Process discovery, as one of the most challenging process analysis techniques, aims to uncover business process models from event logs. Many process discovery approaches were invented in the past twenty years;however,...Process discovery, as one of the most challenging process analysis techniques, aims to uncover business process models from event logs. Many process discovery approaches were invented in the past twenty years;however, most of them have difficulties in handling multi-instance sub-processes. To address this challenge, we first introduce a multi-instance business process model(MBPM) to support the modeling of processes with multiple sub-process instantiations. Formal semantics of MBPMs are precisely defined by using multi-instance Petri nets(MPNs)that are an extension of Petri nets with distinguishable tokens.Then, a novel process discovery technique is developed to support the discovery of MBPMs from event logs with sub-process multi-instantiation information. In addition, we propose to measure the quality of the discovered MBPMs against the input event logs by transforming an MBPM to a classical Petri net such that existing quality metrics, e.g., fitness and precision, can be used.The proposed discovery approach is properly implemented as plugins in the Pro M toolkit. Based on a cloud resource management case study, we compare our approach with the state-of-theart process discovery techniques. The results demonstrate that our approach outperforms existing approaches to discover process models with multi-instance sub-processes.展开更多
Data Envelopment Analysis （DEA） is a mathematical tech- nique to assess relative efficiencies of decision making units （DMUs）. The efficiency of 14 Iranian forest companies and forest management units was investig...Data Envelopment Analysis （DEA） is a mathematical tech- nique to assess relative efficiencies of decision making units （DMUs）. The efficiency of 14 Iranian forest companies and forest management units was investigated in 2010. Efficiency of the companies was esti- mated by using a traditional DEA model and a two-stage DEA model. Traditional DEA models consider all DMU activities as a black box and ignore the intermediate products, while two-stage models address inter- mediate processes. LINGO software was used for analysis. Overall pro- duction was divided into to processes for analyses by the two-stage model, timber harvest and marketing. Wilcoxon＇s signed-rank test was used to identify the differences of average efficiency in the harvesting and marketing sub-process. Weak performance in the harvesting sub-process was the cause of low efficiency in 2010. Companies such as Neka Chob and Kelardasht proved efficient at timber harvest, and Neka Chob forest company scored highest in overall efficiency. Finally, the reference units identified according to the results of two-stage DEA analysis.展开更多
An exergy analysis was performed considering the combustion of methane and agro-industrial residues produced in Portugal (forest residues and vines pruning). Regarding that the irreversibilities of a thermodynamic pro...An exergy analysis was performed considering the combustion of methane and agro-industrial residues produced in Portugal (forest residues and vines pruning). Regarding that the irreversibilities of a thermodynamic process are path dependent, the combustion process was considering as resulting from different hypothetical paths each one characterized by four main sub-processes: reactant mixing, fuel oxidation, internal thermal energy exchange (heat transfer), and product mixing. The exergetic efficiency was computed using a zero dimensional model developed by using a Visual Basic home code. It was concluded that the exergy losses were mainly due to the internal thermal energy exchange sub-process. The exergy losses from this sub-process are higher when the reactants are preheated up to the ignition temperature without previous fuel oxidation. On the other hand, the global exergy destruction can be minored increasing the pressure, the reactants temperature and the oxygen content on the oxidant stream. This methodology allows the identification of the phenomena and processes that have larger exergy losses, the understanding of why these losses occur and how the exergy changes with the parameters associated to each system which is crucial to implement the syngas combustion from biomass products as a competitive technology.展开更多
基金supported by the National Natural Science Foundation of China(61902222)the Taishan Scholars Program of Shandong Province(tsqn201909109)+1 种基金the Natural Science Excellent Youth Foundation of Shandong Province(ZR2021YQ45)the Youth Innovation Science and Technology Team Foundation of Shandong Higher School(2021KJ031)。
文摘Process discovery, as one of the most challenging process analysis techniques, aims to uncover business process models from event logs. Many process discovery approaches were invented in the past twenty years;however, most of them have difficulties in handling multi-instance sub-processes. To address this challenge, we first introduce a multi-instance business process model(MBPM) to support the modeling of processes with multiple sub-process instantiations. Formal semantics of MBPMs are precisely defined by using multi-instance Petri nets(MPNs)that are an extension of Petri nets with distinguishable tokens.Then, a novel process discovery technique is developed to support the discovery of MBPMs from event logs with sub-process multi-instantiation information. In addition, we propose to measure the quality of the discovered MBPMs against the input event logs by transforming an MBPM to a classical Petri net such that existing quality metrics, e.g., fitness and precision, can be used.The proposed discovery approach is properly implemented as plugins in the Pro M toolkit. Based on a cloud resource management case study, we compare our approach with the state-of-theart process discovery techniques. The results demonstrate that our approach outperforms existing approaches to discover process models with multi-instance sub-processes.
文摘Data Envelopment Analysis （DEA） is a mathematical tech- nique to assess relative efficiencies of decision making units （DMUs）. The efficiency of 14 Iranian forest companies and forest management units was investigated in 2010. Efficiency of the companies was esti- mated by using a traditional DEA model and a two-stage DEA model. Traditional DEA models consider all DMU activities as a black box and ignore the intermediate products, while two-stage models address inter- mediate processes. LINGO software was used for analysis. Overall pro- duction was divided into to processes for analyses by the two-stage model, timber harvest and marketing. Wilcoxon＇s signed-rank test was used to identify the differences of average efficiency in the harvesting and marketing sub-process. Weak performance in the harvesting sub-process was the cause of low efficiency in 2010. Companies such as Neka Chob and Kelardasht proved efficient at timber harvest, and Neka Chob forest company scored highest in overall efficiency. Finally, the reference units identified according to the results of two-stage DEA analysis.
基金the Portuguese Foundation for Science and Technology (FCT) for the given support to the grant SFRH/BPD/71686the project PTDC/AAC-AMB/103119/2008
文摘An exergy analysis was performed considering the combustion of methane and agro-industrial residues produced in Portugal (forest residues and vines pruning). Regarding that the irreversibilities of a thermodynamic process are path dependent, the combustion process was considering as resulting from different hypothetical paths each one characterized by four main sub-processes: reactant mixing, fuel oxidation, internal thermal energy exchange (heat transfer), and product mixing. The exergetic efficiency was computed using a zero dimensional model developed by using a Visual Basic home code. It was concluded that the exergy losses were mainly due to the internal thermal energy exchange sub-process. The exergy losses from this sub-process are higher when the reactants are preheated up to the ignition temperature without previous fuel oxidation. On the other hand, the global exergy destruction can be minored increasing the pressure, the reactants temperature and the oxygen content on the oxidant stream. This methodology allows the identification of the phenomena and processes that have larger exergy losses, the understanding of why these losses occur and how the exergy changes with the parameters associated to each system which is crucial to implement the syngas combustion from biomass products as a competitive technology.
