The purpose of this thesis is to investigate the development of new technologies in the energy industry and to explore how it is possible for these technologies to compete with incumbent technologies in a mature market. The pursuit of renewable energy has been at the forefront of national government and international institutional policy in recent years due to the desire to improve the security of energy supply and to reduce CO2e emissions. This thesis aims to contribute to this policy debate, particularly by focussing on the issue of governmental support for infant energy technologies. In order to conduct this investigation, two main topics have been analysed. Firstly, learning curves have been studied to establish whether support for new technologies can be justified by the potential cost reductions which arise from learning-by-doing. This research evolved into the investigation of econometric issues which affect learning curves. Patent counts are used to demonstrate an alternative output-based measurement of industry wide knowledge stock, which is used as a proxy for innovation. This alternative specification of knowledge stock corroborates recent findings in the literature, that learning curves which model cost using only cumulative capacity leads to the over-estimation of cost reductions from learning-by-doing and the failure to capture cost reductions resulting from innovation. This suggests that government support for infant technologies should form a dual strategy of incentivising the deployment of generators as well as encouraging innovation, instead of using feed-in tariffs or renewable obligations which narrowly focus on increasing deployment. A great deal of progress has been made in identifying further econometric problems affecting learning curves in recent years. In the progress of this study, it was identified that cumulative capacity, the cost of wind power and knowledge stock are all non-stationary time series variables. The hypothesis that these variables are cointegrated was rejected by the Westerlund test, which implies that learning curves produce spurious results. This has major consequences for government policy as it suggests that learning curves should not be used to justify support for infant technologies. Secondly, a choice experiment was conducted to determine Scottish households’ willingness to pay for electricity generated from renewable sources compared to conventional sources such as coal, oil and gas. A labelled choice experiment was used to determine whether households have preferences between onshore wind power, offshore wind power and wave power. The results of a latent class model reveal that the majority of households (76.5%) are willing to pay an additional £89-£196 per year to obtain electricity from renewable resources instead of conventional sources. However, there is no statistically significant difference in the willingness to pay between the renewable technologies included in the choice experiment. The latent class model also illustrated that there is a sizeable minority (23.5%) who are opposed to renewable energy development. Older respondents and those less concerned about CO2 emissions are significantly more likely to form part of this group at the 5% level of significance. The study also included a unique addition by identifying households which purchased a house in the previous seven years. Interacting the actual transaction prices of these houses in a multinomial logit model suggested that households may be concerned about renewable energy developments devaluing their properties or the additional expense required to power larger houses. Due to the increasing difficulty of conducting choice experiments in the UK, a novel method of eliciting choice experiment responses from online advertising was tested and was found to be a cost-effective method of eliciting choice experiment responses. Overall, the research indicates that caution should be exercised when interpreting the results of a choice experiment which elicits responses using Internet advertising. It can be observed that the pseudo R2 of the Internet-based sample is lower than the mail-based sample and that the mean respondent to the Internet-based choice experiment is willing to pay significantly more for renewable electricity than the mean respondent to the mail-based choice experiment at the 5% level of significance. Furthermore, the mean willingness to pay estimate in the Internet-based choice experiment appears to be unrealistically high. Further research investigating the elasticity of survey responses to the prize fund on offer would be valuable in identifying the most cost-effective strategy to obtain responses and to generate a more representative sample.

A presente dissertação propõe heurísticas de programação da produção balizadas por curvas de aprendizado e fatores humanos com vistas à alocação de tarefas a equipes de trabalhadores. O objetivo é apresentar propostas de sequenciamento que integrem aspectos ergonômicos impactantes no desempenho do trabalhador, sem prejudicar as exigências produtivas do processo. Parte-se de uma revisão da bibliografia sobre técnicas de sequenciamento, curvas de aprendizado e fatores ergonômicos que influenciam no desempenho do trabalhador a fim de entender seu funcionamento e identificar formas de integração das mesmas. Desse modo, são propostos dois métodos de sequenciamento alinhados a curvas de aprendizado e fatores ergonômicos, aplicados em um processo da indústria calçadista. O primeiro método propõe uma adaptação da heurística de minimização da soma do atraso e adiantamento proposta por Pinedo (2008) em relação a uma data comum de entrega. O segundo método, denominado ATCE (Apparent Tardiness Cost with Ergonomic Factors), consiste na adaptação da regra ATC (Apparent Tardiness Cost) na minimização do atraso do processamento de lotes com distintas datas de entrega. Este último é então avaliado através de experimento de simulação de cenários do processo produtivo e comparado à regra ATC. Os resultados obtidos evidenciam a robustez do método ATCE, atendendo as necessidades produtivas e reduzindo a alocação sucessiva de tarefas de mesma complexidade a uma mesma equipe. / This dissertation presents new scheduling heuristics integrated with learning curves and human factors aimed at job scheduling in unrelated parallel workers teams. The objective is to modify heuristics available in the literature to integrate ergonomic aspects that have a profound impact on human performance, without harming demands of the productive process. Two methods that combine learning curves, heuristics for scheduling and ergonomic factors are proposed and applied in the shoe manufacturing industry. The first method aims to optimized the objective function of minimizing the total weighted earliness and tardiness with a common due date for all jobs. The second method propose the ATCE rule (Apparent Tardiness Cost with Ergonomic Factors) which reduces the total weighted tardiness with different due date. The ATCE rule is then evaluated through simulation scenarios of production process and compared to the original rule (ATC), where the results shows the robustness of the method, attending the production needs with a significant improvement in the ergonomic point of view.

A presente dissertação propõe heurísticas de programação da produção balizadas por curvas de aprendizado e fatores humanos com vistas à alocação de tarefas a equipes de trabalhadores. O objetivo é apresentar propostas de sequenciamento que integrem aspectos ergonômicos impactantes no desempenho do trabalhador, sem prejudicar as exigências produtivas do processo. Parte-se de uma revisão da bibliografia sobre técnicas de sequenciamento, curvas de aprendizado e fatores ergonômicos que influenciam no desempenho do trabalhador a fim de entender seu funcionamento e identificar formas de integração das mesmas. Desse modo, são propostos dois métodos de sequenciamento alinhados a curvas de aprendizado e fatores ergonômicos, aplicados em um processo da indústria calçadista. O primeiro método propõe uma adaptação da heurística de minimização da soma do atraso e adiantamento proposta por Pinedo (2008) em relação a uma data comum de entrega. O segundo método, denominado ATCE (Apparent Tardiness Cost with Ergonomic Factors), consiste na adaptação da regra ATC (Apparent Tardiness Cost) na minimização do atraso do processamento de lotes com distintas datas de entrega. Este último é então avaliado através de experimento de simulação de cenários do processo produtivo e comparado à regra ATC. Os resultados obtidos evidenciam a robustez do método ATCE, atendendo as necessidades produtivas e reduzindo a alocação sucessiva de tarefas de mesma complexidade a uma mesma equipe. / This dissertation presents new scheduling heuristics integrated with learning curves and human factors aimed at job scheduling in unrelated parallel workers teams. The objective is to modify heuristics available in the literature to integrate ergonomic aspects that have a profound impact on human performance, without harming demands of the productive process. Two methods that combine learning curves, heuristics for scheduling and ergonomic factors are proposed and applied in the shoe manufacturing industry. The first method aims to optimized the objective function of minimizing the total weighted earliness and tardiness with a common due date for all jobs. The second method propose the ATCE rule (Apparent Tardiness Cost with Ergonomic Factors) which reduces the total weighted tardiness with different due date. The ATCE rule is then evaluated through simulation scenarios of production process and compared to the original rule (ATC), where the results shows the robustness of the method, attending the production needs with a significant improvement in the ergonomic point of view.

A manufatura de produtos customizados resulta em variedade de modelos, redução no tamanho de lotes e alternância frequente de tarefas executadas por trabalhadores. Neste contexto, tarefas manuais são especialmente afetadas por conta do processo de adaptação do trabalhador a novos modelos de produtos. Este processo de aprendizado pode ocorrer de maneira distinta dentro de um grupo de trabalhadores. Assim, busca-se o agrupamento dos trabalhadores com perfis similares de aprendizado, monitorando a formação de gargalos em linhas de produção constituídas por dissimilaridades de aprendizado em processos manuais. A presente dissertação apresenta abordagens para clusterização de trabalhadores baseadas nos parâmetros oriundos da modelagem de Curvas de Aprendizado. Tais parâmetros, os quais caracterizam o processo de adaptação de trabalhadores a tarefas, são transformados através da Análise de Componentes Principais e então utilizados como variáveis de clusterização. Na sequência, testam-se outras transformações nos parâmetros utilizando funções Kernel. Os trabalhadores são clusterizados através do método K-Means e Fuzzy C-Means e a qualidade dos agrupamentos formados é medida através do Silhouette Index. Por fim, sugere-se um índice de importância de variável baseado em parâmetros obtidos na Análise Componentes Principais com o objetivo de selecionar as variáveis mais relevantes para clusterização. As abordagens propostas são aplicadas em um processo da indústria calçadista, gerando resultados satisfatórios quando comparados a clusterizações realizadas sem a transformação prévia dos dados ou sem seleção das variáveis. / Manufacturing of customized products relies on a large menu choice, reduced batch sizes and frequent alternation of tasks performed by workers. In this context, manual tasks are especially affected by workers’ adaptation to new product models. This learning process takes place in different paces within a group of workers. This thesis aims at grouping workers with similar learning process tailored to avoid bottlenecks in production lines due to learning dissimilarities among workers. For that matter, we present a method for clustering workers based on parameters derived from Learning Curve (LC) modeling. Such parameters are processed through Principal Component Analysis (PCA), and the PCA scores are used as clustering variables. Next, Kernel transformations are also used to improve clustering quality. The data is clustered using K-Means and Fuzzy C-Means techniques, and the quality of resulting clusters is measured by the Silhouette Index. Finally, we suggest a variable importance index based on parameters derived from PCA to select the most relevant variables for clustering. The proposed approaches are applied in a footwear process, yielding satisfactory results when compared to clustering on original data or without variable selection.

O objetivo deste estudo foi estimar os efeitos da curva de aprendizado sobre a competitividade de fontes energéticas, tais como petróleo, carvão, gás natural, biomassa (etanol), hidroeletricidade, nuclear, eólica e fotovoltaica, e propor medidas regulatórias que incentivem as tecnologias renováveis. Para tanto, se propôs a utilização da abordagem de learning curves, que considera três efeitos principais para explicar a redução dos custos de produção: o efeito especialização (chamado de learning by doing), o efeito escala (scale effect) e o efeito da pesquisa e desenvolvimento - P&D (learning by searching). Identificou-se o peso desses efeitos por fonte energética com vistas a auxiliar no direcionamento de incentivos às energias renováveis, de modo a se decidir se a ênfase deve ser dada à especialização, escala ou P&D. Embora os modelos originais de learning curves tenham sido idealizados a partir da trajetória dos custos, devido a facilidades operacionais a literatura na área vem adotando o preço como proxy de custos. Neste estudo, a orientação do modelo a custos ou a preços foi objeto de uma avaliação através de análise concorrencial. Como resultado, verificou-se que a adoção de preços como proxy de custos mostrou-se possível para a maioria das fontes analisadas devido a um grau satisfatório de concorrência dos mercados relativos a essas fontes de energia. Uma vez definida a orientação do modelo, a metodologia proposta envolveu estimar os três efeitos por métodos econométricos. Os resultados indicaram que as fontes carvão, petróleo e gás, energia nuclear e fotovoltaica reagiram ao efeito aprendizado, embora não apresentassem resposta expressiva quanto aos gastos em P&D. Já as fontes eólica e etanol mostraram-se sensíveis aos gastos em P&D e ao efeito escala, sendo a escala também determinante dos preços da energia nuclear e hidroeletricidade. Esses resultados auxiliaram na proposição de medidas públicas específicas como estímulo às fontes renováveis. / The goal of this study is to estimate the effects of the learning process on the competitiveness of the main energy sources, such as oil, coal, natural gas, biomass (ethanol), hydroelectricity, nuclear energy, wind power, and photovoltaic energy, and propose regulatory measures to encourage renewable technologies. The learning curves approach adopted in this thesis considers three main effects to explain the reduction in production costs: the learning by doing effect, the scale effect and the learning by searching effect. The relevance of these three effects was identified for energy source, in order to assist in the incentives direction to promote renewable energy. Although the original models of learning curves approach have been conceived from the point of view of the costs, in the related literature the price has been adopted as a proxy due to operational facilities. In this study, the model orientation to costs or prices was chosen using a competitive analysis. It was found that price orientation could be applied to most sources, since there is enough competition in the markets. Once defined model orientation, the proposed methodology involves estimations of the three effects by econometric methods. The results showed that coal, oil and gas, nuclear and photovoltaic energy react to learning effect, though searching effect is not so important. Wind and ethanol are sensitive to searching and scale effects. Scale effects also determine the nuclear power and hydroelectricity prices. From these results, specific public measures are proposed for each renewable source.

A presente dissertação propõe heurísticas de programação da produção balizadas por curvas de aprendizado e fatores humanos com vistas à alocação de tarefas a equipes de trabalhadores. O objetivo é apresentar propostas de sequenciamento que integrem aspectos ergonômicos impactantes no desempenho do trabalhador, sem prejudicar as exigências produtivas do processo. Parte-se de uma revisão da bibliografia sobre técnicas de sequenciamento, curvas de aprendizado e fatores ergonômicos que influenciam no desempenho do trabalhador a fim de entender seu funcionamento e identificar formas de integração das mesmas. Desse modo, são propostos dois métodos de sequenciamento alinhados a curvas de aprendizado e fatores ergonômicos, aplicados em um processo da indústria calçadista. O primeiro método propõe uma adaptação da heurística de minimização da soma do atraso e adiantamento proposta por Pinedo (2008) em relação a uma data comum de entrega. O segundo método, denominado ATCE (Apparent Tardiness Cost with Ergonomic Factors), consiste na adaptação da regra ATC (Apparent Tardiness Cost) na minimização do atraso do processamento de lotes com distintas datas de entrega. Este último é então avaliado através de experimento de simulação de cenários do processo produtivo e comparado à regra ATC. Os resultados obtidos evidenciam a robustez do método ATCE, atendendo as necessidades produtivas e reduzindo a alocação sucessiva de tarefas de mesma complexidade a uma mesma equipe. / This dissertation presents new scheduling heuristics integrated with learning curves and human factors aimed at job scheduling in unrelated parallel workers teams. The objective is to modify heuristics available in the literature to integrate ergonomic aspects that have a profound impact on human performance, without harming demands of the productive process. Two methods that combine learning curves, heuristics for scheduling and ergonomic factors are proposed and applied in the shoe manufacturing industry. The first method aims to optimized the objective function of minimizing the total weighted earliness and tardiness with a common due date for all jobs. The second method propose the ATCE rule (Apparent Tardiness Cost with Ergonomic Factors) which reduces the total weighted tardiness with different due date. The ATCE rule is then evaluated through simulation scenarios of production process and compared to the original rule (ATC), where the results shows the robustness of the method, attending the production needs with a significant improvement in the ergonomic point of view.

A manufatura de produtos customizados resulta em variedade de modelos, redução no tamanho de lotes e alternância frequente de tarefas executadas por trabalhadores. Neste contexto, tarefas manuais são especialmente afetadas por conta do processo de adaptação do trabalhador a novos modelos de produtos. Este processo de aprendizado pode ocorrer de maneira distinta dentro de um grupo de trabalhadores. Assim, busca-se o agrupamento dos trabalhadores com perfis similares de aprendizado, monitorando a formação de gargalos em linhas de produção constituídas por dissimilaridades de aprendizado em processos manuais. A presente dissertação apresenta abordagens para clusterização de trabalhadores baseadas nos parâmetros oriundos da modelagem de Curvas de Aprendizado. Tais parâmetros, os quais caracterizam o processo de adaptação de trabalhadores a tarefas, são transformados através da Análise de Componentes Principais e então utilizados como variáveis de clusterização. Na sequência, testam-se outras transformações nos parâmetros utilizando funções Kernel. Os trabalhadores são clusterizados através do método K-Means e Fuzzy C-Means e a qualidade dos agrupamentos formados é medida através do Silhouette Index. Por fim, sugere-se um índice de importância de variável baseado em parâmetros obtidos na Análise Componentes Principais com o objetivo de selecionar as variáveis mais relevantes para clusterização. As abordagens propostas são aplicadas em um processo da indústria calçadista, gerando resultados satisfatórios quando comparados a clusterizações realizadas sem a transformação prévia dos dados ou sem seleção das variáveis. / Manufacturing of customized products relies on a large menu choice, reduced batch sizes and frequent alternation of tasks performed by workers. In this context, manual tasks are especially affected by workers’ adaptation to new product models. This learning process takes place in different paces within a group of workers. This thesis aims at grouping workers with similar learning process tailored to avoid bottlenecks in production lines due to learning dissimilarities among workers. For that matter, we present a method for clustering workers based on parameters derived from Learning Curve (LC) modeling. Such parameters are processed through Principal Component Analysis (PCA), and the PCA scores are used as clustering variables. Next, Kernel transformations are also used to improve clustering quality. The data is clustered using K-Means and Fuzzy C-Means techniques, and the quality of resulting clusters is measured by the Silhouette Index. Finally, we suggest a variable importance index based on parameters derived from PCA to select the most relevant variables for clustering. The proposed approaches are applied in a footwear process, yielding satisfactory results when compared to clustering on original data or without variable selection.

京都大学 / 0048 / 新制・課程博士 / 博士(地球環境学) / 甲第20539号 / 地環博第160号 / 新制||地環||32(附属図書館) / 京都大学大学院地球環境学舎地球環境学専攻 / (主査)教授 宇佐美 誠, 教授 佐野 亘, 准教授 吉野 章, 准教授 平田 彩子 / 学位規則第4条第1項該当 / Doctor of Global Environmental Studies / Kyoto University / DFAM

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In the organizational domain, it is well established that a significant relationship exists between cognitive ability and job performance (e.g., Hunter, 1986); however, there is less research surrounding the relationship between how intelligent people think they are and expectations of job performance. Although self-estimates have been used in the educational domain since the early 1900s (e.g., self-estimates of ability; Koerth & Rush, 1923; Schutte, 1929; personality traits; Cogan, Conklin, & Hollingworth, 1915; Shen, 1925) they have only recently been applied to the workplace as predictors of job search behavior and occupational choice (e.g., Prediger, 1994; Tracey & Hopkins, 2001). As a result of changing technologies and organizational structures, an employee's ability to learn new job skills is critical to his/ her continued success in the workplace. However, an employee's perception of his/ her learning potential may be as informative as or more informative than objectively measured ability for subsequent decision making (e.g., job choice). The purpose of this study was to investigate prospective estimates of job performance and learning potential, including gender differences in self-estimates, the determinants of self-estimates, and the predictive validity of self-estimates for decisions about engaging in career-related tasks. The goal of the current study was to evaluate self-estimates of job performance and learning potential for 20 jobs. A total of 153 participants watched short video clips depicting each of the 20 jobs and answered a series of questionnaires, assessing future-oriented estimates of job performance, estimates of learning potential, task interest, task value, task experience, and task engagement. Significant gender differences were found in estimates of job performance across job domains, as well as interactions of gender and self-estimates of job performance over anticipated time-on-task. Some significant relations were found between non-ability traits and self-estimates of job performance and learning potential, while significant relations were found between prior job experience and decisions about task engagement. The practical utility of this research is an understanding of how individual differences in non-ability traits such as personality, interest, and motivation may impact an individual's expectations of future job performance, and consequently, an individual's career choice decisions and job pursuits.