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For heat resistant alloys, long-term structural stability at high temperatures is a critical issue for alloy design and applications. In this paper, the long-term creep behaviours and structural stabilities of six heat resistant high Ni alloys and austenitic stainless steels have been studied. The longest creep rupture life is up to 359 283 hours. High Ni and Cr alloys show a good combination of high creep and oxidation resistances. Precipitation of nano MX particles with a very low growth rate improves long-term creep resistance at high temperatures. Long-term stable multiple nanoprecipitates of MX, Cu-rich, Laves and M23C6 phases can greatly contribute to the creep strength. Low Ni austenitic stainless steels show comparatively low oxidation and creep resistances. It was first found that at 800 & DEG;C, Cr2N could form in the low Ni steel with a long-term crept by the absorption of nitrogen from the air into the matrix.

In this paper, the high-temperature constitutive behaviour of an additively manufactured ductile nickel-based superalloy is investigated and modelled, with application to thermomechanical fatigue, low-cycle fatigue and creep conditions at temperatures up to 800∘800∘C. Thermomechanical fatigue tests have been performed on smooth specimens in both in-phase and out-of-phase conditions at a temperature range of 100−800∘100−800∘C, and creep tests at 625∘625∘C, 700∘700∘C, 750∘750∘C and 800∘800∘C. Additionally, low-cycle fatigue tests at different strain ranges and load ratios have been performed at 700∘700, Funding Agencies|Linkoping University and Siemens Energy AB

Increased demand for actionable knowledge in operations- and supply chain management has fuelled the interest in collaborative, action-oriented research design as well as modes of theorising that generate adaptable and actionable frameworks. Whilst action research (AR) design as well as middle-range theories (MRT) offer guiding principles herein, they are researcher centric in nature. It is taken for granted that practitioners that enter such an endeavour have a certain level of knowledge or experience prior to the initial stages of formalising the research problem. Practitioners in non-academic, operations management-intensive industries or craftsmanship-based industries, such as construction or carpeting (often in the SME range) are often neither prepared nor equipped with the principles necessary to convey their managerial challenges into collaborative research design. This risk limiting or even hindering altogether such participation. This paper elaborates on combining the logic of AR and MRT. By conceptualising a preparatory phase for initiating practitioner engagement, complementing the conventional AR cycle, a four-step approach is presented: (1) Identifying a joint interest; (2) Teaching - Awakening interest in the topic through MRT frameworks; (3) Accepting buy-in to the AR cycle and determining the problem; and (4) Proposing MRT frameworks for analysis and entering the traditional AR cycle.

Waste management in the Architecture, Engineering and Construction (AEC) industry has been a major research topic owing to the AEC industry being one of the top contributors of waste generation. However, research has primarily focused on new build and has neglected refurbishment projects which become relevant due to an aging building stock in Sweden and Europe. Various actors are involved in refurbishment projects which makes it important to study each actor as well as tensions between them. By using a service ecosystem perspective and relying on 38 interviews, this paper aims to investigate tensions and barriers between actors within the service ecosystem of CDW for refurbishment projects in Sweden. Based on the results, spatial barriers are the most mentioned barrier which also create the highest number of tensions between project and contractor mother firm, subcontractors, waste recycling firms and society/citizens. The majority of the tensions are found between projects and the contractor mother firms, followed by tensions between projects and clients and projects and subcontractors. Unlike previous studies investigating only one of the actors waste management practices, this paper contributes by investigating the interactions between seven different actors which is important for improving waste management practices in refurbishment projects., Funding Agencies|Mistra Closing the Loop, the Foundation for Strategic Environmental Research [160026]

Artificial intelligence (AI) has been widely used in robotics, automation, finance, healthcare, etc. However, using AI for decision-making in sustainable product lifecycle operations is still challenging. One major challenge relates to the scarcity and uncertainties of data across the product lifecycle. This paper aims to develop a method that can adopt the most suitable AI techniques to support decision-making for sustainable operations based on the available lifecycle data. It identifies the key lifecycle stages in which AI, especially reinforcement learning (RL), can support decision-making. Then, a generalised procedure of using RL for decision support is proposed based on available lifecycle data, such as operation and maintenance data. The method has been validated in a case study of an international vehicle manufacturer, combined with modelling and simulation. The case study demonstrates the effectiveness of the method and identifies that RL is the current most appropriate method for maintenance scheduling based on limited available lifecycle data. This paper contributes to knowledge by demonstrating an empirically grounded industrial case using RL to optimise decision-making for improved product lifecycle sustainability by effectively prolonging the product lifetime and reducing environmental impact., Funding Agencies|VinnovaVinnova [2017-01649]

Artificial intelligence (AI) has been widely used in robotics, automation, finance, healthcare, etc. However, using AI for decision-making in sustainable product lifecycle operations is still challenging. One major challenge relates to the scarcity and uncertainties of data across the product lifecycle. This paper aims to develop a method that can adopt the most suitable AI techniques to support decision-making for sustainable operations based on the available lifecycle data. It identifies the key lifecycle stages in which AI, especially reinforcement learning (RL), can support decision-making. Then, a generalised procedure of using RL for decision support is proposed based on available lifecycle data, such as operation and maintenance data. The method has been validated in a case study of an international vehicle manufacturer, combined with modelling and simulation. The case study demonstrates the effectiveness of the method and identifies that RL is the current most appropriate method for maintenance scheduling based on limited available lifecycle data. This paper contributes to knowledge by demonstrating an empirically grounded industrial case using RL to optimise decision-making for improved product lifecycle sustainability by effectively prolonging the product lifetime and reducing environmental impact., Funding Agencies|VinnovaVinnova [2017-01649]

Artificial intelligence (AI) has been widely used in robotics, automation, finance, healthcare, etc. However, using AI for decision-making in sustainable product lifecycle operations is still challenging. One major challenge relates to the scarcity and uncertainties of data across the product lifecycle. This paper aims to develop a method that can adopt the most suitable AI techniques to support decision-making for sustainable operations based on the available lifecycle data. It identifies the key lifecycle stages in which AI, especially reinforcement learning (RL), can support decision-making. Then, a generalised procedure of using RL for decision support is proposed based on available lifecycle data, such as operation and maintenance data. The method has been validated in a case study of an international vehicle manufacturer, combined with modelling and simulation. The case study demonstrates the effectiveness of the method and identifies that RL is the current most appropriate method for maintenance scheduling based on limited available lifecycle data. This paper contributes to knowledge by demonstrating an empirically grounded industrial case using RL to optimise decision-making for improved product lifecycle sustainability by effectively prolonging the product lifetime and reducing environmental impact., Funding Agencies|VinnovaVinnova [2017-01649]

Waste management in the Architecture, Engineering and Construction (AEC) industry has been a major research topic owing to the AEC industry being one of the top contributors of waste generation. However, research has primarily focused on new build and has neglected refurbishment projects which become relevant due to an aging building stock in Sweden and Europe. Various actors are involved in refurbishment projects which makes it important to study each actor as well as tensions between them. By using a service ecosystem perspective and relying on 38 interviews, this paper aims to investigate tensions and barriers between actors within the service ecosystem of CDW for refurbishment projects in Sweden. Based on the results, spatial barriers are the most mentioned barrier which also create the highest number of tensions between project and contractor mother firm, subcontractors, waste recycling firms and society/citizens. The majority of the tensions are found between projects and the contractor mother firms, followed by tensions between projects and clients and projects and subcontractors. Unlike previous studies investigating only one of the actors waste management practices, this paper contributes by investigating the interactions between seven different actors which is important for improving waste management practices in refurbishment projects., Funding Agencies|Mistra Closing the Loop, the Foundation for Strategic Environmental Research [160026]

Artificial intelligence (AI) has been widely used in robotics, automation, finance, healthcare, etc. However, using AI for decision-making in sustainable product lifecycle operations is still challenging. One major challenge relates to the scarcity and uncertainties of data across the product lifecycle. This paper aims to develop a method that can adopt the most suitable AI techniques to support decision-making for sustainable operations based on the available lifecycle data. It identifies the key lifecycle stages in which AI, especially reinforcement learning (RL), can support decision-making. Then, a generalised procedure of using RL for decision support is proposed based on available lifecycle data, such as operation and maintenance data. The method has been validated in a case study of an international vehicle manufacturer, combined with modelling and simulation. The case study demonstrates the effectiveness of the method and identifies that RL is the current most appropriate method for maintenance scheduling based on limited available lifecycle data. This paper contributes to knowledge by demonstrating an empirically grounded industrial case using RL to optimise decision-making for improved product lifecycle sustainability by effectively prolonging the product lifetime and reducing environmental impact., Funding Agencies|VinnovaVinnova [2017-01649]

Waste management in the Architecture, Engineering and Construction (AEC) industry has been a major research topic owing to the AEC industry being one of the top contributors of waste generation. However, research has primarily focused on new build and has neglected refurbishment projects which become relevant due to an aging building stock in Sweden and Europe. Various actors are involved in refurbishment projects which makes it important to study each actor as well as tensions between them. By using a service ecosystem perspective and relying on 38 interviews, this paper aims to investigate tensions and barriers between actors within the service ecosystem of CDW for refurbishment projects in Sweden. Based on the results, spatial barriers are the most mentioned barrier which also create the highest number of tensions between project and contractor mother firm, subcontractors, waste recycling firms and society/citizens. The majority of the tensions are found between projects and the contractor mother firms, followed by tensions between projects and clients and projects and subcontractors. Unlike previous studies investigating only one of the actors waste management practices, this paper contributes by investigating the interactions between seven different actors which is important for improving waste management practices in refurbishment projects., Funding Agencies|Mistra Closing the Loop, the Foundation for Strategic Environmental Research [160026]

Artificial intelligence (AI) has been widely used in robotics, automation, finance, healthcare, etc. However, using AI for decision-making in sustainable product lifecycle operations is still challenging. One major challenge relates to the scarcity and uncertainties of data across the product lifecycle. This paper aims to develop a method that can adopt the most suitable AI techniques to support decision-making for sustainable operations based on the available lifecycle data. It identifies the key lifecycle stages in which AI, especially reinforcement learning (RL), can support decision-making. Then, a generalised procedure of using RL for decision support is proposed based on available lifecycle data, such as operation and maintenance data. The method has been validated in a case study of an international vehicle manufacturer, combined with modelling and simulation. The case study demonstrates the effectiveness of the method and identifies that RL is the current most appropriate method for maintenance scheduling based on limited available lifecycle data. This paper contributes to knowledge by demonstrating an empirically grounded industrial case using RL to optimise decision-making for improved product lifecycle sustainability by effectively prolonging the product lifetime and reducing environmental impact., Funding Agencies|VinnovaVinnova [2017-01649]

Waste management in the Architecture, Engineering and Construction (AEC) industry has been a major research topic owing to the AEC industry being one of the top contributors of waste generation. However, research has primarily focused on new build and has neglected refurbishment projects which become relevant due to an aging building stock in Sweden and Europe. Various actors are involved in refurbishment projects which makes it important to study each actor as well as tensions between them. By using a service ecosystem perspective and relying on 38 interviews, this paper aims to investigate tensions and barriers between actors within the service ecosystem of CDW for refurbishment projects in Sweden. Based on the results, spatial barriers are the most mentioned barrier which also create the highest number of tensions between project and contractor mother firm, subcontractors, waste recycling firms and society/citizens. The majority of the tensions are found between projects and the contractor mother firms, followed by tensions between projects and clients and projects and subcontractors. Unlike previous studies investigating only one of the actors waste management practices, this paper contributes by investigating the interactions between seven different actors which is important for improving waste management practices in refurbishment projects., Funding Agencies|Mistra Closing the Loop, the Foundation for Strategic Environmental Research [160026]

Artificial intelligence (AI) has been widely used in robotics, automation, finance, healthcare, etc. However, using AI for decision-making in sustainable product lifecycle operations is still challenging. One major challenge relates to the scarcity and uncertainties of data across the product lifecycle. This paper aims to develop a method that can adopt the most suitable AI techniques to support decision-making for sustainable operations based on the available lifecycle data. It identifies the key lifecycle stages in which AI, especially reinforcement learning (RL), can support decision-making. Then, a generalised procedure of using RL for decision support is proposed based on available lifecycle data, such as operation and maintenance data. The method has been validated in a case study of an international vehicle manufacturer, combined with modelling and simulation. The case study demonstrates the effectiveness of the method and identifies that RL is the current most appropriate method for maintenance scheduling based on limited available lifecycle data. This paper contributes to knowledge by demonstrating an empirically grounded industrial case using RL to optimise decision-making for improved product lifecycle sustainability by effectively prolonging the product lifetime and reducing environmental impact., Funding Agencies|VinnovaVinnova [2017-01649]

Waste management in the Architecture, Engineering and Construction (AEC) industry has been a major research topic owing to the AEC industry being one of the top contributors of waste generation. However, research has primarily focused on new build and has neglected refurbishment projects which become relevant due to an aging building stock in Sweden and Europe. Various actors are involved in refurbishment projects which makes it important to study each actor as well as tensions between them. By using a service ecosystem perspective and relying on 38 interviews, this paper aims to investigate tensions and barriers between actors within the service ecosystem of CDW for refurbishment projects in Sweden. Based on the results, spatial barriers are the most mentioned barrier which also create the highest number of tensions between project and contractor mother firm, subcontractors, waste recycling firms and society/citizens. The majority of the tensions are found between projects and the contractor mother firms, followed by tensions between projects and clients and projects and subcontractors. Unlike previous studies investigating only one of the actors waste management practices, this paper contributes by investigating the interactions between seven different actors which is important for improving waste management practices in refurbishment projects., Funding Agencies|Mistra Closing the Loop, the Foundation for Strategic Environmental Research [160026]

The exact distribution of a classification function is often complicated to allow for easy numerical calculations of misclassification errors. The use of expansions is one way of dealing with this difficulty. In this paper, approximate probabilities of misclassification of the maximum likelihood-based discriminant function are established via an Edgeworth-type expansion based on the standard normal distribution for discriminating between two multivariate normal populations.

The exact distribution of a classification function is often complicated to allow for easy numerical calculations of misclassification errors. The use of expansions is one way of dealing with this difficulty. In this paper, approximate probabilities of misclassification of the maximum likelihood-based discriminant function are established via an Edgeworth-type expansion based on the standard normal distribution for discriminating between two multivariate normal populations.

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem., Funding Agencies|Swedish Transport Administration [TRV2018/132473, TRV2021/22404]; Swedish Energy Agency [46963-1]

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem., Funding Agencies|Swedish Transport Administration [TRV2018/132473, TRV2021/22404]; Swedish Energy Agency [46963-1]

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem., Funding Agencies|Swedish Transport Administration [TRV2018/132473, TRV2021/22404]; Swedish Energy Agency [46963-1]

The exact distribution of a classification function is often complicated to allow for easy numerical calculations of misclassification errors. The use of expansions is one way of dealing with this difficulty. In this paper, approximate probabilities of misclassification of the maximum likelihood-based discriminant function are established via an Edgeworth-type expansion based on the standard normal distribution for discriminating between two multivariate normal populations.

The exact distribution of a classification function is often complicated to allow for easy numerical calculations of misclassification errors. The use of expansions is one way of dealing with this difficulty. In this paper, approximate probabilities of misclassification of the maximum likelihood-based discriminant function are established via an Edgeworth-type expansion based on the standard normal distribution for discriminating between two multivariate normal populations.

The exact distribution of a classification function is often complicated to allow for easy numerical calculations of misclassification errors. The use of expansions is one way of dealing with this difficulty. In this paper, approximate probabilities of misclassification of the maximum likelihood-based discriminant function are established via an Edgeworth-type expansion based on the standard normal distribution for discriminating between two multivariate normal populations.

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem., Funding Agencies|Swedish Transport Administration [TRV2018/132473, TRV2021/22404]; Swedish Energy Agency [46963-1]

Time-dependent Origin-Destination (OD) matrices are an essential input to transportation models. A cost-efficient and widely used approach for estimating OD matrices involves the exploitation of flow counts from stationary traffic detectors. This estimation approach is also referred to as assignment-based OD matrix estimation because, typically, Dynamic Traffic Assignment (DTA) models are used to map the OD matrix to the link flows. The conventional DTA establish a complex non-linear relationship between the demand, and the link flows, adding an inherent complexity to the OD matrix estimation problem. In this paper, attempting to exploit the growing availability of Floating-Car Data (FCD), we suggest a solution approach that is based on a Data-Driven Network Assignment (DDNA) mechanism. The DDNA utilises the FCD from probe vehicles to capture congestion effects, providing a linear mapping of the OD matrix to the link flow observations. We present the results of two synthetic-data experiments that serve as proof of concept, indicating that if FCD are available, the computationally costly DTA may not be necessary for solving the OD matrix estimation problem., Funding Agencies|Swedish Transport Administration [TRV2018/132473, TRV2021/22404]; Swedish Energy Agency [46963-1]

The Cauchy problem for the Helmholtz equation is considered. It was demonstrated in a previous paper by the authors that the alternating algorithm suggested by V.A. Kozlov and V.G. Mazya does not converge for large wavenumbers k in the Helmholtz equation. Here, we present some simple modifications of the algorithm which may restore the convergence. They consist of the replacement of the Neumann-Dirichlet iterations by the Robin-Dirichlet ones which repairs the convergence for less than the first Dirichlet-Laplacian eigenvalue. In order to treat large wavenumbers, we present an algorithm based on iterative solution of Robin-Dirichlet boundary value problems in a sufficiently narrow border strip. Numerical implementations obtained using the finite difference method are presented. The numerical results illustrate that the algorithms suggested in this paper, produce convergent iterative sequences., Funding Agencies|Swedish International Development Cooperation Agency (Sida); University of Rwanda (UR) [51160027-02, 51160059-02]

The purpose of this paper is to introduce and explore the use of paradox theory in a logistics and SCM research context. Although logistics and SCM scholars as well as practitioners have been aware about the existence of paradoxes, they have so far lacked a more formal theoretical framework for further identification and classification of these. Framed in the four general paradox classes of learning, belonging, organising and performing, the paper outlines paradoxes inherent in the global sourcing practices of two case companies. The findings demonstrate the paradox theory to be a useful tool for a systematic illumination and classification of paradoxes valuable for practitioners, researchers as well as teachers in the domain of logistics and SCM. The paradox theory may also help to identify and emphasise inconsistencies among different research fields. This is particularly valuable for research areas built upon multiple research disciplines such as global sourcing. In conclusion, the paper suggests that the paradox theory offers a new lens through which an organisation could be better explored, described and understood., Funding Agencies|Jan Wallander and Tom Hedelius Foundation; Swedish Retail and Wholesale Development Council

The purpose of this paper is to introduce and explore the use of paradox theory in a logistics and SCM research context. Although logistics and SCM scholars as well as practitioners have been aware about the existence of paradoxes, they have so far lacked a more formal theoretical framework for further identification and classification of these. Framed in the four general paradox classes of learning, belonging, organising and performing, the paper outlines paradoxes inherent in the global sourcing practices of two case companies. The findings demonstrate the paradox theory to be a useful tool for a systematic illumination and classification of paradoxes valuable for practitioners, researchers as well as teachers in the domain of logistics and SCM. The paradox theory may also help to identify and emphasise inconsistencies among different research fields. This is particularly valuable for research areas built upon multiple research disciplines such as global sourcing. In conclusion, the paper suggests that the paradox theory offers a new lens through which an organisation could be better explored, described and understood., Funding Agencies|Jan Wallander and Tom Hedelius Foundation; Swedish Retail and Wholesale Development Council

A common issue with many system identification problems is that the true input to the system is unknown. This paper extends a previously presented indirect modelling framework that deals with identification of systems where the input is partially or fully unknown. In this framework, unknown inputs are eliminated by using additional measurements that directly or indirectly contain information about the unknown inputs. The resulting indirect predictor model is only dependent on known and measured signals and can be used to estimate the desired dynamics or properties. Since the input of the indirect model contains both known inputs and measurements that could all be correlated with the same disturbances as the output, estimation of the indirect model has similar challenges as a closed-loop estimation problem. In fact, due to the generality of the indirect modelling framework, it unifies a number of already existing system identification problems that are contained as special cases. For completeness, the paper is concluded with one method that can be used to estimate the indirect model as well as an experimental verification to show the applicability of the framework., Funding Agencies|Vinnova Industry Excellence Center LINK-SIC project [2007-02224]

A common issue with many system identification problems is that the true input to the system is unknown. This paper extends a previously presented indirect modelling framework that deals with identification of systems where the input is partially or fully unknown. In this framework, unknown inputs are eliminated by using additional measurements that directly or indirectly contain information about the unknown inputs. The resulting indirect predictor model is only dependent on known and measured signals and can be used to estimate the desired dynamics or properties. Since the input of the indirect model contains both known inputs and measurements that could all be correlated with the same disturbances as the output, estimation of the indirect model has similar challenges as a closed-loop estimation problem. In fact, due to the generality of the indirect modelling framework, it unifies a number of already existing system identification problems that are contained as special cases. For completeness, the paper is concluded with one method that can be used to estimate the indirect model as well as an experimental verification to show the applicability of the framework., Funding Agencies|Vinnova Industry Excellence Center LINK-SIC project [2007-02224]

The purpose of this paper is to introduce and explore the use of paradox theory in a logistics and SCM research context. Although logistics and SCM scholars as well as practitioners have been aware about the existence of paradoxes, they have so far lacked a more formal theoretical framework for further identification and classification of these. Framed in the four general paradox classes of learning, belonging, organising and performing, the paper outlines paradoxes inherent in the global sourcing practices of two case companies. The findings demonstrate the paradox theory to be a useful tool for a systematic illumination and classification of paradoxes valuable for practitioners, researchers as well as teachers in the domain of logistics and SCM. The paradox theory may also help to identify and emphasise inconsistencies among different research fields. This is particularly valuable for research areas built upon multiple research disciplines such as global sourcing. In conclusion, the paper suggests that the paradox theory offers a new lens through which an organisation could be better explored, described and understood., Funding Agencies|Jan Wallander and Tom Hedelius Foundation; Swedish Retail and Wholesale Development Council

A common issue with many system identification problems is that the true input to the system is unknown. This paper extends a previously presented indirect modelling framework that deals with identification of systems where the input is partially or fully unknown. In this framework, unknown inputs are eliminated by using additional measurements that directly or indirectly contain information about the unknown inputs. The resulting indirect predictor model is only dependent on known and measured signals and can be used to estimate the desired dynamics or properties. Since the input of the indirect model contains both known inputs and measurements that could all be correlated with the same disturbances as the output, estimation of the indirect model has similar challenges as a closed-loop estimation problem. In fact, due to the generality of the indirect modelling framework, it unifies a number of already existing system identification problems that are contained as special cases. For completeness, the paper is concluded with one method that can be used to estimate the indirect model as well as an experimental verification to show the applicability of the framework., Funding Agencies|Vinnova Industry Excellence Center LINK-SIC project [2007-02224]

A common issue with many system identification problems is that the true input to the system is unknown. This paper extends a previously presented indirect modelling framework that deals with identification of systems where the input is partially or fully unknown. In this framework, unknown inputs are eliminated by using additional measurements that directly or indirectly contain information about the unknown inputs. The resulting indirect predictor model is only dependent on known and measured signals and can be used to estimate the desired dynamics or properties. Since the input of the indirect model contains both known inputs and measurements that could all be correlated with the same disturbances as the output, estimation of the indirect model has similar challenges as a closed-loop estimation problem. In fact, due to the generality of the indirect modelling framework, it unifies a number of already existing system identification problems that are contained as special cases. For completeness, the paper is concluded with one method that can be used to estimate the indirect model as well as an experimental verification to show the applicability of the framework., Funding Agencies|Vinnova Industry Excellence Center LINK-SIC project [2007-02224]

A common issue with many system identification problems is that the true input to the system is unknown. This paper extends a previously presented indirect modelling framework that deals with identification of systems where the input is partially or fully unknown. In this framework, unknown inputs are eliminated by using additional measurements that directly or indirectly contain information about the unknown inputs. The resulting indirect predictor model is only dependent on known and measured signals and can be used to estimate the desired dynamics or properties. Since the input of the indirect model contains both known inputs and measurements that could all be correlated with the same disturbances as the output, estimation of the indirect model has similar challenges as a closed-loop estimation problem. In fact, due to the generality of the indirect modelling framework, it unifies a number of already existing system identification problems that are contained as special cases. For completeness, the paper is concluded with one method that can be used to estimate the indirect model as well as an experimental verification to show the applicability of the framework., Funding Agencies|Vinnova Industry Excellence Center LINK-SIC project [2007-02224]

In this paper, we discuss the impact of using a frequency-dependent embedding potential in quantum chemical embedding calculations of response properties. We show that the introduction of a frequency-dependent embedding potential leads to further model complications upon solving the central equations defining specific molecular properties. On the other hand, we also show from a numerical point of view that the consequences of using such a frequency-dependent embedding potential is almost negligible. Thus, for the kind of systems and processes studied in this paper the general recommendation is to use frequency-independent embedding potentials since this leads to less complicated model issues. However, larger effects are expected if the absorption bands of the environment are closer to that of the region treated using quantum mechanics. [GRAPHICS], Funding Agencies|Danish Councils for Independent Research (DFF) [FI-0602-02122B]; Villum Foundation; DeIC National HPC Center, SDU; Swedish Research Council [621-2014-4646]

The purpose of this paper is to introduce and explore the use of paradox theory in a logistics and SCM research context. Although logistics and SCM scholars as well as practitioners have been aware about the existence of paradoxes, they have so far lacked a more formal theoretical framework for further identification and classification of these. Framed in the four general paradox classes of learning, belonging, organising and performing, the paper outlines paradoxes inherent in the global sourcing practices of two case companies. The findings demonstrate the paradox theory to be a useful tool for a systematic illumination and classification of paradoxes valuable for practitioners, researchers as well as teachers in the domain of logistics and SCM. The paradox theory may also help to identify and emphasise inconsistencies among different research fields. This is particularly valuable for research areas built upon multiple research disciplines such as global sourcing. In conclusion, the paper suggests that the paradox theory offers a new lens through which an organisation could be better explored, described and understood., Funding Agencies|Jan Wallander and Tom Hedelius Foundation; Swedish Retail and Wholesale Development Council

The purpose of this paper is to introduce and explore the use of paradox theory in a logistics and SCM research context. Although logistics and SCM scholars as well as practitioners have been aware about the existence of paradoxes, they have so far lacked a more formal theoretical framework for further identification and classification of these. Framed in the four general paradox classes of learning, belonging, organising and performing, the paper outlines paradoxes inherent in the global sourcing practices of two case companies. The findings demonstrate the paradox theory to be a useful tool for a systematic illumination and classification of paradoxes valuable for practitioners, researchers as well as teachers in the domain of logistics and SCM. The paradox theory may also help to identify and emphasise inconsistencies among different research fields. This is particularly valuable for research areas built upon multiple research disciplines such as global sourcing. In conclusion, the paper suggests that the paradox theory offers a new lens through which an organisation could be better explored, described and understood., Funding Agencies|Jan Wallander and Tom Hedelius Foundation; Swedish Retail and Wholesale Development Council

The paper discusses new cubature formulas for the Riesz potential and the fractional Laplacian (-Delta)(alpha/2), 0 < alpha < 2, in the framework of the method approximate approximations. This approach, combined with separated representations, makes the method successful also in high dimensions. We prove error estimates and report on numerical results illustrating that our formulas are accurate and provide the predicted convergence rate 2, 4, 6, 8 up to dimension 10(4)., Funding Agencies|RUDN University Strategic Academic Leadership Program

MRP theory is a theoretical body treating production-inventory systems, in which produced items are made up of sets of produced or purchased sub-items, required to be available a lead-time before each product is completed. The hierarchical dependence between items is captured using input matrices from input-output-analysis, the necessary advanced timing by employing Laplace transform methodology, and the economic consequences by applying the net present value. Little attention has hitherto been given to aspects of a non-empty initial state, e.g. an initial inventory position. Since such states are common in industry, there is a strong need for this theory to include such aspects, in order to gain further practical acceptance. In this paper, theoretical consequences from having a non-empty initial state are investigated. A method for finding the Lot-for-Lot solution is developed using the concept of a truncated monotonically non-decreasing time function, generalising the approach of the generalised Leontief inverse and instrumental for designing plans meeting the necessary inner-corner requirement for optimality. Also the definition of inventory-related costs needs a modification for this concept to be consistent with NPV. These findings are applied in an extensive numerical example. Immediate future research concerns investigating principles for the optimal removal of initial backlogs.

In this paper, we consider the estimated weights of the tangency portfolio. We derive analytical expressions for the higher order non-central and central moments of these weights when the returns are assumed to be independently and multivariate normally distributed. Moreover, the expressions for mean, variance, skewness and kurtosis of the estimated weights are obtained in closed forms. Later, we complement our results with a simulation study where data from the multivariate normal and t-distributions are simulated, and the first four moments of estimated weights are computed by using the Monte Carlo experiment. It is noteworthy to mention that the distributional assumption of returns is found to be important, especially for the first two moments. Finally, through an empirical illustration utilizing returns of four financial indices listed in NASDAQ stock exchange, we observe the presence of time dynamics in higher moments., Funding Agencies|Orebro University; Jan Wallander and Tom Hedelius Foundation [P18-0201]

In this paper, we consider the estimated weights of the tangency portfolio. We derive analytical expressions for the higher order non-central and central moments of these weights when the returns are assumed to be independently and multivariate normally distributed. Moreover, the expressions for mean, variance, skewness and kurtosis of the estimated weights are obtained in closed forms. Later, we complement our results with a simulation study where data from the multivariate normal and t-distributions are simulated, and the first four moments of estimated weights are computed by using the Monte Carlo experiment. It is noteworthy to mention that the distributional assumption of returns is found to be important, especially for the first two moments. Finally, through an empirical illustration utilizing returns of four financial indices listed in NASDAQ stock exchange, we observe the presence of time dynamics in higher moments., Funding Agencies|Orebro University; Jan Wallander and Tom Hedelius Foundation [P18-0201]

Printed electronics implies the use of low-cost, scalable, printing technologies to fabricate electronic devices and circuits on flexible substrates, such as paper or plastics. The development of this new electronic is currently expanding because of the emergence of the internet-of-everything. Although lot of attention has been paid to functional inks based on organic semiconductors, another class of inks is based on nanoparticles obtained from exfoliated 2D materials, such as graphene and metal sulfides. The ultimate scientific and technological challenge is to find a strategy where the exfoliated nanoparticle flakes in the inks can, after solvent evaporation, form a solid which displays performances equal to the single crystal of the 2D material. In this context, a printed layer, formed from an ink composed of nano-flakes of TiS2 intercalated with hexylamine, which displays thermoelectric properties superior to organic intercalated TiS2 single crystals, is demonstrated for the first time. The choice of the fraction of exfoliated nano-flakes appears to be a key to the forming of a new self-organized layered material by solvent evaporation. The printed layer is an efficient n-type thermoelectric material which complements the p-type printable organic semiconductors The thermoelectric power factor of the printed TiS2/hexylamine thin films reach record values of 1460 mu W m(-1) K-2 at 430 K, this is considerably higher than the high value of 900 mu W m(-1) K-2 at 300 K reported for a single crystal. A printed thermoelectric generator based on eight legs of TiS2 confirms the high-power factor values by generating a power density of 16.0 W m(-2) at Delta T = 40 K., Funding Agencies|Knut och Alice Wallenbergs StiftelseKnut & Alice Wallenberg Foundation

In light of a circular economy, to encourage core returns, the remanufacturer charges a deposit and refund it to the customer based on quality inspection of cores. Generally, two types of classification errors exist and interact with each other during the inspection process: either low-quality cores are sorted as remanufacturable, or high-quality cores are sorted as non-remanufacturable. The remanufacturer needs to choose refund policies and determine a reasonable deposit value, considering customers potential responses. This paper firstly develops analytical solutions for these issues within a game theory framework. The effect of inspection information transparency is evaluated by comparing two settings: the information of inspection errors is available to customers or not. The study results show the advantage of inspection information transparency from the remanufacturers perspective. The analysis indicates the importance of avoiding overestimating customers payoff of products and the significance of inspection accuracy. The study also highlights that the salvage value of different cores significantly influences the remanufacturers profits, and the improvement of inspection accuracy does not necessarily reduce the customers return of low-quality cores.

Printed electronics implies the use of low-cost, scalable, printing technologies to fabricate electronic devices and circuits on flexible substrates, such as paper or plastics. The development of this new electronic is currently expanding because of the emergence of the internet-of-everything. Although lot of attention has been paid to functional inks based on organic semiconductors, another class of inks is based on nanoparticles obtained from exfoliated 2D materials, such as graphene and metal sulfides. The ultimate scientific and technological challenge is to find a strategy where the exfoliated nanoparticle flakes in the inks can, after solvent evaporation, form a solid which displays performances equal to the single crystal of the 2D material. In this context, a printed layer, formed from an ink composed of nano-flakes of TiS2 intercalated with hexylamine, which displays thermoelectric properties superior to organic intercalated TiS2 single crystals, is demonstrated for the first time. The choice of the fraction of exfoliated nano-flakes appears to be a key to the forming of a new self-organized layered material by solvent evaporation. The printed layer is an efficient n-type thermoelectric material which complements the p-type printable organic semiconductors The thermoelectric power factor of the printed TiS2/hexylamine thin films reach record values of 1460 mu W m(-1) K-2 at 430 K, this is considerably higher than the high value of 900 mu W m(-1) K-2 at 300 K reported for a single crystal. A printed thermoelectric generator based on eight legs of TiS2 confirms the high-power factor values by generating a power density of 16.0 W m(-2) at Delta T = 40 K., Funding Agencies|Knut och Alice Wallenbergs StiftelseKnut & Alice Wallenberg Foundation

Dynamic Network Loading (DNL) models are typically formulated as a system of differential equations where travel times, densities or any other variable that indicates congestion is endogenous. However, such endogeneities increase the complexity of the Dynamic Traffic Assignment (DTA) problem due to the interdependence of DNL, route choice and demand. In this paper, attempting to exploit the growing accessibility of traffic-related data, we suggest that congestion can be instead captured by exogenous variables, such as travel time observations. We propagate the traffic flow based on an exogenous travel time function, which has a piece-wise linear form. Given piece-wise stationary route flows, the piece-wise linear form of the travel time function allows us to use an efficient event-based modelling structure. Our Data-Driven Network Loading (DDNL) approach is developed in accordance with the theoretical DNL framework ensuring vehicle conservation and FIFO. The first simulation experiment-based results are encouraging, indicating that the DDNL can contribute to improving the efficiency of applications where the monitoring of historical network-wide flows is required. Abbreviations: DDNL - Data Driven Network Loading; DNL - Dynamic Network Loading; DTA - Dynamic Traffic Assignment; ITS - Intelligent Transportation Systems; OD - Origin Destination; TTF - Travel Time Function; LTT - Linear Travel Time; DL - Demand level, Funding Agencies|Swedish Energy AgencySwedish Energy Agency [46963-1]

Printed electronics implies the use of low-cost, scalable, printing technologies to fabricate electronic devices and circuits on flexible substrates, such as paper or plastics. The development of this new electronic is currently expanding because of the emergence of the internet-of-everything. Although lot of attention has been paid to functional inks based on organic semiconductors, another class of inks is based on nanoparticles obtained from exfoliated 2D materials, such as graphene and metal sulfides. The ultimate scientific and technological challenge is to find a strategy where the exfoliated nanoparticle flakes in the inks can, after solvent evaporation, form a solid which displays performances equal to the single crystal of the 2D material. In this context, a printed layer, formed from an ink composed of nano-flakes of TiS2 intercalated with hexylamine, which displays thermoelectric properties superior to organic intercalated TiS2 single crystals, is demonstrated for the first time. The choice of the fraction of exfoliated nano-flakes appears to be a key to the forming of a new self-organized layered material by solvent evaporation. The printed layer is an efficient n-type thermoelectric material which complements the p-type printable organic semiconductors The thermoelectric power factor of the printed TiS2/hexylamine thin films reach record values of 1460 mu W m(-1) K-2 at 430 K, this is considerably higher than the high value of 900 mu W m(-1) K-2 at 300 K reported for a single crystal. A printed thermoelectric generator based on eight legs of TiS2 confirms the high-power factor values by generating a power density of 16.0 W m(-2) at Delta T = 40 K., Funding Agencies|Knut och Alice Wallenbergs StiftelseKnut & Alice Wallenberg Foundation

Dynamic Network Loading (DNL) models are typically formulated as a system of differential equations where travel times, densities or any other variable that indicates congestion is endogenous. However, such endogeneities increase the complexity of the Dynamic Traffic Assignment (DTA) problem due to the interdependence of DNL, route choice and demand. In this paper, attempting to exploit the growing accessibility of traffic-related data, we suggest that congestion can be instead captured by exogenous variables, such as travel time observations. We propagate the traffic flow based on an exogenous travel time function, which has a piece-wise linear form. Given piece-wise stationary route flows, the piece-wise linear form of the travel time function allows us to use an efficient event-based modelling structure. Our Data-Driven Network Loading (DDNL) approach is developed in accordance with the theoretical DNL framework ensuring vehicle conservation and FIFO. The first simulation experiment-based results are encouraging, indicating that the DDNL can contribute to improving the efficiency of applications where the monitoring of historical network-wide flows is required. Abbreviations: DDNL - Data Driven Network Loading; DNL - Dynamic Network Loading; DTA - Dynamic Traffic Assignment; ITS - Intelligent Transportation Systems; OD - Origin Destination; TTF - Travel Time Function; LTT - Linear Travel Time; DL - Demand level, Funding Agencies|Swedish Energy AgencySwedish Energy Agency [46963-1]

In light of a circular economy, to encourage core returns, the remanufacturer charges a deposit and refund it to the customer based on quality inspection of cores. Generally, two types of classification errors exist and interact with each other during the inspection process: either low-quality cores are sorted as remanufacturable, or high-quality cores are sorted as non-remanufacturable. The remanufacturer needs to choose refund policies and determine a reasonable deposit value, considering customers potential responses. This paper firstly develops analytical solutions for these issues within a game theory framework. The effect of inspection information transparency is evaluated by comparing two settings: the information of inspection errors is available to customers or not. The study results show the advantage of inspection information transparency from the remanufacturers perspective. The analysis indicates the importance of avoiding overestimating customers payoff of products and the significance of inspection accuracy. The study also highlights that the salvage value of different cores significantly influences the remanufacturers profits, and the improvement of inspection accuracy does not necessarily reduce the customers return of low-quality cores.

In light of a circular economy, to encourage core returns, the remanufacturer charges a deposit and refund it to the customer based on quality inspection of cores. Generally, two types of classification errors exist and interact with each other during the inspection process: either low-quality cores are sorted as remanufacturable, or high-quality cores are sorted as non-remanufacturable. The remanufacturer needs to choose refund policies and determine a reasonable deposit value, considering customers potential responses. This paper firstly develops analytical solutions for these issues within a game theory framework. The effect of inspection information transparency is evaluated by comparing two settings: the information of inspection errors is available to customers or not. The study results show the advantage of inspection information transparency from the remanufacturers perspective. The analysis indicates the importance of avoiding overestimating customers payoff of products and the significance of inspection accuracy. The study also highlights that the salvage value of different cores significantly influences the remanufacturers profits, and the improvement of inspection accuracy does not necessarily reduce the customers return of low-quality cores.

Printed electronics implies the use of low-cost, scalable, printing technologies to fabricate electronic devices and circuits on flexible substrates, such as paper or plastics. The development of this new electronic is currently expanding because of the emergence of the internet-of-everything. Although lot of attention has been paid to functional inks based on organic semiconductors, another class of inks is based on nanoparticles obtained from exfoliated 2D materials, such as graphene and metal sulfides. The ultimate scientific and technological challenge is to find a strategy where the exfoliated nanoparticle flakes in the inks can, after solvent evaporation, form a solid which displays performances equal to the single crystal of the 2D material. In this context, a printed layer, formed from an ink composed of nano-flakes of TiS2 intercalated with hexylamine, which displays thermoelectric properties superior to organic intercalated TiS2 single crystals, is demonstrated for the first time. The choice of the fraction of exfoliated nano-flakes appears to be a key to the forming of a new self-organized layered material by solvent evaporation. The printed layer is an efficient n-type thermoelectric material which complements the p-type printable organic semiconductors The thermoelectric power factor of the printed TiS2/hexylamine thin films reach record values of 1460 mu W m(-1) K-2 at 430 K, this is considerably higher than the high value of 900 mu W m(-1) K-2 at 300 K reported for a single crystal. A printed thermoelectric generator based on eight legs of TiS2 confirms the high-power factor values by generating a power density of 16.0 W m(-2) at Delta T = 40 K., Funding Agencies|Knut och Alice Wallenbergs StiftelseKnut & Alice Wallenberg Foundation

Dynamic Network Loading (DNL) models are typically formulated as a system of differential equations where travel times, densities or any other variable that indicates congestion is endogenous. However, such endogeneities increase the complexity of the Dynamic Traffic Assignment (DTA) problem due to the interdependence of DNL, route choice and demand. In this paper, attempting to exploit the growing accessibility of traffic-related data, we suggest that congestion can be instead captured by exogenous variables, such as travel time observations. We propagate the traffic flow based on an exogenous travel time function, which has a piece-wise linear form. Given piece-wise stationary route flows, the piece-wise linear form of the travel time function allows us to use an efficient event-based modelling structure. Our Data-Driven Network Loading (DDNL) approach is developed in accordance with the theoretical DNL framework ensuring vehicle conservation and FIFO. The first simulation experiment-based results are encouraging, indicating that the DDNL can contribute to improving the efficiency of applications where the monitoring of historical network-wide flows is required. Abbreviations: DDNL - Data Driven Network Loading; DNL - Dynamic Network Loading; DTA - Dynamic Traffic Assignment; ITS - Intelligent Transportation Systems; OD - Origin Destination; TTF - Travel Time Function; LTT - Linear Travel Time; DL - Demand level, Funding Agencies|Swedish Energy AgencySwedish Energy Agency [46963-1]