The purpose of the present study was to determine the concurrent validity of learning potential and psychomotor ability measures for the prediction of haul truck operator (N=128) performance in an open-pit mine. Specific aims were to determine the nature of the relationship between learning potential and psychomotor ability; whether there are higher order cognitive or psychomotor factors present in the combined use of the TRAM 1 and Vienna Test System measures; and the relative contribution of learning potential and psychomotor ability in the prediction of haul truck operator performance. The validity of learning potential and psychomotor ability measures was partially supported. A positive correlation between general (cognitive) ability (g) and psychomotor ability was reported. Factor analysis provided relatively consistent evidence for a general (cognitive) ability factor (g) underlying performance on all measures. The relative contribution of learning potential and psychomotor ability in the prediction of performance could not be established. / Industrial and Organisational Psychology / M. Com. (Industrial and Organisational Psychology)

The purpose of the present study was to determine the concurrent validity of learning potential and psychomotor ability measures for the prediction of haul truck operator (N=128) performance in an open-pit mine. Specific aims were to determine the nature of the relationship between learning potential and psychomotor ability; whether there are higher order cognitive or psychomotor factors present in the combined use of the TRAM 1 and Vienna Test System measures; and the relative contribution of learning potential and psychomotor ability in the prediction of haul truck operator performance. The validity of learning potential and psychomotor ability measures was partially supported. A positive correlation between general (cognitive) ability (g) and psychomotor ability was reported. Factor analysis provided relatively consistent evidence for a general (cognitive) ability factor (g) underlying performance on all measures. The relative contribution of learning potential and psychomotor ability in the prediction of performance could not be established. / Industrial and Organisational Psychology / M. Com. (Industrial and Organisational Psychology)

Abstract The road freight transport industry as a labour-intensive sector is dependent on the work ability and well-being at work of employees. The majority of the occupational accidents are related to work phases outside the cab. These work phases, which are performed in various different work environments, contain several kinds of ergonomic discomforts. This poses complex challenges for the employers from a safety and productivity point of view. The framework of this thesis is based on the foundations of ergonomics and design science. The main objective was to provide knowledge that can be implemented into the design and management of work systems for local and short haul delivery operations. Material was obtained from two sources. A meta-synthesis was performed to frame holistic management in a human perspective. Furthermore, additional in-depth design knowledge was obtained through participatory ergonomics video analyses on drivers’ work outside the cab. Video analyses resulted in 262 identifications of demanding work situations where ergonomic discomforts and risks of accidents occurred. Sudden over-exertions and strains, falls and slips as well as losing control of work equipment were the most common deviations related to drivers’ work outside the cab and mainly related to physical activities of movement and carrying by hand. The majority of the work situations identified were performed in cargo spaces or elsewhere within the truck structure or at premises and yards that are administered by the customers or other stakeholders. In these environments, drivers tend to perform their work manually or using different types of work equipment. This thesis provides new in-depth knowledge on drivers’ work outside the cab. The results show that different stakeholders can contribute to drivers’ work systems. The knowledge provided by drivers and other stakeholders can be applied to holistic design and management processes at company level. Moreover, the knowledge can also be applied to broader value chain design and management processes. / Tiivistelmä Tieliikenteen tavarankuljetus työvoimavaltaisena toimialana on riippuvainen henkilöstön työkyvystä ja -hyvinvoinnista. Suurin osa tapaturmista liittyy työtehtäviin ohjaamon ulkopuolella. Näitä töitä tehdään hyvin vaihtelevissa työympäristöissä ja niihin työtehtäviin liittyy monenlaisia ergonomisia haittakuormitustekijöitä. Tämä asettaa haasteita niin työsuojelun kuin tuottavuuden näkökulmasta. Väitöskirjan viitekehys pohjautuu ergonomiaan sekä suunnittelutieteeseen. Tavoitteena on tuottaa tietoa, jota voidaan hyödyntää työjärjestelmien suunnittelussa ja johtamisessa erityisesti maaliikenteen jakelukuljetuksissa. Materiaali koostui kahdesta osiosta. Metasynteesillä muodostettiin näkemys kokonaisvaltaisesta johtamisesta ihmisnäkökulmasta. Lisäksi kuljettajat ja sidosryhmien edustajat analysoivat osallistuvan ergonomian keinoin videoaineistoa jakelukuljettajien työstä ohjaamon ulkopuolella. Videoanalyyseissa tunnistettiin yhteensä 262 työtilannetta, jossa esiintyy erilaisia ergonomisia haittakuormitustekijöitä sekä mahdollisia tapaturmariskejä. Äkilliset fyysiset kuormitukset, putoamiset, liukastumiset ja kaatumiset sekä työvälineiden hallinnan menettäminen olivat yleisimpiä tunnistettuja poikkeamia kuljettajan työssä. Pääasiassa nämä liittyivät kuljettajan liikkumiseen sekä erilaisten taakkojen kantamiseen. Valtaosassa (85 %) havainnoista kuljettaja työskenteli ajoneuvon kuormatilassa tai päällirakenteissa tai asiakkaiden tai muiden sidosryhmien hallinnoimissa työympäristöissä. Näissä työympäristöissä kuljettaja työskenteli sekä manuaalisesti käsin että hyödyntäen erilaisia apuvälineitä. Väitöskirja tarjoaa uudenlaista syvällistä tietoa kuljettajan työstä ohjaamon ulkopuolella. Eri sidosryhmät voivat osaltaan vaikuttaa kuljettajan työjärjestelmiin. Kuljettajien ja sidosryhmien tuottamaa tietoa voidaan soveltaa työjärjestelmien kokonaisvaltaisessa suunnittelussa ja johtamisessa niin yritystasolla kuin myös suunniteltaessa ja johdettaessa laajempia arvoketjuja.

This thesis presents a predictive energy management controller for long-haul hybrid trucks. In a receding horizon control framework, the vehicle speed reference, battery energy reference, and engine on/off decision are optimized over a prediction horizon. A mixed-integer quadratic program (MIQP) is formulated by performing modelling approximations and by including the binary engine on/off decision in the optimal control problem. The branch-and-bound algorithm is applied to solve this problem. Simulation results show fuel consumption reductions between 10-15%, depending on driving cycle, compared to a conventional truck. The hybrid truck without the predictive control saves significantly less. Fuel consumption is reduced by 3-8% in this case. A sensitivity analysis studies the effects on branch-and-bound iterations and fuel consumption when varying parameters related to the binary engine on/off decision. In addition, it is shown that the control strategy can maintain a safe time gap to a leading vehicle. Also, the introduction of the battery temperature state makes it possible to approximately model the dynamic battery power limitations over the prediction horizon. The main contributions of the thesis are the MIQP control problem formulation, the strategy to solve this with the branch-and-bound method, and the sensitivity analysis.

In order to uncover the best kept secret in today's commercial aviation, this project deals with the calculation of fuel consumption of aircraft. With only the reference of the aircraft manufacturer's information, given within the airport planning documents, a method is established that allows computing values for the fuel consumption of every aircraft in question. The aircraft's fuel consumption per passenger and 100 flown kilometers decreases rapidly with range, until a near constant level is reached around the aircraft's average range. At longer range, where payload reduction becomes necessary, fuel consumption increases significantly. Numerical results are visualized, explained, and discussed. With regard to today's increasing number of long-haul flights, the results are investigated in terms of efficiency and viability. The environmental impact of burning fuel is not considered in this report. The presented method allows calculating aircraft type specific fuel consumption based on publicly available information. In this way, the fuel consumption of every aircraft can be investigated and can be discussed openly.

[pt] O consumo de combustível em empresas do setor de transporte ferroviário representa um dos maiores gastos operacionais e uma das maiores preocupações em termos de emissões de poluentes. O alto consumo em combustíveis acarreta também em uma alta representatividade na matriz de escopo de emissões (mais de 90 por cento das emissões de ferrovias são provenientes do consumo de combustível fóssil). Com o viés de se buscar uma constante melhora operacional, estudos vêm sendo realizados com a finalidade de se propor novas ferramentas na redução do consumo de combustível na operação de um trem de carga. Nesse ramo, destaca-se o aperfeiçoamento dos parâmetros de condução de um trem que são passíveis de calibração com o objetivo de reduzir o consumo de combustível. Para chegar a esse fim, o presente trabalho implementa dois modelos de aprendizado de máquina (machine learning) para prever a eficiência energética de um trem de carga, são eles: floresta randômica e redes neurais artificiais. A floresta randômica obteve o melhor desempenho entre os modelos, apresentando uma acurácia de 91 por cento. Visando calcular quanto cada parâmetro influencia no modelo de previsão, este trabalho também utiliza técnica de efeitos acumulados locais em cada parâmetro em relação à eficiência energética. Os resultados finais mostraram que, dentro dos quatro parâmetros de calibração analisados, o indicador de tração por tonelada transportada apresentou maior representatividade em termos de impacto absoluto na eficiência energética de um trem de carga. / [en] Fuel consumption in companies in the rail transport sector represents one of the largest operating expenses and one of the biggest concerns in terms of pollutant emissions. The high fuel consumption also entails a high representation in the emissions scope matrix (more than 90 percent of railroad emissions come from fossil fuel consumption). Aiming to seek constant operational improvement, numerous studies have been carried out proposing new tools to reduce fuel consumption in the operation of a freight train. In this way, it is important to highlight the improvement of train driving parameters that can be calibrated to reduce fuel consumption. To accomplish this goal, the present work implements two machine learning models to predict the energy efficiency of a freight train: random forest and artificial neural networks. The random forest achieves the best performance against the models, with an accuracy of 91 percent. To calculate how much each parameter influences the prediction model, this work also uses the technique of accumulated local effects for each parameter related to energy efficiency. The final results show that, within the four analyzed calibration parameters, the traction per transported ton indicator presented greater representation in terms of absolute impact on the energy efficiency of a freight train.