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Uncertainty has been an important topic, in research, as well as a social concern. The notion of path uncertainty is introduced as the likelihood of encountering a wide variety of possible trajectories when following a given strategy. The research question is: “How can path uncertainty be modelled?”. This thesis proposes the Path Uncertainty Aware Markov Decision Process (PUA-MDP), based on other types of MDPs related to other types of uncertainty. Its algorithm finds optimal policies for balancing maximal reward with minimal cumulative path uncertainty exposure. Experimental validation demonstrates that the algorithm’s behaviour resembles human behavioural responses to uncertainty. It also demonstrates that a small decrease in reward can result in a drastic decrease in uncertainty. If such a method is applied to any classic MDP, path uncertainty could be reduced greatly.

This paper discusses how Q-Learning and Deep Q-Networks (DQN) canbe applied to state-action problems described by a Markov decision process(MDP). These are machine learning methods for finding the optimal choiceof action at each time step, resulting in the optimal policy. The limitationsand advantages for the two methods are discussed, with the main limitationbeing the fact that Q-learning is unable to be used on problems with infinitestate spaces. Q-learning, however, has an advantage in the simplicity of thealgorithm, leading to a better understanding of what the algorithm is actuallydoing. Q-Learning did manage to find the optimal policy for the simpleproblem studied in this paper, but was unable to do so for the advancedproblem. The Deep Q-Network (DQN) approach was able to solve bothproblems, with a drawback in it being harder to understand what the algorithmactually is doing.

Advancements in information technology enabled to track real time data in a more accurate and precise way in many manufacturing facilities. However, before obtaining the more accurate and precise data, the investment in information technology should be validated. Value of information may be adopted as a criterion in this investment. In this study, we analyze the value of information in a manufacturing facility where production and lead time quotation decisions are taken. In order to assess the value of information, two settings are analyzed. Under the first setting, the manufacturer takes decisions under perfect information. To find the optimal decisions under perfect information, a stochastic model is introduced. Under the second setting, the manufacturer takes decisions under imperfect information. To obtain a solution for this problem, Partially Observable Markov Decision Process is employed. Under the second setting, we study two approaches. In the first approach, we introduce a nonlinear programming model to find the optimal decisions. In the second approach, a heuristic approach, constructed on optimal actions taken under perfect information is presented. We examine the value of information under different parameters by considering the policies under nonlinear programming model and heuristic approach. The profit gap between the two policies is investigated. The effect of Make-to-Order (MTO) and Make-to-Stock (MTS) schemes on the value of information is analyzed. Lastly, different lead time quotation schemes / accept-all, accept-reject and precise lead time / are compared to find under which quotation scheme value of information is highest.

Today’s society is earmarked by developments in technology so drastic, that our lives are continuously changed, impacted and remolded. When thinking about how many impressions one goes through during a single day, the results will be uncountable. And at an increasing pace, many of these impressions are shifting to the digital realm. This thesis acknowledges these rapid changes in the modern world of marketing, particularly the digital aspects of it. Yet while a great amount of research has been conducted to more closely understand digital marketing and how to conduct it, a significantly little amount of research has been spent focusing on the customer. This while the path that a customer takes from need to purchase may have grown to be more complex than ever. Not all the impressions that a customer gains on a certain product or brand come solely from the company behind it. As such, we decided to focus on exploring this customer journey – a relatively nascent theme in the field of marketing research. More specifically, we aim to answer the following research question: How is the customer purchasing decision affected by the customer’s interaction with touch points throughout the customer journey? Our main purpose was to arrive at a model as an answer to the research question, that could be used as a foundation for future research studies. We were intensely curious at gaining a better understanding of the customer journey and what the customers experience as they are touched by different influential channels during their travel from need to purchase. Our study shows that it is possible to synthesize the state of current research on the topic of the customer journey and support it with empirical data gathered from interviews with a variety of customers. Central to our thesis are the following two definitions that we have constructed based on current literature: The customer journey is the individual experience that a customer has when interacting with touch points in the path from a pre-purchase to a post-purchase setting. Touch points are moments of contact between customer and company that individually and collectively influence customer experience. The third critical element is that of the customer experience, which we don’t explicitly define, but implicitly construe as a distinct construct with a powerful impact by serving as the catalyst between touch points and customer actions and perceptions.The result is a conceptual model of the customer journey that we feel could be a foundation for future research on the customer journey to build upon and perhaps one day could serve as an overarching model for marketing as a whole.

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Este trabalho foi motivado pela necessidade de considerar comportamento estocástico durante o planejamento da produção de sistemas de manufatura, ou seja, o que produzir e em que ordem. Estes sistemas possuem um comportamento estocástico geralmente não considerado no planejamento da produção. O principal objetivo deste trabalho foi obter um método que modelasse sistemas de manufatura e representasse seu comportamento estocástico durante o planejamento de produção destes sistemas. Como os métodos que eram ideais para planejamento não forneciam a modelagem adequada dos sistemas, e os com modelagem adequada não forneciam a capacidade de planejamento necessária, decidiu-se combinar dois métodos para atingir o objetivo desejado. Decidiu-se modelar os sistemas em rede de Petri e convertê-los em processos de decisão markovianos, e então realizar o planejamento com o ultimo. Para que fosse possível modelar as probabilidades envolvidas nos processos, foi proposto um tipo especial de rede de Petri, nomeada rede de Petri fatorada. Utilizando este tipo de rede de Petri, foi desenvolvido o método de conversão em processos de decisão markovianos. A conversão ocorreu com sucesso, conforme testes que mostraram que planos podem ser produzidos utilizando-se algoritmos de ponta para processos de decisão markovianos. / The present work was motivated by the need to consider stochastic behavior when planning the production mix in a manufacturing system. These systems are exposed to stochastic behavior that is usually not considered during production planning. The main goal of this work was to obtain a method to model manufacturing systems and to represent their stochastic behavior when planning the production for these systems. Because the methods that were suitable for planning were not adequate for modeling the systems and vice-versa, two methods were combined to achieve the main goal. It was decided to model the systems in Petri nets and to convert them into Markov decision processes, to do the planning with the latter. In order to represent probabilities in the process, a special type of Petri nets, named Factored Petri nets, were proposed. Using this kind of Petri nets, a conversion method into Markov decision processes was developed. The conversion is successful as tests showed that plans can be produced within seconds using state-of-art algorithms for Markov decision processes.

Este trabalho foi motivado pela necessidade de considerar comportamento estocástico durante o planejamento da produção de sistemas de manufatura, ou seja, o que produzir e em que ordem. Estes sistemas possuem um comportamento estocástico geralmente não considerado no planejamento da produção. O principal objetivo deste trabalho foi obter um método que modelasse sistemas de manufatura e representasse seu comportamento estocástico durante o planejamento de produção destes sistemas. Como os métodos que eram ideais para planejamento não forneciam a modelagem adequada dos sistemas, e os com modelagem adequada não forneciam a capacidade de planejamento necessária, decidiu-se combinar dois métodos para atingir o objetivo desejado. Decidiu-se modelar os sistemas em rede de Petri e convertê-los em processos de decisão markovianos, e então realizar o planejamento com o ultimo. Para que fosse possível modelar as probabilidades envolvidas nos processos, foi proposto um tipo especial de rede de Petri, nomeada rede de Petri fatorada. Utilizando este tipo de rede de Petri, foi desenvolvido o método de conversão em processos de decisão markovianos. A conversão ocorreu com sucesso, conforme testes que mostraram que planos podem ser produzidos utilizando-se algoritmos de ponta para processos de decisão markovianos. / The present work was motivated by the need to consider stochastic behavior when planning the production mix in a manufacturing system. These systems are exposed to stochastic behavior that is usually not considered during production planning. The main goal of this work was to obtain a method to model manufacturing systems and to represent their stochastic behavior when planning the production for these systems. Because the methods that were suitable for planning were not adequate for modeling the systems and vice-versa, two methods were combined to achieve the main goal. It was decided to model the systems in Petri nets and to convert them into Markov decision processes, to do the planning with the latter. In order to represent probabilities in the process, a special type of Petri nets, named Factored Petri nets, were proposed. Using this kind of Petri nets, a conversion method into Markov decision processes was developed. The conversion is successful as tests showed that plans can be produced within seconds using state-of-art algorithms for Markov decision processes.

本論文共分五章，其主要內容如下： ：說明論文之動機、目的、架構及 限制。 及理論：分為對外投資決策行為相關研究、對外投資決策過程 @ 之研究及對外投資相關研究。方法與分析：分為名詞定義、問卷 設計、研究對象與資料蒐集及研究分析方法。結果與分析：使用Probit及 Regression統計分析方法，分析二主題，得到以下結果：典型台灣企業對 外投資決策過程 @ 台灣企業通常是為了取得低價勞工而去對外投資， 對外投資計劃的提議與決定多由董事長或董事會執行，通常成立專案小組 負責對外投資計劃，專案小組的成員主要有董事長或董事會、總經理及副 總經理。進行初步調查的方法主要係由公司自行搜集相關資料，廠商投資 的產業以在國內曾在生產銷售的產品，主要調查東南亞地區，且多會派總 經理實地調查投資地點的市場及廠房設備情形；除此之外，會更進一步調 查投資報酬率。廠商主要對外投資資金來源是以公司現有的資金，投資計 劃的評估方法是採回收期間法，投資風險的評估方法是採主觀判斷法，多 在當地獨 @自成立新公司，一般在對外投資上遭遇的問題與困難主要為利 潤 外投資計劃。影響台灣企業對外投資決策過程的公司因素公司曾有對 外投資經驗與公司規模（以員工人數與營業額衡量）和對外投資動機（考 慮市場因素、生產條件因素、資金因素及地主國政府政策）、對外投資計 劃題議者、對外投資專案小組成員、實地派去調查的人員、對外投資資金 來源管道、對外投資 存在，顯見公司因素會影響台灣企業對外投資 的決策過程。與建議。務評估方式及風險評估方式等七種決策活動之間有 顯著關不符合，有半數的廠商曾因風險或不確定性太大而放棄對