The scheduling problem of distributed permutation flow shop with limited buffer aiming at production efficiency measures has attracted widespread attention due to its closer alignment with real manufacturing environme...The scheduling problem of distributed permutation flow shop with limited buffer aiming at production efficiency measures has attracted widespread attention due to its closer alignment with real manufacturing environments.However,the energy efficiency metric is often ignored.The Energy-Efficient scheduling of Distributed Permutation Flow Shop Problem with Limited Buffer(EEDPFSP-LB)with the objectives of Makespan(C_(max))and Total Energy Consumption(TEC)is studied,and a Cooperative Fruit fly Optimization Algorithm(CFOA)is proposed in this paper.First,the critical path of EEDPFSP-LB is identified,and energy-efficient operation is applied to non-critical paths to reduce the system’s energy consumption.Second,five acceptance criteria for multi-objective optimization are introduced to enhance the diversity of the population.Third,to select a superior next-generation population,a new congestion calculation method is introduced to resolve the issue of indeterminate positional relationships among non-dominated solutions with identical crowding distances at the same dominance level.Finally,CFOA is extensively tested and compared with state-of-the-art algorithms across 360 instances,demonstrating CFOA’s strong competitiveness in solving EEDPFSP-LB.展开更多
Temporal knowledge graph(TKG)reasoning has emerged as a pivotal approach in event prediction.An important yet challenging task in TKG reasoning is to predict future events by extrapolating from historical events and t...Temporal knowledge graph(TKG)reasoning has emerged as a pivotal approach in event prediction.An important yet challenging task in TKG reasoning is to predict future events by extrapolating from historical events and their correlations.Existing methods either overlook the modeling of long-term dependencies between entities or are ineffective in aggregating long-term information with recent facts.Motivated by dual process theory in cognitive sciences,we introduce TKG-LDG,an approach enhancing TKG for future entity prediction with long-term dense graph,to model event evolution in an adaptive manner.We first construct a unified dense graph from historical data to capture long-term dependencies,reflecting cumulative knowledge of entity interactions over time.This unified dense graph is compatible with any graph neural network and facilitates entity interaction learning from a long-term perspective.Then we initialize a TKG encoder from the unified dense graph to enhance short-term event interaction modeling.TKG-LDG effectively marries global context with local adaptability to recent temporal changes through its short-term recurrent encoders,in a way that mirrors human reasoning by integrating both long-term and short-term event dynamics.Extensive experiments conducted on six widely used TKG datasets demonstrate that our model outperforms strong baselines in future event prediction.展开更多
基金supported by the National Key Research and Development Program of China(No.2023YFC3011100)the National Natural Science Foundation of China(No.62373146)+3 种基金the Natural Science Foundation of Hunan Province(No.2022JJ30265)the Young Talent of Lifting Engineering for Science and Technology in Hunan Province(No.2022TJ-Q03)the Outstanding Youth Project of Education Department of Hunan Province(No.22B0476)the Key Project of Education Department of Hunan Province of China(No.23A0382).
文摘The scheduling problem of distributed permutation flow shop with limited buffer aiming at production efficiency measures has attracted widespread attention due to its closer alignment with real manufacturing environments.However,the energy efficiency metric is often ignored.The Energy-Efficient scheduling of Distributed Permutation Flow Shop Problem with Limited Buffer(EEDPFSP-LB)with the objectives of Makespan(C_(max))and Total Energy Consumption(TEC)is studied,and a Cooperative Fruit fly Optimization Algorithm(CFOA)is proposed in this paper.First,the critical path of EEDPFSP-LB is identified,and energy-efficient operation is applied to non-critical paths to reduce the system’s energy consumption.Second,five acceptance criteria for multi-objective optimization are introduced to enhance the diversity of the population.Third,to select a superior next-generation population,a new congestion calculation method is introduced to resolve the issue of indeterminate positional relationships among non-dominated solutions with identical crowding distances at the same dominance level.Finally,CFOA is extensively tested and compared with state-of-the-art algorithms across 360 instances,demonstrating CFOA’s strong competitiveness in solving EEDPFSP-LB.
基金supported by the National Natural Science Foundation of China(Nos.62176043 and 62072077)the Intelligent Terminal Key Laboratory of Sichuan Province(No.SCITLAB-30002).
文摘Temporal knowledge graph(TKG)reasoning has emerged as a pivotal approach in event prediction.An important yet challenging task in TKG reasoning is to predict future events by extrapolating from historical events and their correlations.Existing methods either overlook the modeling of long-term dependencies between entities or are ineffective in aggregating long-term information with recent facts.Motivated by dual process theory in cognitive sciences,we introduce TKG-LDG,an approach enhancing TKG for future entity prediction with long-term dense graph,to model event evolution in an adaptive manner.We first construct a unified dense graph from historical data to capture long-term dependencies,reflecting cumulative knowledge of entity interactions over time.This unified dense graph is compatible with any graph neural network and facilitates entity interaction learning from a long-term perspective.Then we initialize a TKG encoder from the unified dense graph to enhance short-term event interaction modeling.TKG-LDG effectively marries global context with local adaptability to recent temporal changes through its short-term recurrent encoders,in a way that mirrors human reasoning by integrating both long-term and short-term event dynamics.Extensive experiments conducted on six widely used TKG datasets demonstrate that our model outperforms strong baselines in future event prediction.
