently, the priorities of topics presented in the indicator list were compared by various stakeholders from the Greater Gothenburg region. Comparing priorities and identifying disparities was done using the methodological framework of the Analytical Hierarchy Process. More specifically, a multi-actor-multi-criteria analysis was used to compare priorities. The differences between responses showed that the gap between knowledge and adaptation might be smaller than initially suspected. Differences in priorities between planning actors across spatial scales shows interspatial discrepancies in mainly the valuation of public transport, urban services, and biodiversity conservatio n. Furthermore, notable differences in prioritisation by singular actors illustrate the need to qualitatively inquire certain topics further, including the role of inter-urban employment on transport networks, the incorporation of social factors in quantitative appraisal, the capacity of small municipalities to incorporate contextualised biodiversity conservation, and the role of a varied accommodation supply on metropolitan housing shortage.

Nowadays the competition between cities is something very common, especially between cities of different countries. But this competition it can be observed even between cities of the same country or between districts of the same city. Based on this phenomenon municipalities try to change and become more sustainable (socially and environmentally), implement more green spaces in their urban core, create vibrant local environments and launch campaigns in order to create liveable districts, improve their local economy and survive this growing competition. In other words, cities want to become more attractive.In Sweden cities are also part of this global trend and since they are growing economically they try to create an urban environment that is desirable for its citizens. In Stockholm’s Översiktsplan there are different main goals, such as “The growing city (växande stad)” which is analyzed as “An attractive big city” or “Good public spaces (God offentlig miljö)” which is analyzed as “Mixed use urban space”, “Inviting public space”, “Living local centers” leading to the question how do these correlate and how do they affect each other.The reason of this research is to understand what is an attractive area in a city and find out a way to measure attractiveness by using spatial or non-spatial factors who play a major role on how a city is perceived. It is known based on existing research and literature, that many different factors are involved for a place to be considered as attractive, such as the distance from the means of transport, the distance to public amenities, house affordability, vibrant lifestyle, the distance from market places, social equality, the distance from the city center, the proximity to nature and many others, but there is no index that uses all these factors and calculates an attractiveness score.So this research aims in the creation of an attractiveness index, by formulating a lot of different indicators (social, geographic, economic, etc) based on the Översiktsplan goals and the calculation of attractiveness of different areas in Stockholm. The areas are SoFo District in Södermalm, Skarpnäck suburb in the south and Tensta suburb in the north. The main goal of this research is to improve the urban quality in Stockholm by identifying problematic areas, in order to increase the awareness about urban quality and the way to accomplish this research is the use of Multi-Criteria Evaluation in collaboration with Geographic Information Systems.Based on all the above the research question in this thesis will be: How city’s block attractiveness is measured with the use of Multi-Criteria Evaluation and the implementation of Geographic Information Systems?

Multi-Criteria Decision Making (MCDM) problems are often associated with tradeoffs between performances of the available alternative solutions under decision making criteria. These problems become more complex when performances are associated with uncertainty. This study proposes a stochastic MCDM procedure that can handle uncertainty in MCDM problems. The proposed method coverts a stochastic MCDM problem into many deterministic ones through a Monte-Carlo (MC) selection. Each deterministic problem is then solved using a range of MCDM methods and the ranking order of the alternatives is established for each deterministic MCDM. The final ranking of the alternatives can be determined based on winning probabilities and ranking distribution of the alternatives. Ranking probability distributions can help the decision-maker understand the risk associated with the overall ranking of the options. Therefore, the final selection of the best alternative can be affected by the risk tolerance of the decisionmakers. A Group Decision Support System (GDSS) is developed here with a user-friendly interface to facilitate the application of the proposed MC-MCDM approach in real-world multiparticipant decision making for an average user. The GDSS uses a range of decision making methods to increase the robustness of the decision analysis outputs and to help understand the sensitivity of the results to level of cooperation among the decision-makers. The decision analysis methods included in the GDSS are: 1) conventional MCDM methods (Maximin, Lexicographic, TOPSIS, SAW and Dominance), appropriate when there is a high cooperation level among the decision-makers; 2) social choice rules or voting methods (Condorcet Choice, Borda scoring, Plurality, Anti-Plurality, Median Voting, Hare System of voting, Majoritarian iii Compromise ,and Condorcet Practical), appropriate for cases with medium cooperation level among the decision-makers; and 3) Fallback Bargaining methods (Unanimity, Q-Approval and Fallback Bargaining with Impasse), appropriate for cases with non-cooperative decision-makers. To underline the utility of the proposed method and the developed GDSS in providing valuable insights into real-world hydro-environmental group decision making, the GDSS is applied to a benchmark example, namely the California‘s Sacramento-San Joaquin Delta decision making problem. The implications of GDSS‘ outputs (winning probabilities and ranking distributions) are discussed. Findings are compared with those of previous studies, which used other methods to solve this problem, to highlight the sensitivity of the results to the choice of decision analysis methods and/or different cooperation levels among the decision-makers