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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]

In the past two decades, there has been ongoing development of corporate sustainability reporting frameworks to increase awareness of sustainability-related issues from various company stakeholders. At the same time, major reporting frameworks dealing with sustainability issues increasingly publish guides to support corporations Sustainability Development Goals (SDGs) commitments. For corporations, it is challenging to use these guides to obtain the highest benefits for fulfilling corporate sustainability targets and provide valuable reporting on these for its stakeholders. This paper aims to provide a structural alignment approach and a robust alignment methodology that corporations can repeatedly use to harmonize the SDGs guides of the Global Reporting Initiative (GRI) and the International Integrated Reporting Council (IIRC) to satisfy their evolving SDGs reporting challenges. The corporate sustainability assessment and reporting performance of four telecommunication companies on their SDGs commitment and their SDGs reporting challenges are examined to support them with tailor-made guidance established by aligning various SDGs guides of GRI and IIRC. The alignment process is carried out by utilizing the alignment framework and the alignment approach established by the paper. By applying the proposed alignment approach and alignment framework, the paper concludes that corporations can increase their SDGs reporting performance by satisfying their SDGs challenges with the deliverables of the SDGs guides. It adds value to strategic corporate sustainability literature by analyzing the relationship among companies SDGs approaches in the same sector and supporting the various SDGs guides of GRI and IIRC by applying the alignment approach established in this paper.

In the past two decades, there has been ongoing development of corporate sustainability reporting frameworks to increase awareness of sustainability-related issues from various company stakeholders. At the same time, major reporting frameworks dealing with sustainability issues increasingly publish guides to support corporations Sustainability Development Goals (SDGs) commitments. For corporations, it is challenging to use these guides to obtain the highest benefits for fulfilling corporate sustainability targets and provide valuable reporting on these for its stakeholders. This paper aims to provide a structural alignment approach and a robust alignment methodology that corporations can repeatedly use to harmonize the SDGs guides of the Global Reporting Initiative (GRI) and the International Integrated Reporting Council (IIRC) to satisfy their evolving SDGs reporting challenges. The corporate sustainability assessment and reporting performance of four telecommunication companies on their SDGs commitment and their SDGs reporting challenges are examined to support them with tailor-made guidance established by aligning various SDGs guides of GRI and IIRC. The alignment process is carried out by utilizing the alignment framework and the alignment approach established by the paper. By applying the proposed alignment approach and alignment framework, the paper concludes that corporations can increase their SDGs reporting performance by satisfying their SDGs challenges with the deliverables of the SDGs guides. It adds value to strategic corporate sustainability literature by analyzing the relationship among companies SDGs approaches in the same sector and supporting the various SDGs guides of GRI and IIRC by applying the alignment approach established in this paper.

In the past two decades, there has been ongoing development of corporate sustainability reporting frameworks to increase awareness of sustainability-related issues from various company stakeholders. At the same time, major reporting frameworks dealing with sustainability issues increasingly publish guides to support corporations Sustainability Development Goals (SDGs) commitments. For corporations, it is challenging to use these guides to obtain the highest benefits for fulfilling corporate sustainability targets and provide valuable reporting on these for its stakeholders. This paper aims to provide a structural alignment approach and a robust alignment methodology that corporations can repeatedly use to harmonize the SDGs guides of the Global Reporting Initiative (GRI) and the International Integrated Reporting Council (IIRC) to satisfy their evolving SDGs reporting challenges. The corporate sustainability assessment and reporting performance of four telecommunication companies on their SDGs commitment and their SDGs reporting challenges are examined to support them with tailor-made guidance established by aligning various SDGs guides of GRI and IIRC. The alignment process is carried out by utilizing the alignment framework and the alignment approach established by the paper. By applying the proposed alignment approach and alignment framework, the paper concludes that corporations can increase their SDGs reporting performance by satisfying their SDGs challenges with the deliverables of the SDGs guides. It adds value to strategic corporate sustainability literature by analyzing the relationship among companies SDGs approaches in the same sector and supporting the various SDGs guides of GRI and IIRC by applying the alignment approach established in this paper.

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]

In the past two decades, there has been ongoing development of corporate sustainability reporting frameworks to increase awareness of sustainability-related issues from various company stakeholders. At the same time, major reporting frameworks dealing with sustainability issues increasingly publish guides to support corporations Sustainability Development Goals (SDGs) commitments. For corporations, it is challenging to use these guides to obtain the highest benefits for fulfilling corporate sustainability targets and provide valuable reporting on these for its stakeholders. This paper aims to provide a structural alignment approach and a robust alignment methodology that corporations can repeatedly use to harmonize the SDGs guides of the Global Reporting Initiative (GRI) and the International Integrated Reporting Council (IIRC) to satisfy their evolving SDGs reporting challenges. The corporate sustainability assessment and reporting performance of four telecommunication companies on their SDGs commitment and their SDGs reporting challenges are examined to support them with tailor-made guidance established by aligning various SDGs guides of GRI and IIRC. The alignment process is carried out by utilizing the alignment framework and the alignment approach established by the paper. By applying the proposed alignment approach and alignment framework, the paper concludes that corporations can increase their SDGs reporting performance by satisfying their SDGs challenges with the deliverables of the SDGs guides. It adds value to strategic corporate sustainability literature by analyzing the relationship among companies SDGs approaches in the same sector and supporting the various SDGs guides of GRI and IIRC by applying the alignment approach established in this paper.

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]

In the past two decades, there has been ongoing development of corporate sustainability reporting frameworks to increase awareness of sustainability-related issues from various company stakeholders. At the same time, major reporting frameworks dealing with sustainability issues increasingly publish guides to support corporations Sustainability Development Goals (SDGs) commitments. For corporations, it is challenging to use these guides to obtain the highest benefits for fulfilling corporate sustainability targets and provide valuable reporting on these for its stakeholders. This paper aims to provide a structural alignment approach and a robust alignment methodology that corporations can repeatedly use to harmonize the SDGs guides of the Global Reporting Initiative (GRI) and the International Integrated Reporting Council (IIRC) to satisfy their evolving SDGs reporting challenges. The corporate sustainability assessment and reporting performance of four telecommunication companies on their SDGs commitment and their SDGs reporting challenges are examined to support them with tailor-made guidance established by aligning various SDGs guides of GRI and IIRC. The alignment process is carried out by utilizing the alignment framework and the alignment approach established by the paper. By applying the proposed alignment approach and alignment framework, the paper concludes that corporations can increase their SDGs reporting performance by satisfying their SDGs challenges with the deliverables of the SDGs guides. It adds value to strategic corporate sustainability literature by analyzing the relationship among companies SDGs approaches in the same sector and supporting the various SDGs guides of GRI and IIRC by applying the alignment approach established in this paper.

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]

In the past two decades, there has been ongoing development of corporate sustainability reporting frameworks to increase awareness of sustainability-related issues from various company stakeholders. At the same time, major reporting frameworks dealing with sustainability issues increasingly publish guides to support corporations Sustainability Development Goals (SDGs) commitments. For corporations, it is challenging to use these guides to obtain the highest benefits for fulfilling corporate sustainability targets and provide valuable reporting on these for its stakeholders. This paper aims to provide a structural alignment approach and a robust alignment methodology that corporations can repeatedly use to harmonize the SDGs guides of the Global Reporting Initiative (GRI) and the International Integrated Reporting Council (IIRC) to satisfy their evolving SDGs reporting challenges. The corporate sustainability assessment and reporting performance of four telecommunication companies on their SDGs commitment and their SDGs reporting challenges are examined to support them with tailor-made guidance established by aligning various SDGs guides of GRI and IIRC. The alignment process is carried out by utilizing the alignment framework and the alignment approach established by the paper. By applying the proposed alignment approach and alignment framework, the paper concludes that corporations can increase their SDGs reporting performance by satisfying their SDGs challenges with the deliverables of the SDGs guides. It adds value to strategic corporate sustainability literature by analyzing the relationship among companies SDGs approaches in the same sector and supporting the various SDGs guides of GRI and IIRC by applying the alignment approach established in this paper.

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]

Building a more sustainable society is an urgent requirement for todays service-oriented manufacturing enterprises, such as automobile maintenance firms. In automobile maintenance service, traditional painting process scheduling scheme usually only considers the profits of enterprises, ignoring the requirements of customers or other stakeholders. To address this gap and achieve sustainable operations of enterprises in the long term, this paper concentrated on the vehicle scheduling of painting process problem with the concern of the demands of managers, workers, customers, governments and non-government environmental protection organisations. This problem was formulated as a nonlinear 0-1 integer programming model to minimise makespan (MP), total pollutant emissions (TPE) and total customers perceived dissatisfaction (TCPDS). A genetic algorithm was designed to solve the model, and a practical case using data from both the information system and the survey was performed to test the performance of the proposed model and algorithm. Computational results revealed that the genetic algorithm performed well in terms of validity and stability. Pareto solutions demonstrated that optimising task sequences helped increase customers perceived satisfaction while improving the makespan of vehicle painting, decreased paint waste, and reduced worker health and safety risks. Some of the increases in the percentage of well-timed customer service reservations were catalysed by the method that combined tiered pricing, related to delivery times, the automobile painting efficiency, which improved customers perceived satisfaction. This paper also further guides managers to incorporate sustainable development into operations in service-oriented manufacturing enterprises., Funding Agencies|National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [71904021]; Humanities and Social Sciences Foundation of Ministry of Education of ChinaMinistry of Education, China [20YJA630079, 19YJAZH051]; Chunhui Plan of the Ministry of Education of the Peoples Republic of China [CQ2019001]; Planning Project of Philosophy and Social Science in Chengdu [2020ZXB10]; Project of Science and Technology (soft science research) in Chengdu [2020-RK00-00052-ZF]; Natural Science Foundation of Chongqing, ChinaNatural Science Foundation of Chongqing [cstc2020jcyj-msxmX0164]; Planning Project of Sichuan Normal University [SNU19J4Z2019-11]; Undergraduate Innovation and Entrepreneurship Training Program of Sichuan Normal University [202010636246, 202010636270]

Building a more sustainable society is an urgent requirement for todays service-oriented manufacturing enterprises, such as automobile maintenance firms. In automobile maintenance service, traditional painting process scheduling scheme usually only considers the profits of enterprises, ignoring the requirements of customers or other stakeholders. To address this gap and achieve sustainable operations of enterprises in the long term, this paper concentrated on the vehicle scheduling of painting process problem with the concern of the demands of managers, workers, customers, governments and non-government environmental protection organisations. This problem was formulated as a nonlinear 0-1 integer programming model to minimise makespan (MP), total pollutant emissions (TPE) and total customers perceived dissatisfaction (TCPDS). A genetic algorithm was designed to solve the model, and a practical case using data from both the information system and the survey was performed to test the performance of the proposed model and algorithm. Computational results revealed that the genetic algorithm performed well in terms of validity and stability. Pareto solutions demonstrated that optimising task sequences helped increase customers perceived satisfaction while improving the makespan of vehicle painting, decreased paint waste, and reduced worker health and safety risks. Some of the increases in the percentage of well-timed customer service reservations were catalysed by the method that combined tiered pricing, related to delivery times, the automobile painting efficiency, which improved customers perceived satisfaction. This paper also further guides managers to incorporate sustainable development into operations in service-oriented manufacturing enterprises., Funding Agencies|National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [71904021]; Humanities and Social Sciences Foundation of Ministry of Education of ChinaMinistry of Education, China [20YJA630079, 19YJAZH051]; Chunhui Plan of the Ministry of Education of the Peoples Republic of China [CQ2019001]; Planning Project of Philosophy and Social Science in Chengdu [2020ZXB10]; Project of Science and Technology (soft science research) in Chengdu [2020-RK00-00052-ZF]; Natural Science Foundation of Chongqing, ChinaNatural Science Foundation of Chongqing [cstc2020jcyj-msxmX0164]; Planning Project of Sichuan Normal University [SNU19J4Z2019-11]; Undergraduate Innovation and Entrepreneurship Training Program of Sichuan Normal University [202010636246, 202010636270]

Building a more sustainable society is an urgent requirement for todays service-oriented manufacturing enterprises, such as automobile maintenance firms. In automobile maintenance service, traditional painting process scheduling scheme usually only considers the profits of enterprises, ignoring the requirements of customers or other stakeholders. To address this gap and achieve sustainable operations of enterprises in the long term, this paper concentrated on the vehicle scheduling of painting process problem with the concern of the demands of managers, workers, customers, governments and non-government environmental protection organisations. This problem was formulated as a nonlinear 0-1 integer programming model to minimise makespan (MP), total pollutant emissions (TPE) and total customers perceived dissatisfaction (TCPDS). A genetic algorithm was designed to solve the model, and a practical case using data from both the information system and the survey was performed to test the performance of the proposed model and algorithm. Computational results revealed that the genetic algorithm performed well in terms of validity and stability. Pareto solutions demonstrated that optimising task sequences helped increase customers perceived satisfaction while improving the makespan of vehicle painting, decreased paint waste, and reduced worker health and safety risks. Some of the increases in the percentage of well-timed customer service reservations were catalysed by the method that combined tiered pricing, related to delivery times, the automobile painting efficiency, which improved customers perceived satisfaction. This paper also further guides managers to incorporate sustainable development into operations in service-oriented manufacturing enterprises., Funding Agencies|National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [71904021]; Humanities and Social Sciences Foundation of Ministry of Education of ChinaMinistry of Education, China [20YJA630079, 19YJAZH051]; Chunhui Plan of the Ministry of Education of the Peoples Republic of China [CQ2019001]; Planning Project of Philosophy and Social Science in Chengdu [2020ZXB10]; Project of Science and Technology (soft science research) in Chengdu [2020-RK00-00052-ZF]; Natural Science Foundation of Chongqing, ChinaNatural Science Foundation of Chongqing [cstc2020jcyj-msxmX0164]; Planning Project of Sichuan Normal University [SNU19J4Z2019-11]; Undergraduate Innovation and Entrepreneurship Training Program of Sichuan Normal University [202010636246, 202010636270]

Building a more sustainable society is an urgent requirement for todays service-oriented manufacturing enterprises, such as automobile maintenance firms. In automobile maintenance service, traditional painting process scheduling scheme usually only considers the profits of enterprises, ignoring the requirements of customers or other stakeholders. To address this gap and achieve sustainable operations of enterprises in the long term, this paper concentrated on the vehicle scheduling of painting process problem with the concern of the demands of managers, workers, customers, governments and non-government environmental protection organisations. This problem was formulated as a nonlinear 0-1 integer programming model to minimise makespan (MP), total pollutant emissions (TPE) and total customers perceived dissatisfaction (TCPDS). A genetic algorithm was designed to solve the model, and a practical case using data from both the information system and the survey was performed to test the performance of the proposed model and algorithm. Computational results revealed that the genetic algorithm performed well in terms of validity and stability. Pareto solutions demonstrated that optimising task sequences helped increase customers perceived satisfaction while improving the makespan of vehicle painting, decreased paint waste, and reduced worker health and safety risks. Some of the increases in the percentage of well-timed customer service reservations were catalysed by the method that combined tiered pricing, related to delivery times, the automobile painting efficiency, which improved customers perceived satisfaction. This paper also further guides managers to incorporate sustainable development into operations in service-oriented manufacturing enterprises., Funding Agencies|National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [71904021]; Humanities and Social Sciences Foundation of Ministry of Education of ChinaMinistry of Education, China [20YJA630079, 19YJAZH051]; Chunhui Plan of the Ministry of Education of the Peoples Republic of China [CQ2019001]; Planning Project of Philosophy and Social Science in Chengdu [2020ZXB10]; Project of Science and Technology (soft science research) in Chengdu [2020-RK00-00052-ZF]; Natural Science Foundation of Chongqing, ChinaNatural Science Foundation of Chongqing [cstc2020jcyj-msxmX0164]; Planning Project of Sichuan Normal University [SNU19J4Z2019-11]; Undergraduate Innovation and Entrepreneurship Training Program of Sichuan Normal University [202010636246, 202010636270]

Building a more sustainable society is an urgent requirement for todays service-oriented manufacturing enterprises, such as automobile maintenance firms. In automobile maintenance service, traditional painting process scheduling scheme usually only considers the profits of enterprises, ignoring the requirements of customers or other stakeholders. To address this gap and achieve sustainable operations of enterprises in the long term, this paper concentrated on the vehicle scheduling of painting process problem with the concern of the demands of managers, workers, customers, governments and non-government environmental protection organisations. This problem was formulated as a nonlinear 0-1 integer programming model to minimise makespan (MP), total pollutant emissions (TPE) and total customers perceived dissatisfaction (TCPDS). A genetic algorithm was designed to solve the model, and a practical case using data from both the information system and the survey was performed to test the performance of the proposed model and algorithm. Computational results revealed that the genetic algorithm performed well in terms of validity and stability. Pareto solutions demonstrated that optimising task sequences helped increase customers perceived satisfaction while improving the makespan of vehicle painting, decreased paint waste, and reduced worker health and safety risks. Some of the increases in the percentage of well-timed customer service reservations were catalysed by the method that combined tiered pricing, related to delivery times, the automobile painting efficiency, which improved customers perceived satisfaction. This paper also further guides managers to incorporate sustainable development into operations in service-oriented manufacturing enterprises., Funding Agencies|National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [71904021]; Humanities and Social Sciences Foundation of Ministry of Education of ChinaMinistry of Education, China [20YJA630079, 19YJAZH051]; Chunhui Plan of the Ministry of Education of the Peoples Republic of China [CQ2019001]; Planning Project of Philosophy and Social Science in Chengdu [2020ZXB10]; Project of Science and Technology (soft science research) in Chengdu [2020-RK00-00052-ZF]; Natural Science Foundation of Chongqing, ChinaNatural Science Foundation of Chongqing [cstc2020jcyj-msxmX0164]; Planning Project of Sichuan Normal University [SNU19J4Z2019-11]; Undergraduate Innovation and Entrepreneurship Training Program of Sichuan Normal University [202010636246, 202010636270]

Building a more sustainable society is an urgent requirement for todays service-oriented manufacturing enterprises, such as automobile maintenance firms. In automobile maintenance service, traditional painting process scheduling scheme usually only considers the profits of enterprises, ignoring the requirements of customers or other stakeholders. To address this gap and achieve sustainable operations of enterprises in the long term, this paper concentrated on the vehicle scheduling of painting process problem with the concern of the demands of managers, workers, customers, governments and non-government environmental protection organisations. This problem was formulated as a nonlinear 0-1 integer programming model to minimise makespan (MP), total pollutant emissions (TPE) and total customers perceived dissatisfaction (TCPDS). A genetic algorithm was designed to solve the model, and a practical case using data from both the information system and the survey was performed to test the performance of the proposed model and algorithm. Computational results revealed that the genetic algorithm performed well in terms of validity and stability. Pareto solutions demonstrated that optimising task sequences helped increase customers perceived satisfaction while improving the makespan of vehicle painting, decreased paint waste, and reduced worker health and safety risks. Some of the increases in the percentage of well-timed customer service reservations were catalysed by the method that combined tiered pricing, related to delivery times, the automobile painting efficiency, which improved customers perceived satisfaction. This paper also further guides managers to incorporate sustainable development into operations in service-oriented manufacturing enterprises., Funding Agencies|National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [71904021]; Humanities and Social Sciences Foundation of Ministry of Education of ChinaMinistry of Education, China [20YJA630079, 19YJAZH051]; Chunhui Plan of the Ministry of Education of the Peoples Republic of China [CQ2019001]; Planning Project of Philosophy and Social Science in Chengdu [2020ZXB10]; Project of Science and Technology (soft science research) in Chengdu [2020-RK00-00052-ZF]; Natural Science Foundation of Chongqing, ChinaNatural Science Foundation of Chongqing [cstc2020jcyj-msxmX0164]; Planning Project of Sichuan Normal University [SNU19J4Z2019-11]; Undergraduate Innovation and Entrepreneurship Training Program of Sichuan Normal University [202010636246, 202010636270]

Serial entrepreneurial performance is affected by serial entrepreneurs risk preference, but the way prior experience affects the relationship between the risk preference and performance remains unclear. Through regression analysis of 588 listed serial entrepreneurial companies in China, this paper shows that serial entrepreneurs who make more use of risk have higher serial entrepreneurial performance. For serial entrepreneurs with relevant industry experience, the degree of influence of their risk preference on serial entrepreneurial performance will be strengthened. For serial entrepreneurs with rich entrepreneurial experience, the degree of influence of their risk preference on serial entrepreneurial performance will be weakened. The results are conducive to the effective use of prior experience and reasonable adjustment of risk preference for serial entrepreneurial enterprises, thereby improving the performance of serial entrepreneurship., Funding Agencies|Later Funded Projects of the National Social Science Foundation of China [20FGLB007]

Serial entrepreneurial performance is affected by serial entrepreneurs risk preference, but the way prior experience affects the relationship between the risk preference and performance remains unclear. Through regression analysis of 588 listed serial entrepreneurial companies in China, this paper shows that serial entrepreneurs who make more use of risk have higher serial entrepreneurial performance. For serial entrepreneurs with relevant industry experience, the degree of influence of their risk preference on serial entrepreneurial performance will be strengthened. For serial entrepreneurs with rich entrepreneurial experience, the degree of influence of their risk preference on serial entrepreneurial performance will be weakened. The results are conducive to the effective use of prior experience and reasonable adjustment of risk preference for serial entrepreneurial enterprises, thereby improving the performance of serial entrepreneurship., Funding Agencies|Later Funded Projects of the National Social Science Foundation of China [20FGLB007]

Serial entrepreneurial performance is affected by serial entrepreneurs risk preference, but the way prior experience affects the relationship between the risk preference and performance remains unclear. Through regression analysis of 588 listed serial entrepreneurial companies in China, this paper shows that serial entrepreneurs who make more use of risk have higher serial entrepreneurial performance. For serial entrepreneurs with relevant industry experience, the degree of influence of their risk preference on serial entrepreneurial performance will be strengthened. For serial entrepreneurs with rich entrepreneurial experience, the degree of influence of their risk preference on serial entrepreneurial performance will be weakened. The results are conducive to the effective use of prior experience and reasonable adjustment of risk preference for serial entrepreneurial enterprises, thereby improving the performance of serial entrepreneurship., Funding Agencies|Later Funded Projects of the National Social Science Foundation of China [20FGLB007]

Serial entrepreneurial performance is affected by serial entrepreneurs risk preference, but the way prior experience affects the relationship between the risk preference and performance remains unclear. Through regression analysis of 588 listed serial entrepreneurial companies in China, this paper shows that serial entrepreneurs who make more use of risk have higher serial entrepreneurial performance. For serial entrepreneurs with relevant industry experience, the degree of influence of their risk preference on serial entrepreneurial performance will be strengthened. For serial entrepreneurs with rich entrepreneurial experience, the degree of influence of their risk preference on serial entrepreneurial performance will be weakened. The results are conducive to the effective use of prior experience and reasonable adjustment of risk preference for serial entrepreneurial enterprises, thereby improving the performance of serial entrepreneurship., Funding Agencies|Later Funded Projects of the National Social Science Foundation of China [20FGLB007]

Serial entrepreneurial performance is affected by serial entrepreneurs risk preference, but the way prior experience affects the relationship between the risk preference and performance remains unclear. Through regression analysis of 588 listed serial entrepreneurial companies in China, this paper shows that serial entrepreneurs who make more use of risk have higher serial entrepreneurial performance. For serial entrepreneurs with relevant industry experience, the degree of influence of their risk preference on serial entrepreneurial performance will be strengthened. For serial entrepreneurs with rich entrepreneurial experience, the degree of influence of their risk preference on serial entrepreneurial performance will be weakened. The results are conducive to the effective use of prior experience and reasonable adjustment of risk preference for serial entrepreneurial enterprises, thereby improving the performance of serial entrepreneurship., Funding Agencies|Later Funded Projects of the National Social Science Foundation of China [20FGLB007]

Crowd stampede has attracted significant attention of emergency management researchers in recent years. Early-warning of crowd stampede in metro station commercial area is discussed in this paper under the context of Internet of Things (IoT). Metro station commercial area is one of the entity carriers of E-commerce. IOT is a new concept of realizing intelligent sense, monitoring, tracking and management, which can be used in early-warning analysis of crowd stampede in metro station. Stampede risk early-warning in commercial area plays an important role in ensuring the operation of e-commerce online. Firstly, the laws and characteristics of the crowd movement in the commercial area of metro station are studied, which include the laeuna effect, block effect and aggravation effect. Secondly, the early-warning paradigm is constructed from four dimensions, ie. function, modules, principle and process. And then, under the IOT environment, the AHPsort II is applied to integrate the early-warning information and classify the stampede risk level. Finally, the paper takes the commercial area of Wuhan A metro station as an example to verify the practicability and effectiveness of the AHPsort II application to early-warning of crowd stampede in metro station commercial area., Funding Agencies|National Social Science Foundation of China [15AGL021]

Crowd stampede has attracted significant attention of emergency management researchers in recent years. Early-warning of crowd stampede in metro station commercial area is discussed in this paper under the context of Internet of Things (IoT). Metro station commercial area is one of the entity carriers of E-commerce. IOT is a new concept of realizing intelligent sense, monitoring, tracking and management, which can be used in early-warning analysis of crowd stampede in metro station. Stampede risk early-warning in commercial area plays an important role in ensuring the operation of e-commerce online. Firstly, the laws and characteristics of the crowd movement in the commercial area of metro station are studied, which include the laeuna effect, block effect and aggravation effect. Secondly, the early-warning paradigm is constructed from four dimensions, ie. function, modules, principle and process. And then, under the IOT environment, the AHPsort II is applied to integrate the early-warning information and classify the stampede risk level. Finally, the paper takes the commercial area of Wuhan A metro station as an example to verify the practicability and effectiveness of the AHPsort II application to early-warning of crowd stampede in metro station commercial area., Funding Agencies|National Social Science Foundation of China [15AGL021]

The objective of this paper is to provide a better understanding of the dynamics of engineering innovation by building a novel, extensive metaphor between engineering innovation and biological evolution. In a metaphor, a phrase literally denoting one kind of idea is used in place of another in order to suggest an analogy between them. In this metaphor, the insights gleaned from biological evolution were applied to engineering innovation. Biological evolution and engineering innovation are similar in that both exhibit creative destruction as evidenced by Darwin and Schumpeter. In the research analyses, special attention is given to the current multipolar world, with the poles consisting of societies based on different cultural traditions and to the emerging renaissance of decentralized production and its current iteration, Co-Engaging Production. The metaphor used in this paper challenges and even contradicts conventionally explicit and embedded beliefs in six ways. First, the dominance of Occidental science and engineering cannot be expected to last indefinitely. Second, the current dominance of Occidental science and engineering can have detrimental effects on engineering innovation. Third, free trade leads to inefficiency. Fourth, successful engineering innovation requires societal legitimacy and environmental conformity. Fifth, human cognition forms the basis of multipolar types of science and engineering. Sixth, past and current dominance reduces absorptive capacity in the Occidental. (C) 2019 Elsevier Ltd. All rights reserved., Postprint version uploaded on author’s personal web page: https://ylifin.wordpress.com/publications

Crowd stampede has attracted significant attention of emergency management researchers in recent years. Early-warning of crowd stampede in metro station commercial area is discussed in this paper under the context of Internet of Things (IoT). Metro station commercial area is one of the entity carriers of E-commerce. IOT is a new concept of realizing intelligent sense, monitoring, tracking and management, which can be used in early-warning analysis of crowd stampede in metro station. Stampede risk early-warning in commercial area plays an important role in ensuring the operation of e-commerce online. Firstly, the laws and characteristics of the crowd movement in the commercial area of metro station are studied, which include the laeuna effect, block effect and aggravation effect. Secondly, the early-warning paradigm is constructed from four dimensions, ie. function, modules, principle and process. And then, under the IOT environment, the AHPsort II is applied to integrate the early-warning information and classify the stampede risk level. Finally, the paper takes the commercial area of Wuhan A metro station as an example to verify the practicability and effectiveness of the AHPsort II application to early-warning of crowd stampede in metro station commercial area., Funding Agencies|National Social Science Foundation of China [15AGL021]

The objective of this paper is to provide a better understanding of the dynamics of engineering innovation by building a novel, extensive metaphor between engineering innovation and biological evolution. In a metaphor, a phrase literally denoting one kind of idea is used in place of another in order to suggest an analogy between them. In this metaphor, the insights gleaned from biological evolution were applied to engineering innovation. Biological evolution and engineering innovation are similar in that both exhibit creative destruction as evidenced by Darwin and Schumpeter. In the research analyses, special attention is given to the current multipolar world, with the poles consisting of societies based on different cultural traditions and to the emerging renaissance of decentralized production and its current iteration, Co-Engaging Production. The metaphor used in this paper challenges and even contradicts conventionally explicit and embedded beliefs in six ways. First, the dominance of Occidental science and engineering cannot be expected to last indefinitely. Second, the current dominance of Occidental science and engineering can have detrimental effects on engineering innovation. Third, free trade leads to inefficiency. Fourth, successful engineering innovation requires societal legitimacy and environmental conformity. Fifth, human cognition forms the basis of multipolar types of science and engineering. Sixth, past and current dominance reduces absorptive capacity in the Occidental. (C) 2019 Elsevier Ltd. All rights reserved., Postprint version uploaded on author’s personal web page: https://ylifin.wordpress.com/publications

Crowd stampede has attracted significant attention of emergency management researchers in recent years. Early-warning of crowd stampede in metro station commercial area is discussed in this paper under the context of Internet of Things (IoT). Metro station commercial area is one of the entity carriers of E-commerce. IOT is a new concept of realizing intelligent sense, monitoring, tracking and management, which can be used in early-warning analysis of crowd stampede in metro station. Stampede risk early-warning in commercial area plays an important role in ensuring the operation of e-commerce online. Firstly, the laws and characteristics of the crowd movement in the commercial area of metro station are studied, which include the laeuna effect, block effect and aggravation effect. Secondly, the early-warning paradigm is constructed from four dimensions, ie. function, modules, principle and process. And then, under the IOT environment, the AHPsort II is applied to integrate the early-warning information and classify the stampede risk level. Finally, the paper takes the commercial area of Wuhan A metro station as an example to verify the practicability and effectiveness of the AHPsort II application to early-warning of crowd stampede in metro station commercial area., Funding Agencies|National Social Science Foundation of China [15AGL021]

The objective of this paper is to provide a better understanding of the dynamics of engineering innovation by building a novel, extensive metaphor between engineering innovation and biological evolution. In a metaphor, a phrase literally denoting one kind of idea is used in place of another in order to suggest an analogy between them. In this metaphor, the insights gleaned from biological evolution were applied to engineering innovation. Biological evolution and engineering innovation are similar in that both exhibit creative destruction as evidenced by Darwin and Schumpeter. In the research analyses, special attention is given to the current multipolar world, with the poles consisting of societies based on different cultural traditions and to the emerging renaissance of decentralized production and its current iteration, Co-Engaging Production. The metaphor used in this paper challenges and even contradicts conventionally explicit and embedded beliefs in six ways. First, the dominance of Occidental science and engineering cannot be expected to last indefinitely. Second, the current dominance of Occidental science and engineering can have detrimental effects on engineering innovation. Third, free trade leads to inefficiency. Fourth, successful engineering innovation requires societal legitimacy and environmental conformity. Fifth, human cognition forms the basis of multipolar types of science and engineering. Sixth, past and current dominance reduces absorptive capacity in the Occidental. (C) 2019 Elsevier Ltd. All rights reserved., Postprint version uploaded on author’s personal web page: https://ylifin.wordpress.com/publications

The objective of this paper is to provide a better understanding of the dynamics of engineering innovation by building a novel, extensive metaphor between engineering innovation and biological evolution. In a metaphor, a phrase literally denoting one kind of idea is used in place of another in order to suggest an analogy between them. In this metaphor, the insights gleaned from biological evolution were applied to engineering innovation. Biological evolution and engineering innovation are similar in that both exhibit creative destruction as evidenced by Darwin and Schumpeter. In the research analyses, special attention is given to the current multipolar world, with the poles consisting of societies based on different cultural traditions and to the emerging renaissance of decentralized production and its current iteration, Co-Engaging Production. The metaphor used in this paper challenges and even contradicts conventionally explicit and embedded beliefs in six ways. First, the dominance of Occidental science and engineering cannot be expected to last indefinitely. Second, the current dominance of Occidental science and engineering can have detrimental effects on engineering innovation. Third, free trade leads to inefficiency. Fourth, successful engineering innovation requires societal legitimacy and environmental conformity. Fifth, human cognition forms the basis of multipolar types of science and engineering. Sixth, past and current dominance reduces absorptive capacity in the Occidental. (C) 2019 Elsevier Ltd. All rights reserved., Postprint version uploaded on author’s personal web page: https://ylifin.wordpress.com/publications

Crowd stampede has attracted significant attention of emergency management researchers in recent years. Early-warning of crowd stampede in metro station commercial area is discussed in this paper under the context of Internet of Things (IoT). Metro station commercial area is one of the entity carriers of E-commerce. IOT is a new concept of realizing intelligent sense, monitoring, tracking and management, which can be used in early-warning analysis of crowd stampede in metro station. Stampede risk early-warning in commercial area plays an important role in ensuring the operation of e-commerce online. Firstly, the laws and characteristics of the crowd movement in the commercial area of metro station are studied, which include the laeuna effect, block effect and aggravation effect. Secondly, the early-warning paradigm is constructed from four dimensions, ie. function, modules, principle and process. And then, under the IOT environment, the AHPsort II is applied to integrate the early-warning information and classify the stampede risk level. Finally, the paper takes the commercial area of Wuhan A metro station as an example to verify the practicability and effectiveness of the AHPsort II application to early-warning of crowd stampede in metro station commercial area., Funding Agencies|National Social Science Foundation of China [15AGL021]

The objective of this paper is to provide a better understanding of the dynamics of engineering innovation by building a novel, extensive metaphor between engineering innovation and biological evolution. In a metaphor, a phrase literally denoting one kind of idea is used in place of another in order to suggest an analogy between them. In this metaphor, the insights gleaned from biological evolution were applied to engineering innovation. Biological evolution and engineering innovation are similar in that both exhibit creative destruction as evidenced by Darwin and Schumpeter. In the research analyses, special attention is given to the current multipolar world, with the poles consisting of societies based on different cultural traditions and to the emerging renaissance of decentralized production and its current iteration, Co-Engaging Production. The metaphor used in this paper challenges and even contradicts conventionally explicit and embedded beliefs in six ways. First, the dominance of Occidental science and engineering cannot be expected to last indefinitely. Second, the current dominance of Occidental science and engineering can have detrimental effects on engineering innovation. Third, free trade leads to inefficiency. Fourth, successful engineering innovation requires societal legitimacy and environmental conformity. Fifth, human cognition forms the basis of multipolar types of science and engineering. Sixth, past and current dominance reduces absorptive capacity in the Occidental. (C) 2019 Elsevier Ltd. All rights reserved., Postprint version uploaded on author’s personal web page: https://ylifin.wordpress.com/publications

Under the context that entrepreneurial risk in clean energy industry has become increasingly complex, government subsidies are helpful to guide and promote the entrepreneurial companies survival and development. This paper explored the roles of government subsidies, more specifically, in forms of government subsidies beforehand and government subsidies afterwards on the relationship between Ramp;D and entrepreneurial companies growth, particularly in the clean energy industry. To delve deeper into the roles, this paper analysed data from the 2013 and 2014 annual reports of 58 listed companies in clean energy industries in China. By using a moderating effect model, this paper derived following conclusions: (1) Ramp;D investment does not have a lag effect on entrepreneurial companies growth; (2) Government subsidies beforehand have negative moderating effects on the relationship between Ramp;D investment and entrepreneurial companies growth; (3) Government subsidies afterwards have positive moderating effects on the relationship between Ramp;D investment and entrepreneurial companies growth. This paper helped to understand the difference between the functions of government subsidies beforehand and government subsidies afterwards. To some extent, the results can revise prior studies about the function of government subsidies to influence the relationship between Ramp;D and entrepreneurial companies growth. The results will also be helpful for the government to evaluate the performance of government subsidies and design appropriate subsidies strategies. (C) 2018 Elsevier Ltd. All rights reserved., Funding Agencies|Fundamental Research Funds for the Central Universities [2016VI018]

Under the context that entrepreneurial risk in clean energy industry has become increasingly complex, government subsidies are helpful to guide and promote the entrepreneurial companies survival and development. This paper explored the roles of government subsidies, more specifically, in forms of government subsidies beforehand and government subsidies afterwards on the relationship between Ramp;D and entrepreneurial companies growth, particularly in the clean energy industry. To delve deeper into the roles, this paper analysed data from the 2013 and 2014 annual reports of 58 listed companies in clean energy industries in China. By using a moderating effect model, this paper derived following conclusions: (1) Ramp;D investment does not have a lag effect on entrepreneurial companies growth; (2) Government subsidies beforehand have negative moderating effects on the relationship between Ramp;D investment and entrepreneurial companies growth; (3) Government subsidies afterwards have positive moderating effects on the relationship between Ramp;D investment and entrepreneurial companies growth. This paper helped to understand the difference between the functions of government subsidies beforehand and government subsidies afterwards. To some extent, the results can revise prior studies about the function of government subsidies to influence the relationship between Ramp;D and entrepreneurial companies growth. The results will also be helpful for the government to evaluate the performance of government subsidies and design appropriate subsidies strategies. (C) 2018 Elsevier Ltd. All rights reserved., Funding Agencies|Fundamental Research Funds for the Central Universities [2016VI018]

Under the context that entrepreneurial risk in clean energy industry has become increasingly complex, government subsidies are helpful to guide and promote the entrepreneurial companies survival and development. This paper explored the roles of government subsidies, more specifically, in forms of government subsidies beforehand and government subsidies afterwards on the relationship between Ramp;D and entrepreneurial companies growth, particularly in the clean energy industry. To delve deeper into the roles, this paper analysed data from the 2013 and 2014 annual reports of 58 listed companies in clean energy industries in China. By using a moderating effect model, this paper derived following conclusions: (1) Ramp;D investment does not have a lag effect on entrepreneurial companies growth; (2) Government subsidies beforehand have negative moderating effects on the relationship between Ramp;D investment and entrepreneurial companies growth; (3) Government subsidies afterwards have positive moderating effects on the relationship between Ramp;D investment and entrepreneurial companies growth. This paper helped to understand the difference between the functions of government subsidies beforehand and government subsidies afterwards. To some extent, the results can revise prior studies about the function of government subsidies to influence the relationship between Ramp;D and entrepreneurial companies growth. The results will also be helpful for the government to evaluate the performance of government subsidies and design appropriate subsidies strategies. (C) 2018 Elsevier Ltd. All rights reserved., Funding Agencies|Fundamental Research Funds for the Central Universities [2016VI018]

Under the context that entrepreneurial risk in clean energy industry has become increasingly complex, government subsidies are helpful to guide and promote the entrepreneurial companies survival and development. This paper explored the roles of government subsidies, more specifically, in forms of government subsidies beforehand and government subsidies afterwards on the relationship between Ramp;D and entrepreneurial companies growth, particularly in the clean energy industry. To delve deeper into the roles, this paper analysed data from the 2013 and 2014 annual reports of 58 listed companies in clean energy industries in China. By using a moderating effect model, this paper derived following conclusions: (1) Ramp;D investment does not have a lag effect on entrepreneurial companies growth; (2) Government subsidies beforehand have negative moderating effects on the relationship between Ramp;D investment and entrepreneurial companies growth; (3) Government subsidies afterwards have positive moderating effects on the relationship between Ramp;D investment and entrepreneurial companies growth. This paper helped to understand the difference between the functions of government subsidies beforehand and government subsidies afterwards. To some extent, the results can revise prior studies about the function of government subsidies to influence the relationship between Ramp;D and entrepreneurial companies growth. The results will also be helpful for the government to evaluate the performance of government subsidies and design appropriate subsidies strategies. (C) 2018 Elsevier Ltd. All rights reserved., Funding Agencies|Fundamental Research Funds for the Central Universities [2016VI018]

Under the context that entrepreneurial risk in clean energy industry has become increasingly complex, government subsidies are helpful to guide and promote the entrepreneurial companies survival and development. This paper explored the roles of government subsidies, more specifically, in forms of government subsidies beforehand and government subsidies afterwards on the relationship between Ramp;D and entrepreneurial companies growth, particularly in the clean energy industry. To delve deeper into the roles, this paper analysed data from the 2013 and 2014 annual reports of 58 listed companies in clean energy industries in China. By using a moderating effect model, this paper derived following conclusions: (1) Ramp;D investment does not have a lag effect on entrepreneurial companies growth; (2) Government subsidies beforehand have negative moderating effects on the relationship between Ramp;D investment and entrepreneurial companies growth; (3) Government subsidies afterwards have positive moderating effects on the relationship between Ramp;D investment and entrepreneurial companies growth. This paper helped to understand the difference between the functions of government subsidies beforehand and government subsidies afterwards. To some extent, the results can revise prior studies about the function of government subsidies to influence the relationship between Ramp;D and entrepreneurial companies growth. The results will also be helpful for the government to evaluate the performance of government subsidies and design appropriate subsidies strategies. (C) 2018 Elsevier Ltd. All rights reserved., Funding Agencies|Fundamental Research Funds for the Central Universities [2016VI018]

Under the context that entrepreneurial risk in clean energy industry has become increasingly complex, government subsidies are helpful to guide and promote the entrepreneurial companies survival and development. This paper explored the roles of government subsidies, more specifically, in forms of government subsidies beforehand and government subsidies afterwards on the relationship between Ramp;D and entrepreneurial companies growth, particularly in the clean energy industry. To delve deeper into the roles, this paper analysed data from the 2013 and 2014 annual reports of 58 listed companies in clean energy industries in China. By using a moderating effect model, this paper derived following conclusions: (1) Ramp;D investment does not have a lag effect on entrepreneurial companies growth; (2) Government subsidies beforehand have negative moderating effects on the relationship between Ramp;D investment and entrepreneurial companies growth; (3) Government subsidies afterwards have positive moderating effects on the relationship between Ramp;D investment and entrepreneurial companies growth. This paper helped to understand the difference between the functions of government subsidies beforehand and government subsidies afterwards. To some extent, the results can revise prior studies about the function of government subsidies to influence the relationship between Ramp;D and entrepreneurial companies growth. The results will also be helpful for the government to evaluate the performance of government subsidies and design appropriate subsidies strategies. (C) 2018 Elsevier Ltd. All rights reserved., Funding Agencies|Fundamental Research Funds for the Central Universities [2016VI018]

This special volume originates from the International Conference on Innovation and Management held at University of Vaasa in Finland in 2014. Talks with the key note speaker and Editor-in-Chief of the Journal of Cleaner Production led to an idea to develop a special volume about innovative products and services according to the themes of the conference. Thus, the purpose of this special volume is to explore different viewpoints of how innovative products and services may support sustainable societal development. There are five thematic areas with papers that describe new advancements in different industries and organizations. The included papers cover relevant theoretical background and present case studies and practical results. This special volume shows that great progresses are being made in different thematic areas but also that there are so much more waiting to be done for sustainable societal development. This volume indicates that cross-disciplinary approach is truly needed to achieve societal sustainable development. This requires people to change their mindsets and genuinely co-operate towards better future. (C) 2017 Published by Elsevier Ltd.

This special volume originates from the International Conference on Innovation and Management held at University of Vaasa in Finland in 2014. Talks with the key note speaker and Editor-in-Chief of the Journal of Cleaner Production led to an idea to develop a special volume about innovative products and services according to the themes of the conference. Thus, the purpose of this special volume is to explore different viewpoints of how innovative products and services may support sustainable societal development. There are five thematic areas with papers that describe new advancements in different industries and organizations. The included papers cover relevant theoretical background and present case studies and practical results. This special volume shows that great progresses are being made in different thematic areas but also that there are so much more waiting to be done for sustainable societal development. This volume indicates that cross-disciplinary approach is truly needed to achieve societal sustainable development. This requires people to change their mindsets and genuinely co-operate towards better future. (C) 2017 Published by Elsevier Ltd.

This special volume originates from the International Conference on Innovation and Management held at University of Vaasa in Finland in 2014. Talks with the key note speaker and Editor-in-Chief of the Journal of Cleaner Production led to an idea to develop a special volume about innovative products and services according to the themes of the conference. Thus, the purpose of this special volume is to explore different viewpoints of how innovative products and services may support sustainable societal development. There are five thematic areas with papers that describe new advancements in different industries and organizations. The included papers cover relevant theoretical background and present case studies and practical results. This special volume shows that great progresses are being made in different thematic areas but also that there are so much more waiting to be done for sustainable societal development. This volume indicates that cross-disciplinary approach is truly needed to achieve societal sustainable development. This requires people to change their mindsets and genuinely co-operate towards better future. (C) 2017 Published by Elsevier Ltd.

This special volume originates from the International Conference on Innovation and Management held at University of Vaasa in Finland in 2014. Talks with the key note speaker and Editor-in-Chief of the Journal of Cleaner Production led to an idea to develop a special volume about innovative products and services according to the themes of the conference. Thus, the purpose of this special volume is to explore different viewpoints of how innovative products and services may support sustainable societal development. There are five thematic areas with papers that describe new advancements in different industries and organizations. The included papers cover relevant theoretical background and present case studies and practical results. This special volume shows that great progresses are being made in different thematic areas but also that there are so much more waiting to be done for sustainable societal development. This volume indicates that cross-disciplinary approach is truly needed to achieve societal sustainable development. This requires people to change their mindsets and genuinely co-operate towards better future. (C) 2017 Published by Elsevier Ltd.

This special volume originates from the International Conference on Innovation and Management held at University of Vaasa in Finland in 2014. Talks with the key note speaker and Editor-in-Chief of the Journal of Cleaner Production led to an idea to develop a special volume about innovative products and services according to the themes of the conference. Thus, the purpose of this special volume is to explore different viewpoints of how innovative products and services may support sustainable societal development. There are five thematic areas with papers that describe new advancements in different industries and organizations. The included papers cover relevant theoretical background and present case studies and practical results. This special volume shows that great progresses are being made in different thematic areas but also that there are so much more waiting to be done for sustainable societal development. This volume indicates that cross-disciplinary approach is truly needed to achieve societal sustainable development. This requires people to change their mindsets and genuinely co-operate towards better future. (C) 2017 Published by Elsevier Ltd.

This special volume originates from the International Conference on Innovation and Management held at University of Vaasa in Finland in 2014. Talks with the key note speaker and Editor-in-Chief of the Journal of Cleaner Production led to an idea to develop a special volume about innovative products and services according to the themes of the conference. Thus, the purpose of this special volume is to explore different viewpoints of how innovative products and services may support sustainable societal development. There are five thematic areas with papers that describe new advancements in different industries and organizations. The included papers cover relevant theoretical background and present case studies and practical results. This special volume shows that great progresses are being made in different thematic areas but also that there are so much more waiting to be done for sustainable societal development. This volume indicates that cross-disciplinary approach is truly needed to achieve societal sustainable development. This requires people to change their mindsets and genuinely co-operate towards better future. (C) 2017 Published by Elsevier Ltd.

Cutting energy consumption prediction gives decision supports for the energy-saving operation to realise green manufacturing. However, there are challenges when predicting aviation parts due to the machining features causing tool wear and expensive data labelling. Consequently, this paper builds a prediction model and emphasises training with limited experimental data by proposing an ensemble transfer learning approach. The approach incorporates transfer learning, i.e., TrAdaBoost-R2 (TR) algorithm, and calibration, i.e., Bayesian and Markov chain Monte Carlo calibration (MCMC). Firstly, a cutting energy consumption prediction model considering tool wear is formulated with cutting and tool parameters as the inputs. Secondly, a dataset including experiment and simulation data for training is constructed, where TR is used to identify the valuable data from the simulation model calibrated by MCMC. Then random forest regression (RFR) is introduced as a base learner to train the prediction model on the hybrid dataset. Finally, a case study of the aluminium alloy 7075 parts milling process shows that the proposed method is accurate in cutting energy consumption prediction. Compared with RFR and TR-RFR, the proposed methods coefficient of determination (R2) increases by 11.60% and 3.55%, indicating high goodness of fit under the same small samples of the experiment. Therefore, the proposed method could help determine the most efficient process plan without excessive time, materials and energy, significantly contributing to green manufacturing., Funding Agencies|National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [51975463]; FlexSUS: Flexibility for Smart Urban Energy Systems [91352]; European UnionEuropean Commission [775970]

Cutting energy consumption prediction gives decision supports for the energy-saving operation to realise green manufacturing. However, there are challenges when predicting aviation parts due to the machining features causing tool wear and expensive data labelling. Consequently, this paper builds a prediction model and emphasises training with limited experimental data by proposing an ensemble transfer learning approach. The approach incorporates transfer learning, i.e., TrAdaBoost-R2 (TR) algorithm, and calibration, i.e., Bayesian and Markov chain Monte Carlo calibration (MCMC). Firstly, a cutting energy consumption prediction model considering tool wear is formulated with cutting and tool parameters as the inputs. Secondly, a dataset including experiment and simulation data for training is constructed, where TR is used to identify the valuable data from the simulation model calibrated by MCMC. Then random forest regression (RFR) is introduced as a base learner to train the prediction model on the hybrid dataset. Finally, a case study of the aluminium alloy 7075 parts milling process shows that the proposed method is accurate in cutting energy consumption prediction. Compared with RFR and TR-RFR, the proposed methods coefficient of determination (R2) increases by 11.60% and 3.55%, indicating high goodness of fit under the same small samples of the experiment. Therefore, the proposed method could help determine the most efficient process plan without excessive time, materials and energy, significantly contributing to green manufacturing., Funding Agencies|National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [51975463]; FlexSUS: Flexibility for Smart Urban Energy Systems [91352]; European UnionEuropean Commission [775970]

Being an environmentally friendly fuel obtained from rapeseed oil, biodiesel is used extensively in Europe. However, the dependence structure between global crude oil prices and the European prices of biodiesel and rapeseed oil is understudied and unclear. In this paper, we address this gap by utilizing asymmetric copulas and cross-quantilogram approaches on daily data. The results of the DCC-Student-t copula indicate that during bearish periods the conditional connectedness between crude oil prices and biodiesel (rapeseed oil) prices are stronger than during bullish periods, indicating increased co-movement with a decline in crude oil prices. The application of cross-quantilogram indicates that an increase in crude oil price positively influences biodiesel prices reflecting an asymmetric dependence structure among the assets. There is evidence of shifts in the dynamics of quantile dependency during periods of financial and economic turmoil. Overall, the results show a significant dependence between the global crude oil market and the European markets of biodiesel and rapeseed oil in specific periods and under specific market conditions, which have important implications for policymakers and investors.

Being an environmentally friendly fuel obtained from rapeseed oil, biodiesel is used extensively in Europe. However, the dependence structure between global crude oil prices and the European prices of biodiesel and rapeseed oil is understudied and unclear. In this paper, we address this gap by utilizing asymmetric copulas and cross-quantilogram approaches on daily data. The results of the DCC-Student-t copula indicate that during bearish periods the conditional connectedness between crude oil prices and biodiesel (rapeseed oil) prices are stronger than during bullish periods, indicating increased co-movement with a decline in crude oil prices. The application of cross-quantilogram indicates that an increase in crude oil price positively influences biodiesel prices reflecting an asymmetric dependence structure among the assets. There is evidence of shifts in the dynamics of quantile dependency during periods of financial and economic turmoil. Overall, the results show a significant dependence between the global crude oil market and the European markets of biodiesel and rapeseed oil in specific periods and under specific market conditions, which have important implications for policymakers and investors.

Being an environmentally friendly fuel obtained from rapeseed oil, biodiesel is used extensively in Europe. However, the dependence structure between global crude oil prices and the European prices of biodiesel and rapeseed oil is understudied and unclear. In this paper, we address this gap by utilizing asymmetric copulas and cross-quantilogram approaches on daily data. The results of the DCC-Student-t copula indicate that during bearish periods the conditional connectedness between crude oil prices and biodiesel (rapeseed oil) prices are stronger than during bullish periods, indicating increased co-movement with a decline in crude oil prices. The application of cross-quantilogram indicates that an increase in crude oil price positively influences biodiesel prices reflecting an asymmetric dependence structure among the assets. There is evidence of shifts in the dynamics of quantile dependency during periods of financial and economic turmoil. Overall, the results show a significant dependence between the global crude oil market and the European markets of biodiesel and rapeseed oil in specific periods and under specific market conditions, which have important implications for policymakers and investors.