To satisfy the requirements of high energy density,high power density,quick response and long lifespan for energy storage systems(ESSs),hybrid energy storage systems(HESSs)have been investigated for their complementar...To satisfy the requirements of high energy density,high power density,quick response and long lifespan for energy storage systems(ESSs),hybrid energy storage systems(HESSs)have been investigated for their complementary characteristics of‘high energy density components’and‘high power density components’.To optimize HESS combinations,related indices such as annual cost,fluctuation smoothing ability as well as safety and environmental impact have to be evaluated.The multiattribute utility method investigated in this paper is aimed to draw an overall conclusion for HESS allocation optimization in microgrid.Building on multi-attribute utility theory,this method has significant advantages in solving the incommensurability and contradiction among multiple attributes.Instead of determining the weights of various attributes subjectively,when adopting the multi-attribute utility method,the characteristics of attributes and the relation among them can be investigated objectively.Also,the proper utility function and merging rules are identified to achieve the aggregate utility which can reflect comprehensive qualities of HESSs.展开更多
Water resource allocation was defined as an input-output question in this paper, and a preliminary input-output index system was set up. Then GEM （group eigenvalue method）-MAUE （multi-attribute utility theory） mod...Water resource allocation was defined as an input-output question in this paper, and a preliminary input-output index system was set up. Then GEM （group eigenvalue method）-MAUE （multi-attribute utility theory） model was applied to evaluate relative efficiency of water resource allocation plans. This model determined weights of indicators by GEM, and assessed the allocation schemes by MAUE. Compared with DEA （Data Envelopment Analysis） or ANN （Artificial Neural Networks）, the mode was more applicable in some cases where decision-makers had preference for certain indicators展开更多
A carefully planned software development process helps in maintaining the quality of the software.In today’s scenario the primitive software development models have been replaced by the Agile based models like SCRUM,...A carefully planned software development process helps in maintaining the quality of the software.In today’s scenario the primitive software development models have been replaced by the Agile based models like SCRUM,KANBAN,LEAN,etc.Although,every framework has its own boon,the reason for widespread acceptance of the agile-based approach is its evolutionary nature that permits change in the path of software development.The development process occurs in iterative and incremental cycles called sprints.In SCRUM,which is one of the most widely used agile-based software development modeling framework;the sprint length is fixed throughout the process wherein;it is usually taken to be 1–4 weeks.But in practical application,the sprint length should be altered intuitively as per the requirement.To overcome this limitation,in this paper,a methodical work has been presented that determines the optimal sprint length based on two varied and yet connected attributes;the cost incurred and the work intensity required.The approach defines the number of tasks performed in each sprint along with the corresponding cost incurred in performing those tasks.Multi-attribute utility theory(MAUT),a multi-criterion decision making approach,has been utilized to find the required trade-off between two attributes under consideration.The proposed modeling framework has been validated using real life data set.With the use of the model,the optimal sprint for each sprint could be evaluated which was much shorter than the original length.Thus,the results obtained validate the proposal of a dynamic sprint length that can be determined before the start of each sprint.The structure would help in cost as well as time savings for a firm.展开更多
基金supported by Science and Technology Foundation of State Grid Corporation of China (No.520940120036)the Key Project of the National Twelfth-Five Year Research Programme of China (No.2013BAA01B04)
文摘To satisfy the requirements of high energy density,high power density,quick response and long lifespan for energy storage systems(ESSs),hybrid energy storage systems(HESSs)have been investigated for their complementary characteristics of‘high energy density components’and‘high power density components’.To optimize HESS combinations,related indices such as annual cost,fluctuation smoothing ability as well as safety and environmental impact have to be evaluated.The multiattribute utility method investigated in this paper is aimed to draw an overall conclusion for HESS allocation optimization in microgrid.Building on multi-attribute utility theory,this method has significant advantages in solving the incommensurability and contradiction among multiple attributes.Instead of determining the weights of various attributes subjectively,when adopting the multi-attribute utility method,the characteristics of attributes and the relation among them can be investigated objectively.Also,the proper utility function and merging rules are identified to achieve the aggregate utility which can reflect comprehensive qualities of HESSs.
文摘Water resource allocation was defined as an input-output question in this paper, and a preliminary input-output index system was set up. Then GEM （group eigenvalue method）-MAUE （multi-attribute utility theory） model was applied to evaluate relative efficiency of water resource allocation plans. This model determined weights of indicators by GEM, and assessed the allocation schemes by MAUE. Compared with DEA （Data Envelopment Analysis） or ANN （Artificial Neural Networks）, the mode was more applicable in some cases where decision-makers had preference for certain indicators
文摘A carefully planned software development process helps in maintaining the quality of the software.In today’s scenario the primitive software development models have been replaced by the Agile based models like SCRUM,KANBAN,LEAN,etc.Although,every framework has its own boon,the reason for widespread acceptance of the agile-based approach is its evolutionary nature that permits change in the path of software development.The development process occurs in iterative and incremental cycles called sprints.In SCRUM,which is one of the most widely used agile-based software development modeling framework;the sprint length is fixed throughout the process wherein;it is usually taken to be 1–4 weeks.But in practical application,the sprint length should be altered intuitively as per the requirement.To overcome this limitation,in this paper,a methodical work has been presented that determines the optimal sprint length based on two varied and yet connected attributes;the cost incurred and the work intensity required.The approach defines the number of tasks performed in each sprint along with the corresponding cost incurred in performing those tasks.Multi-attribute utility theory(MAUT),a multi-criterion decision making approach,has been utilized to find the required trade-off between two attributes under consideration.The proposed modeling framework has been validated using real life data set.With the use of the model,the optimal sprint for each sprint could be evaluated which was much shorter than the original length.Thus,the results obtained validate the proposal of a dynamic sprint length that can be determined before the start of each sprint.The structure would help in cost as well as time savings for a firm.
