Submitted by JOSIANE SANTOS DE OLIVEIRA (josianeso) on 2018-04-11T16:07:01Z No. of bitstreams: 1 Pedro Nascimento de Lima_.pdf: 14461751 bytes, checksum: 473cf6676a373e7639a581816a08b7a5 (MD5) / Made available in DSpace on 2018-04-11T16:07:01Z (GMT). No. of bitstreams: 1 Pedro Nascimento de Lima_.pdf: 14461751 bytes, checksum: 473cf6676a373e7639a581816a08b7a5 (MD5) Previous issue date: 2018-01-26 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / A avaliação de decisões estratégicas em condições de profunda incerteza é um desafio significativo para as organizações. Em condições onde informação disponível permite que stakeholders cheguem a um consenso sobre o futuro que será mais provável, ferramentas de planejamento baseadas em predição podem suportar o processo decisório de modo confiável. No entanto, em situações de instabilidade, onde o futuro é altamente incerto, a avaliação de decisões estratégicas utilizando predições pode levar a decisões equivocadas. Tais condições de incerteza frequentemente ocorrem em mercados nascentes, onde há alta incerteza relacionada ao processo de difusão de um novo produto. Na Indústria da Manufatura Aditiva, enquanto alguns especialistas estimam que a indústria pode chegar a faturar 21 bilhões de dólares em 2020, outros estimam que este mercado pode valer até 550 bilhões até 2025. Esta pesquisa emprega a simulação computacional de dinâmica de sistemas utilizando o método Robust Decision Making (RDM) para avaliar decisões estratégicas de fabricantes de sistemas de impressão 3D profissional. Para tanto, este trabalho amplia modelos de dinâmica competitiva e difusão de novos produtos para permitir a simulação no contexto da manufatura aditiva. Em seguida, são desenvolvidos algoritmos necessários para a análise RDM. Para avaliar decisões estratégicas em um amplo conjunto de futuros plausíveis, 10.800 simulações são realizadas. Em seguida, a robustez das estratégias avaliadas é testada, e as vulnerabilidades da estratégia mais robusta localizada são examinadas utilizando técnicas estatísticas. Finalmente, o trabalho identifica estratégias alternativas à estratégia mais robusta. Os resultados da simulação sugerem que fabricantes de sistemas de impressão 3D profissional deveriam perseguir uma estratégia de dominação do mercado agressiva, com um modelo de Pesquisa e Desenvolvimento e proteção intelectual fechado. Finalmente, o trabalho discute implicações gerenciais e teóricas relacionadas à avaliação de decisões estratégicas em condições de incerteza profunda. / Strategic Decision Making under deep uncertainty is a relevant challenge to organizations. When the available information allows sound decision making based on predictions, traditional decision making tools based on maximum expected value can lead to the right decision. Under conditions of deep uncertainty, however, decision making based on predict-then-act approaches might mislead and build overconfidence. In the 3D printing industry, uncertainty is highly relevant. While some experts forecast that this industry will worth 21 billion dollars by 2020, other estimates point that this market can have an economic impact of 550 billion by 2025. This dissertation leverages system dynamics simulation, using the Robust Decision Making (RDM) approach as the analytical framework to evaluate 3D printing Systems Manufacturers’ strategic decisions. I extend an existing competitive dynamics model allowing it to take into account expiring patents dynamics, an important aspect of the 3D printing industry. Then, I test 54 different strategies under 200 different scenarios, highlighting the most robust strategies. Afterwards, I examine the vulnerabilities of a candidate strategy using machine learning algorithms. The experiments showed that aggressive strategies dominate their conservative counterparts, using robustness as a criteria. Also, the results do not lend support to open source Research and Development strategies. Finally, I discuss managerial implications to the 3D printing industry, and theoretical contributions to the Strategic Decision-Making literature.

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

Pumped hydro energy storages commonly plan their operations on short-term forecasts of the upcoming electricity prices, meaning that errors in these forecasts would entail suboptimal operations of the energy storage. Despite the high investment costs of pumped hydro energy storages, few studies take a holistic approach to the uncertainties involved in such investment decisions. The aim of this study is to investigate how forecast errors in electricity prices affect the chosen size configuration in investment decisions for pumped hydro energy storages. Moreover, sizing decisions are made in the long-term and involve long-term uncertainties in electricity prices. A robust decision-making framework including long-term electricity price scenarios is therefore used to evaluate the effects of including forecast errors in the sizing decision. By simulating the day-to-day operation of the energy storage with short-term forecasts, the effects of including the errors are compared to using perfect information. Using this approach, the most robust capacity is shown to increase by 25 MW, from 2 375 MW to 2 400 MW, when including forecast errors instead of assuming perfect information in the simulations. This indicates that the deviations in short-term forecasts require the pumped hydro energy storage operator to be more flexible in their operations, thus requiring a higher capacity. In addition, the profitability of the energy storage decreased significantly when including forecast errors in the simulations, showing the importance of taking the short-term forecast errors into account in sizing and investment decisions of pumped hydro energy storage. / Driften av pumpkraftverk optimeras med hjälp av kortsiktiga prognoser av elpriser, vilket innebär att fel i dessa prognoser leder till suboptimal drift. Trots att investeringar i pumpkraftverk är kostsamma, har få studier ett holistisk synsätt kring osäkerheter i investeringsbeslutet. Målet med denna studie är att undersöka hur kortsiktiga prognosfel i elpriser påverkar den optimala dimensionering av pumpkraftverk. Investeringsbeslut i pumpkraftverk är långsiktiga och kräver estimat av framtida elpriser, vars karakteristik är osäker. Ett ramverk som bygger på robust beslutstagande, med scenarier över framtida elpriser, används därför för att bedöma effekten av att inkludera kortsiktiga prognosfel i investeringsbeslutet. Genom att simulera den dagliga driften av energilager, undersöks effekten av att inkludera prognosfel jämfört med perfekt information. Med detta tillvägagångsätt ökade den mest robusta kapaciteten med 25 MW, från 2 375 MW till 2 400 MW, när prognosfel inkluderades. Detta visar på att fel i kortsiktiga prognoser kräver pumpkraftverket av vara mer flexibelt, vilket ges av höjdkapacitet. Lönsamheten minskade också signifikant när prognosfel inkluderades, vilket visar på vikten av att ta hänsyn till kortsiktiga prognosfel i beslut kring dimensionering och investering av pumpkraftverk.

Dans cette thèse, je discute deux questions autour de la transition énergétique : comment définir une stratégie face aux nombreuses incertitudes et aux inerties des systèmes, et quels sont impacts sur l’emploi de cette transition ?Pour étudier le choix d’une stratégie, je m’intéresse au cas du secteur électrique français. Les réacteurs nucléaires arrivent au terme de leur durée de vie initiale, mais ils peuvent être rénovés moyennant un investissement estimé à 100 milliards d'euros pour l'ensemble du parc. Combien de centrales faut-il rénover ? En mobilisant un modèle d'optimisation et la méthode de \textit{Robust Decision Making}, je montre qu’au vu des estimations actuelles, les stratégies les plus intéressantes consistent à fermer 7 à 14 réacteurs et à les remplacer par des énergies renouvelables.Sur le volet de l’emploi, je m’intéresse tout d’abord à la notion de contenu en emploi. Je propose une méthodologie nouvelle permettant de décomposer ce contenu, afin de mettre en évidence, pour chaque branche économique, l’importance relative de ses constituants : les taux d’importations, les taxes et subventions, la part du travail dans la valeur ajoutée et le niveau de salaire. J'étudie ensuite l’impact d’une réallocation des investissements vers des secteurs bas-carbone à l'aide de modèles d'équilibre général, dont j'explicite les mécanismes économiques sous-jacents.Mes résultats indiquent qu'encourager les secteurs avec une forte part du travail dans la valeur ajoutée ou de faibles salaires permet de créer de l'emploi, mais ils nuancent les bénéfices à encourager des secteurs peu importateurs. / This thesis deals with two aspects of the transition towards a low-carbon economy: how to define a strategy under the uncertainties and inertia surrounding power systems, and what are the impacts of this transition on employment?To study the choice of a strategy, I focus on the case of the French power system. The 58 nuclear reactors are reaching the end of their initially planned lifetime, but they can be retrofitted for a cost estimated at a \euro 100 billion for the entire fleet. How many reactors should be retrofitted? I study this question using an optimization model of the French power system and the \textit{Robust Decision Making} framework. My results indicate that the most interesting strategies consist in closing 7 to 14 reactors in favor of renewable energies, given current estimates.As to employment impacts, I study the notion of employment content and offer an original methodology to break it down and understand the relative importance of four components: import rates, taxes and subsidies, the share of labour in value added and the level of wage.Then, I study the employment impacts of shifting investment towards low-carbon sectors with general equilibrium models, and highlight their underlying economic mechanisms.My results suggest that encouraging sectors with a high share of labor or low wages might increase employment, but they also challenge the benefits of targeting sectors with a low import rate.