An Electric Vehicle(EV)is an appropriate substitution for traditional transportation means for diminishing greenhouse gas emissions.However,decision-makers are beset by the limited driving range caused by the low batt...An Electric Vehicle(EV)is an appropriate substitution for traditional transportation means for diminishing greenhouse gas emissions.However,decision-makers are beset by the limited driving range caused by the low battery capacity and the long recharging time.To resolve the former issue,several transportation companies increases the travel distance of the EV by establishing recharging stations in various locations.The proposed Electric Vehicle-Routing Problem with Time Windows(E-VRPTW)and recharging stations are constructed in this context;it augments the VRPTW by reinforcing battery capacity constraints.Meanwhile,super-recharging stations are gradually emerging in the surroundings.They can decrease the recharging time for an EV but consume more energy than regular stations.In this paper,we first extend the E-VRPRTW by adding the elements of super-recharging stations.We then apply a two-stage heuristic algorithm driven by a dynamic programming process to solve the new proposed problem to minimize the travel and total recharging costs.Subsequently,we compare the experimental results of this approach with other algorithms on several sets of benchmark instances.Furthermore,we analyze the impact of super-recharging stations on the total cost of the logistic plan.展开更多
3D quality inspection is widely applied in many industrial fields including mould design, automotive and blade manufacturing, etc. A commonly used method is to obtain the point cloud of the inspected object and make a...3D quality inspection is widely applied in many industrial fields including mould design, automotive and blade manufacturing, etc. A commonly used method is to obtain the point cloud of the inspected object and make a comparison between the point cloud and the corresponding CAD model or template. Thus, it is important to align the point cloud with the template first and foremost. Moreover, for the purpose of automatization of quality inspection, this alignment process is expected to be completed without manual interference. In this paper, we propose to combine the particle swarm optimization （PSO） with iterative closest point （ICP） algorithm to achieve the automated point cloud alignment. The combination of the two algorithms can achieve a balance between the alignment speed and accuracy, and avoid the local optimal caused by bad initial position of the point cloud.展开更多
Supply chain disruption risk usually poses a serious challenge to the management of emergency supplies procurement between the government and enterprises in cooperation.To research the impact of supply chain disruptio...Supply chain disruption risk usually poses a serious challenge to the management of emergency supplies procurement between the government and enterprises in cooperation.To research the impact of supply chain disruption on the supply and demand sides of emergency supplies for disaster relief,the emergency procurement model based on quantity flexibility contract is constructed.The model introduces a stockout disruption to measure the degree of supply chain disruption and uses per unit of material relief value to quantify government disaster relief benefits.Further,it analyzes the basic pricing strategy and the agreed order quantity between the government and enterprises,focusing on the negative impact of supply disruption on the government and enterprises.The model deduction and data analysis results show that supply disruption creates a“lose-lose”situation for governments and enterprises,reducing their benefits and willingness to cooperate.Finally,a sensitivity analysis is conducted on the case data to explain the decision-making changes in the contract price and flexibility parameters between the government and enterprises before and after the supply disruption.展开更多
This paper proposed a novel distributed memetic evolutionary model,where four modules distributed exploration,intensified exploitation,knowledge transfer,and evolutionary restart are coevolved to maximize their streng...This paper proposed a novel distributed memetic evolutionary model,where four modules distributed exploration,intensified exploitation,knowledge transfer,and evolutionary restart are coevolved to maximize their strengths and achieve superior global optimality.Distributed exploration evolves three independent populations by heterogenous operators.Intensified exploitation evolves an external elite archive in parallel with exploration to balance global and local searches.Knowledge transfer is based on a point-ring communication topology to share successful experiences among distinct search agents.Evolutionary restart adopts an adaptive perturbation strategy to control search diversity reasonably.Quantum computation is a newly emerging technique,which has powerful computing power and parallelized ability.Therefore,this paper further fuses quantum mechanisms into the proposed evolutionary model to build a new evolutionary algorithm,referred to as quantum-inspired distributed memetic algorithm(QDMA).In QDMA,individuals are represented by the quantum characteristics and evolved by the quantum-inspired evolutionary optimizers in the quantum hyperspace.The QDMA integrates the superiorities of distributed,memetic,and quantum evolution.Computational experiments are carried out to evaluate the superior performance of QDMA.The results demonstrate the effectiveness of special designs and show that QDMA has greater superiority compared to the compared state-of-the-art algorithms based on Wilcoxon’s rank-sum test.The superiority is attributed not only to good cooperative coevolution of distributed memetic evolutionary model,but also to superior designs of each special component.展开更多
基金supported in part by National Institute on Drug Abuse(NIDA)grants K01 DA029643 and R01DA016750National Institute on Alcohol Abuse and Alcoholism(NIAAA)grants R21 AA021380 and R21 AA020319+9 种基金the National Alliance for Research on Schizophrenia and Depression(NARSAD)Award 17616(L.Z.)ABMRF/The Foundation for Alcohol Research(L.Z.)Funding and other supports for phenotype and genotype data were provided through the National Institutes of Health(NIH)Genes,Environment and Health Initiative(GEI)(U01HG004422,U01HG004436 and U01HG004438)the GENEVA Coordinating Center(U01HG004446)the NIAAA(U10AA008401,R01AA013320,P60AA011998)the NIDA(R01DA013423)the National Cancer Institute(P01 CA089392)the NIH contract‘High throughput genotyping for studying the genetic contributions to human disease’(HHSN268200782096C)the Center for Inherited Disease Research(CIDR)the National Center for Biotechnology Information.Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research
基金supported by the Science and Technology Innovation Team of Shaanxi Province(No.2023-CX-TD07)the Special Project in Major Fields of Guangdong Universities(No.2021ZDZX1019)+4 种基金the Major Projects of Guangdong Education Department for Foundation Research and Applied Research(Nos.2017KZDXM081 and 2018KZDXM066)the Guangdong Provincial University Innovation Team Project(No.2020KCXTD045)the Hunan Key Laboratory of Intelligent Decision-making Technology for Emergency Management(No.2020TP1013)the Research Topic of China Logistics Association and China Federation of Logistics and Purchasing(No.2022CSLKT3-151)National Social Science Fund Project(No.22BJL114).
文摘An Electric Vehicle(EV)is an appropriate substitution for traditional transportation means for diminishing greenhouse gas emissions.However,decision-makers are beset by the limited driving range caused by the low battery capacity and the long recharging time.To resolve the former issue,several transportation companies increases the travel distance of the EV by establishing recharging stations in various locations.The proposed Electric Vehicle-Routing Problem with Time Windows(E-VRPTW)and recharging stations are constructed in this context;it augments the VRPTW by reinforcing battery capacity constraints.Meanwhile,super-recharging stations are gradually emerging in the surroundings.They can decrease the recharging time for an EV but consume more energy than regular stations.In this paper,we first extend the E-VRPRTW by adding the elements of super-recharging stations.We then apply a two-stage heuristic algorithm driven by a dynamic programming process to solve the new proposed problem to minimize the travel and total recharging costs.Subsequently,we compare the experimental results of this approach with other algorithms on several sets of benchmark instances.Furthermore,we analyze the impact of super-recharging stations on the total cost of the logistic plan.
文摘3D quality inspection is widely applied in many industrial fields including mould design, automotive and blade manufacturing, etc. A commonly used method is to obtain the point cloud of the inspected object and make a comparison between the point cloud and the corresponding CAD model or template. Thus, it is important to align the point cloud with the template first and foremost. Moreover, for the purpose of automatization of quality inspection, this alignment process is expected to be completed without manual interference. In this paper, we propose to combine the particle swarm optimization （PSO） with iterative closest point （ICP） algorithm to achieve the automated point cloud alignment. The combination of the two algorithms can achieve a balance between the alignment speed and accuracy, and avoid the local optimal caused by bad initial position of the point cloud.
基金supported by the National Social Science Foundation of China(No.20BGL025).
文摘Supply chain disruption risk usually poses a serious challenge to the management of emergency supplies procurement between the government and enterprises in cooperation.To research the impact of supply chain disruption on the supply and demand sides of emergency supplies for disaster relief,the emergency procurement model based on quantity flexibility contract is constructed.The model introduces a stockout disruption to measure the degree of supply chain disruption and uses per unit of material relief value to quantify government disaster relief benefits.Further,it analyzes the basic pricing strategy and the agreed order quantity between the government and enterprises,focusing on the negative impact of supply disruption on the government and enterprises.The model deduction and data analysis results show that supply disruption creates a“lose-lose”situation for governments and enterprises,reducing their benefits and willingness to cooperate.Finally,a sensitivity analysis is conducted on the case data to explain the decision-making changes in the contract price and flexibility parameters between the government and enterprises before and after the supply disruption.
基金the National Natural Science Foundation of China(No.62273193)the Talent Introducing Project of Hebei Agricultural University(Nos.KY201903 and YJ201953).
文摘This paper proposed a novel distributed memetic evolutionary model,where four modules distributed exploration,intensified exploitation,knowledge transfer,and evolutionary restart are coevolved to maximize their strengths and achieve superior global optimality.Distributed exploration evolves three independent populations by heterogenous operators.Intensified exploitation evolves an external elite archive in parallel with exploration to balance global and local searches.Knowledge transfer is based on a point-ring communication topology to share successful experiences among distinct search agents.Evolutionary restart adopts an adaptive perturbation strategy to control search diversity reasonably.Quantum computation is a newly emerging technique,which has powerful computing power and parallelized ability.Therefore,this paper further fuses quantum mechanisms into the proposed evolutionary model to build a new evolutionary algorithm,referred to as quantum-inspired distributed memetic algorithm(QDMA).In QDMA,individuals are represented by the quantum characteristics and evolved by the quantum-inspired evolutionary optimizers in the quantum hyperspace.The QDMA integrates the superiorities of distributed,memetic,and quantum evolution.Computational experiments are carried out to evaluate the superior performance of QDMA.The results demonstrate the effectiveness of special designs and show that QDMA has greater superiority compared to the compared state-of-the-art algorithms based on Wilcoxon’s rank-sum test.The superiority is attributed not only to good cooperative coevolution of distributed memetic evolutionary model,but also to superior designs of each special component.
