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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Babaei, Mohammad Hossein; Wong, Stephen D.;

    Disasters significantly impact communities, often require large-scale evacuations, and damage key infrastructure (e.g., power, transportation). With growing electric vehicle (EV) adoption and electrification of transportation, governments and utilities may face significant power challenges during a disaster, especially during the evacuation stage. Low states-of-charge, sporadic charging infrastructure, or power outages could significantly hamper safe and effective evacuations. Yet, EVs also offer possible resilience benefits to emergency response by more easily charging electronics (e.g., mobile phones, radios, vital medical equipment) or sending power back to the grid through vehicle-to-grid (V2G) technology. To understand this dichotomy, this paper focuses on the use cases, benefits, and drawbacks of EVs in disasters and evacuations through a systematic review of current literature, reports, and sources. Overall, this review discovered EVs show promise as modes of transportation and mobile energy supply units during disasters and emergencies. However, crucial challenges such as charging infrastructure locations, upfront cost of resilience technologies, and user behavior remain understudied. We recommend that more dedicated research, evaluation programs, and demonstrations be conducted to overcome EV shortcomings and guide more realistic implementation of EV benefits.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Akhtar Hussain; Van-Hai Bui; Petr Musilek;

    Transportation electrification has the potential to reduce carbon emissions from the transport sector. However, the increased penetration of electric vehicles (EVs) can potentially overload the distribution systems. This becomes prominent in locations with multiple EV chargers and charging stations with many EVs. Therefore, this study proposes a welfare maximization-based soft actor critic (SAC) model to mitigate transformer overload in distribution systems due to the high penetration of EVs. The demand of each charging station is managed locally to avoid network overload during peak load hours in two steps. First, a welfare maximization-based optimization model is developed to maximize the welfare of electric vehicle owners by performing vehicle-to-vehicle(V2V) service. In this step, the sensitivity of EV owners to different parameters (energy level, battery degradation, and incentives provided by fleet operators) is considered. Then, a deep reinforcement learning-based method (soft-actor critic) is trained by incorporating the welfare value (obtained from the welfare maximization model) in the reward function. The total power demand (at the transformer level) and transformer capacity are also included in the reward function. The agent (fleet operator) learns the optimal pricing strategy for local demand management of EVs by interacting with the environment. Each electric vehicle responds to the action (price) by deciding the amount of power they are willing to charge/discharge (V2V) during that interval. Training is performed offline, and the trained model can be used for real-time demand management of different types of charging stations. The simulation results have shown that the proposed method can successfully manage the demand of different charging stations, via V2V, without violating the transformer capacity limits.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Bernard, Jérémy; Bocher, Erwan; Gousseff, Matthieu; Le Saux Wiederhold; +1 Authors

    This repository can be used to reproduced all the work described in the manuscript called "A generic algorithm to automatically classify urban fabric according to the Local Climate Zone system: implementation in GeoClimate 0.0.1 and application to French cities" Please open "Readme.txt" for further detail.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Hussain, Akhtar; Musilek, Petr;

    In order to maximize utilitarianism and fairness in allocating energy to electric vehicles (EVs) during outages, this study proposes an optimization method based on modified division rules. The idea of essential energy demand is introduced in this paper to maximize the number of EVs that are served during power contingencies. For EVs carrying out the time-critical task(s), the essential energy demand is defined as the amount of energy required by EVs to accomplish their upcoming task(s). For EVs carrying out delay-tolerant tasks, it is defined as the amount of energy required by EVs to travel to a nearby healthy charging station. To this end, an EV ranking mechanism is devised considering the full and essential energy demands along with the urgency of EVs. Subsequently, EVs with higher ranks are prioritized during energy allocation. The performance of the proposed method is compared with four existing division rules, i.e., proportional, constrained equal awards, constrained equal losses, and sequential priority rules. A utilitarianism index is proposed to analyze the performance of these methods and fairness is evaluated using existing indices, such as Jain’s fairness index and cost of fairness index. The proposed method has outperformed existing division rules in both utilitarianism and fairness for the essential energy demand. Finally, a sensitivity analysis of uncertain parameters such as EV fleet size, available/required energy, and weight factors is carried out to analyze the performance of the proposed method under various conditions.

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  • Authors: PRADHAN, SHAMEER KUMAR; TUNGAL, SAGAR;

    Large volume of data is generated by different systems. Intelligent systems such as autonomous driving uses such large volume of data to train their artificial intelligence models. However, good quality data is one of the foremost needs of any system to function in an effective and safe manner. Especially in critical systems such as those related with autonomous driving, quality data becomes sacrosanct as fault in such systems could result in fatal accidents. In this thesis, a Design Science Research is conducted to identify challenges related with data quality of a distributed deep learning system. The challenges are identified by conducing interviews with five experts from autonomous driving domain as well as through literature review. The challenges and their severity are validated using a survey. After identification of the challenges, five artifact components are developed that relate with assessing and improving data quality. The artifact components include Data Quality Workflow, List of Challenges, List of Data Quality Attributes, List of Data Quality Attribute Metrics, and Potential Solutions. The abstract artifact components and concrete implementation of those components are devised and validated using second round of interviews. In the third iteration of this study, the final artifact components are validated through a focus group session with experts and survey. Furthermore, the artifact also presents the information regarding which challenges affect which data quality attributes. This association between challenges and attributes are also validated in the focus group session. The results depict that most of the challenge - attribute association presumed by the researchers of this thesis are valid. Similarly, the templates developed for the artifact components are regarded as appropriate as well. A contribution of this thesis study towards the body of software engineering and requirements engineering research is the comprehensive and unified "Data Quality Assessment and Maintenance Framework" developed as a series of artifact components in this thesis. This framework can be used by researchers and practitioners to improve processes related with data quality as well as enhance data quality of the systems they develop.

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  • Authors: Elovsson Hentschel, Alicia; Zetterberg, Frida;

    Previous literature within the GVC research field have mainly focused on economic values, however, recently environmental aspects have increasingly started to be considered. Nevertheless, although road transport is a major cause of CO2 emissions, it has been overlooked in the context of greening GVCs. Therefore, the study aims to enrich this rather underinvestigated field by examining how greening of GVCs is addressed by reducing CO2 emissions from road transports with an emphasis on fossil-free fuels. This is done through a qualitative multiple-case study, taking a dynamic approach by including four lead firms (topdown perspective) and five suppliers (bottom-up perspective). The results show that the lead firms mainly adopt captive governance in relation to road carriers. The requirements set by lead firms can lead to an external drive for suppliers to environmentally upgrade, however, it can also weaken the motivation and inhibit the innovation process of suppliers to develop new fuel solutions. Furthermore, this study found that lead firm characteristics, such as if they operate B2B or B2C, influence the sustainability governance. The prerequisites for suppliers to environmentally upgrade are not only affected by the adopted governance structure by the lead firm, but also the suppliers’ characteristics as strategic intent, capabilities and competencies. In addition to providing a more holistic view to analyze greening of GVCs by taking both a topdown and bottom-up approach, this study also goes one step further in understanding and grasping the importance of the interconnectedness between lead firms and suppliers through inter-firm collaborations. MSc in International Business and Trade

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  • Authors: Back, Johanna; Svensson, Olivia; Weiler, Pauline;

    The customer journey is a complex phenomenon which consists of numerous touch points. The customer journey is not only constructed through touch points but also by the level of involvement that the journey is characterized by. Thus, the customer journey could be designed and characterized by different touch points depending on the level of involvement but also the specific market. This research aims to discover the design of the customer journey and its integrated touch points of electric vehicles in the South Korean market. The study is built upon the chronological steps; pre-purchase, purchase and post-purchase in order to derive the outline of the customer journey. In order to fulfill the aim, a qualitative research strategy was chosen. Semi-structured interviews with experts on the South Korean market and experience from the automotive industry were conducted. Furthermore, in order to strengthen and increase the reliability of the interviews an additional survey was conducted with customers operating in the South Korean market. The main findings suggest that the customer journey cannot be considered to have a consistent shape, rather it can be viewed from different perspectives. Thus, in some instances it can be viewed as linear considering the purchasing process consisting of the phases pre-purchase, purchase and post-purchase. However, it could also be viewed as circular due to the loyalty loop and retention aspects of the customer journey and lastly it can be viewed as a maze where consumers take different paths towards the same destination or could decide to abandon the journey. Furthermore, the most prominent activities in the pre-purchase phase are information search, Word of Mouth and reviews where the most important touch points are Naver, Google, Kakao and the physical meeting with a car-retailer. In the purchase phase, payment and delivery showed to be the most important activities. These would rather be carried out in a physical setting, putting emphasis on the physical meeting as the most prominent touchpoint. However, if the purchases are performed online, the company website showed to be of significance and thus an additional important touchpoint. In the post-purchase phase the touch points related to loyalty , reviews and service were most prominent. The specific touch points were loyalty programs, physical meeting in after-services and reviews on Naver Café, Naver blogs as well as pre-designed review forms. An additional finding that should be highlighted is that online touch points are more related to the pre-purchase and post-purchase phase, however the physical element still permeates the entire customer journey. MSc in Marketing and Consumption

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  • Authors: Storgärd, Kevin;

    The thesis aims to analyze how the 3PL industry can become more sustainable in its warehousing operations. The thesis presents several factors that affect a 3PL warehouse’s environmental impact through a qualitative case report. The results are based on interviews with relevant persons working within or in connection to 3PL warehousing. These interviews are analyzed in combination with a theoretical framework to ultimately answering the research questions. The conclusion structures a framework for Swedish 3PL-actors to consider when shaping their sustainable warehouses. Factors affecting a warehouse’s energy efficiency are investigated to identify which areas are of higher importance to reduce the environmental impact of the warehousing operations. Also, the thesis explores the possibilities of self-producing solar energy, revealing the current pros and cons of producing renewable energy and presenting two different approaches for developing solar energy production plants. Additionally, the future of energy storage is explored to draw a picture of where the market is today and why energy storage should be considered for all modern warehouses MSc in Logistics and Transport Management

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  • Authors: McGowan, Ellen;

    The coronavirus disease 2019 (COVID-19) has radically impacted public transport ridership and service provision across the country. Since the outbreak of the virus, transit agencies have had to adapt to new and rapidly evolving conditions. Many agencies modified services to reflect lower ridership levels and to ensure the safety of both riders and operators. These changes in service were guided by public health agencies, as well as major transit associations like the Canadian Urban Transit Association (CUTA) and International Association of Public Transport (UITP). Other agencies implemented precautionary measures like rear door boarding, temporary fare suspension, and reduced capacity limits to enable the safe continuity of operations. As the COVID-19 pandemic continues, transit agencies are having to strike a balance between providing enough transportation options for essential travel and reducing service offerings to match the declining overall demand for mobility services. Using a case study of Grand River Transit (GRT) in the Region of Waterloo, this report will document the impacts of COVID-19 on transit agencies and their responses, with a focus on modifications to services. By analyzing the challenges that transit agencies faced in modifying transit services, this report will offer guidance on the protocols and procedures that should be established for an effective pandemic response. Further, the findings of this report will help to inform discussions and guide decisions on the role and operation of public transit in future pandemic events. 

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Pariyarath, Anand Maniyam;

    Increased penetration of electric vehicles (EVs) and renewable energy sources (RESs) in power systems can directly affect the system reliability and impose additional complexities to planning and operation due to their uncertainties. The traditional planning methods based on deterministic analysis fail to accurately capture the impact of the aforementioned uncertainty on the system reliability. In this thesis, a reliability-oriented distribution system analysis methodology that captures the complex interactions between EVs, photovoltaic (PV) power production, and energy storage is proposed. Firstly, a two-layer stochastic EV charging demand estimation model is proposed. The model comprises of a traffic layer representing the spatial-temporal distributions of EVs and an electrical network layer describing the impact of EV charging demand on electrical network. A Dynamic Hidden Markov model is used to capture the EV movements in the traffic layer. The ability of the traffic layer model to faithfully represent the random travel pattern of actual vehicles used by different types of drivers is examined. Secondly, a novel stochastic solar radiation model based on probability distributions of the first-order differences of hourly global solar horizontal radiation is proposed to calculate the stochastic power output of the PV system. Measured solar radiation data from four different locations with varying climate characteristics were used to evaluate the proposed model in comparison to two previously reported models. Additionally, various computational models such as the EV charging station model, reliability evaluation model, and economic evaluation model are developed to support the reliability and economic evaluation with necessary inputs. Monte Carlo simulation (MCS) is used to analyze a range of best to worst-case scenarios for more optimal outcomes. A range of sensitivity analysis is performed to illustrate the reliability and economic impact due to EV charging, PV power production and various operating strategies. Several new reliability indices are proposed to quantify the impact of EV charging characteristics, RES penetration, and energy storage system (ESS) on the reliability performance of distribution systems. Finally, an optimization algorithm along with developed stochastic models and MCS framework is used for the optimization of the resource sizes considering EV charging stations (EVCSs) life-cycle costs, reliability and emissions.

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    Authors: Babaei, Mohammad Hossein; Wong, Stephen D.;

    Disasters significantly impact communities, often require large-scale evacuations, and damage key infrastructure (e.g., power, transportation). With growing electric vehicle (EV) adoption and electrification of transportation, governments and utilities may face significant power challenges during a disaster, especially during the evacuation stage. Low states-of-charge, sporadic charging infrastructure, or power outages could significantly hamper safe and effective evacuations. Yet, EVs also offer possible resilience benefits to emergency response by more easily charging electronics (e.g., mobile phones, radios, vital medical equipment) or sending power back to the grid through vehicle-to-grid (V2G) technology. To understand this dichotomy, this paper focuses on the use cases, benefits, and drawbacks of EVs in disasters and evacuations through a systematic review of current literature, reports, and sources. Overall, this review discovered EVs show promise as modes of transportation and mobile energy supply units during disasters and emergencies. However, crucial challenges such as charging infrastructure locations, upfront cost of resilience technologies, and user behavior remain understudied. We recommend that more dedicated research, evaluation programs, and demonstrations be conducted to overcome EV shortcomings and guide more realistic implementation of EV benefits.

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    Authors: Akhtar Hussain; Van-Hai Bui; Petr Musilek;

    Transportation electrification has the potential to reduce carbon emissions from the transport sector. However, the increased penetration of electric vehicles (EVs) can potentially overload the distribution systems. This becomes prominent in locations with multiple EV chargers and charging stations with many EVs. Therefore, this study proposes a welfare maximization-based soft actor critic (SAC) model to mitigate transformer overload in distribution systems due to the high penetration of EVs. The demand of each charging station is managed locally to avoid network overload during peak load hours in two steps. First, a welfare maximization-based optimization model is developed to maximize the welfare of electric vehicle owners by performing vehicle-to-vehicle(V2V) service. In this step, the sensitivity of EV owners to different parameters (energy level, battery degradation, and incentives provided by fleet operators) is considered. Then, a deep reinforcement learning-based method (soft-actor critic) is trained by incorporating the welfare value (obtained from the welfare maximization model) in the reward function. The total power demand (at the transformer level) and transformer capacity are also included in the reward function. The agent (fleet operator) learns the optimal pricing strategy for local demand management of EVs by interacting with the environment. Each electric vehicle responds to the action (price) by deciding the amount of power they are willing to charge/discharge (V2V) during that interval. Training is performed offline, and the trained model can be used for real-time demand management of different types of charging stations. The simulation results have shown that the proposed method can successfully manage the demand of different charging stations, via V2V, without violating the transformer capacity limits.

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    Authors: Bernard, Jérémy; Bocher, Erwan; Gousseff, Matthieu; Le Saux Wiederhold; +1 Authors

    This repository can be used to reproduced all the work described in the manuscript called "A generic algorithm to automatically classify urban fabric according to the Local Climate Zone system: implementation in GeoClimate 0.0.1 and application to French cities" Please open "Readme.txt" for further detail.

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    ZENODO
    Other ORP type . 2023
    License: CC BY
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    ZENODO
    Other ORP type . 2023
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      ZENODO
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    Authors: Hussain, Akhtar; Musilek, Petr;

    In order to maximize utilitarianism and fairness in allocating energy to electric vehicles (EVs) during outages, this study proposes an optimization method based on modified division rules. The idea of essential energy demand is introduced in this paper to maximize the number of EVs that are served during power contingencies. For EVs carrying out the time-critical task(s), the essential energy demand is defined as the amount of energy required by EVs to accomplish their upcoming task(s). For EVs carrying out delay-tolerant tasks, it is defined as the amount of energy required by EVs to travel to a nearby healthy charging station. To this end, an EV ranking mechanism is devised considering the full and essential energy demands along with the urgency of EVs. Subsequently, EVs with higher ranks are prioritized during energy allocation. The performance of the proposed method is compared with four existing division rules, i.e., proportional, constrained equal awards, constrained equal losses, and sequential priority rules. A utilitarianism index is proposed to analyze the performance of these methods and fairness is evaluated using existing indices, such as Jain’s fairness index and cost of fairness index. The proposed method has outperformed existing division rules in both utilitarianism and fairness for the essential energy demand. Finally, a sensitivity analysis of uncertain parameters such as EV fleet size, available/required energy, and weight factors is carried out to analyze the performance of the proposed method under various conditions.

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  • Authors: PRADHAN, SHAMEER KUMAR; TUNGAL, SAGAR;

    Large volume of data is generated by different systems. Intelligent systems such as autonomous driving uses such large volume of data to train their artificial intelligence models. However, good quality data is one of the foremost needs of any system to function in an effective and safe manner. Especially in critical systems such as those related with autonomous driving, quality data becomes sacrosanct as fault in such systems could result in fatal accidents. In this thesis, a Design Science Research is conducted to identify challenges related with data quality of a distributed deep learning system. The challenges are identified by conducing interviews with five experts from autonomous driving domain as well as through literature review. The challenges and their severity are validated using a survey. After identification of the challenges, five artifact components are developed that relate with assessing and improving data quality. The artifact components include Data Quality Workflow, List of Challenges, List of Data Quality Attributes, List of Data Quality Attribute Metrics, and Potential Solutions. The abstract artifact components and concrete implementation of those components are devised and validated using second round of interviews. In the third iteration of this study, the final artifact components are validated through a focus group session with experts and survey. Furthermore, the artifact also presents the information regarding which challenges affect which data quality attributes. This association between challenges and attributes are also validated in the focus group session. The results depict that most of the challenge - attribute association presumed by the researchers of this thesis are valid. Similarly, the templates developed for the artifact components are regarded as appropriate as well. A contribution of this thesis study towards the body of software engineering and requirements engineering research is the comprehensive and unified "Data Quality Assessment and Maintenance Framework" developed as a series of artifact components in this thesis. This framework can be used by researchers and practitioners to improve processes related with data quality as well as enhance data quality of the systems they develop.

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  • Authors: Elovsson Hentschel, Alicia; Zetterberg, Frida;

    Previous literature within the GVC research field have mainly focused on economic values, however, recently environmental aspects have increasingly started to be considered. Nevertheless, although road transport is a major cause of CO2 emissions, it has been overlooked in the context of greening GVCs. Therefore, the study aims to enrich this rather underinvestigated field by examining how greening of GVCs is addressed by reducing CO2 emissions from road transports with an emphasis on fossil-free fuels. This is done through a qualitative multiple-case study, taking a dynamic approach by including four lead firms (topdown perspective) and five suppliers (bottom-up perspective). The results show that the lead firms mainly adopt captive governance in relation to road carriers. The requirements set by lead firms can lead to an external drive for suppliers to environmentally upgrade, however, it can also weaken the motivation and inhibit the innovation process of suppliers to develop new fuel solutions. Furthermore, this study found that lead firm characteristics, such as if they operate B2B or B2C, influence the sustainability governance. The prerequisites for suppliers to environmentally upgrade are not only affected by the adopted governance structure by the lead firm, but also the suppliers’ characteristics as strategic intent, capabilities and competencies. In addition to providing a more holistic view to analyze greening of GVCs by taking both a topdown and bottom-up approach, this study also goes one step further in understanding and grasping the importance of the interconnectedness between lead firms and suppliers through inter-firm collaborations. MSc in International Business and Trade

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  • Authors: Back, Johanna; Svensson, Olivia; Weiler, Pauline;

    The customer journey is a complex phenomenon which consists of numerous touch points. The customer journey is not only constructed through touch points but also by the level of involvement that the journey is characterized by. Thus, the customer journey could be designed and characterized by different touch points depending on the level of involvement but also the specific market. This research aims to discover the design of the customer journey and its integrated touch points of electric vehicles in the South Korean market. The study is built upon the chronological steps; pre-purchase, purchase and post-purchase in order to derive the outline of the customer journey. In order to fulfill the aim, a qualitative research strategy was chosen. Semi-structured interviews with experts on the South Korean market and experience from the automotive industry were conducted. Furthermore, in order to strengthen and increase the reliability of the interviews an additional survey was conducted with customers operating in the South Korean market. The main findings suggest that the customer journey cannot be considered to have a consistent shape, rather it can be viewed from different perspectives. Thus, in some instances it can be viewed as linear considering the purchasing process consisting of the phases pre-purchase, purchase and post-purchase. However, it could also be viewed as circular due to the loyalty loop and retention aspects of the customer journey and lastly it can be viewed as a maze where consumers take different paths towards the same destination or could decide to abandon the journey. Furthermore, the most prominent activities in the pre-purchase phase are information search, Word of Mouth and reviews where the most important touch points are Naver, Google, Kakao and the physical meeting with a car-retailer. In the purchase phase, payment and delivery showed to be the most important activities. These would rather be carried out in a physical setting, putting emphasis on the physical meeting as the most prominent touchpoint. However, if the purchases are performed online, the company website showed to be of significance and thus an additional important touchpoint. In the post-purchase phase the touch points related to loyalty , reviews and service were most prominent. The specific touch points were loyalty programs, physical meeting in after-services and reviews on Naver Café, Naver blogs as well as pre-designed review forms. An additional finding that should be highlighted is that online touch points are more related to the pre-purchase and post-purchase phase, however the physical element still permeates the entire customer journey. MSc in Marketing and Consumption

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  • Authors: Storgärd, Kevin;

    The thesis aims to analyze how the 3PL industry can become more sustainable in its warehousing operations. The thesis presents several factors that affect a 3PL warehouse’s environmental impact through a qualitative case report. The results are based on interviews with relevant persons working within or in connection to 3PL warehousing. These interviews are analyzed in combination with a theoretical framework to ultimately answering the research questions. The conclusion structures a framework for Swedish 3PL-actors to consider when shaping their sustainable warehouses. Factors affecting a warehouse’s energy efficiency are investigated to identify which areas are of higher importance to reduce the environmental impact of the warehousing operations. Also, the thesis explores the possibilities of self-producing solar energy, revealing the current pros and cons of producing renewable energy and presenting two different approaches for developing solar energy production plants. Additionally, the future of energy storage is explored to draw a picture of where the market is today and why energy storage should be considered for all modern warehouses MSc in Logistics and Transport Management

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  • Authors: McGowan, Ellen;

    The coronavirus disease 2019 (COVID-19) has radically impacted public transport ridership and service provision across the country. Since the outbreak of the virus, transit agencies have had to adapt to new and rapidly evolving conditions. Many agencies modified services to reflect lower ridership levels and to ensure the safety of both riders and operators. These changes in service were guided by public health agencies, as well as major transit associations like the Canadian Urban Transit Association (CUTA) and International Association of Public Transport (UITP). Other agencies implemented precautionary measures like rear door boarding, temporary fare suspension, and reduced capacity limits to enable the safe continuity of operations. As the COVID-19 pandemic continues, transit agencies are having to strike a balance between providing enough transportation options for essential travel and reducing service offerings to match the declining overall demand for mobility services. Using a case study of Grand River Transit (GRT) in the Region of Waterloo, this report will document the impacts of COVID-19 on transit agencies and their responses, with a focus on modifications to services. By analyzing the challenges that transit agencies faced in modifying transit services, this report will offer guidance on the protocols and procedures that should be established for an effective pandemic response. Further, the findings of this report will help to inform discussions and guide decisions on the role and operation of public transit in future pandemic events. 

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    Authors: Pariyarath, Anand Maniyam;

    Increased penetration of electric vehicles (EVs) and renewable energy sources (RESs) in power systems can directly affect the system reliability and impose additional complexities to planning and operation due to their uncertainties. The traditional planning methods based on deterministic analysis fail to accurately capture the impact of the aforementioned uncertainty on the system reliability. In this thesis, a reliability-oriented distribution system analysis methodology that captures the complex interactions between EVs, photovoltaic (PV) power production, and energy storage is proposed. Firstly, a two-layer stochastic EV charging demand estimation model is proposed. The model comprises of a traffic layer representing the spatial-temporal distributions of EVs and an electrical network layer describing the impact of EV charging demand on electrical network. A Dynamic Hidden Markov model is used to capture the EV movements in the traffic layer. The ability of the traffic layer model to faithfully represent the random travel pattern of actual vehicles used by different types of drivers is examined. Secondly, a novel stochastic solar radiation model based on probability distributions of the first-order differences of hourly global solar horizontal radiation is proposed to calculate the stochastic power output of the PV system. Measured solar radiation data from four different locations with varying climate characteristics were used to evaluate the proposed model in comparison to two previously reported models. Additionally, various computational models such as the EV charging station model, reliability evaluation model, and economic evaluation model are developed to support the reliability and economic evaluation with necessary inputs. Monte Carlo simulation (MCS) is used to analyze a range of best to worst-case scenarios for more optimal outcomes. A range of sensitivity analysis is performed to illustrate the reliability and economic impact due to EV charging, PV power production and various operating strategies. Several new reliability indices are proposed to quantify the impact of EV charging characteristics, RES penetration, and energy storage system (ESS) on the reliability performance of distribution systems. Finally, an optimization algorithm along with developed stochastic models and MCS framework is used for the optimization of the resource sizes considering EV charging stations (EVCSs) life-cycle costs, reliability and emissions.

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