The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant...The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP efficiently.The work is novel and contributes to three aspects:problemmodel,optimization framework,and algorithmdesign.Firstly,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over time.Secondly,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic environment.The framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous environments.This way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme library.In the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational complexity.Thirdly,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search efficiency.To evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic algorithms.The experimental results show that ACO with 2-opt can solve the DTSPs effectively.展开更多
This paper proposes a novel cargo loading algorithm applicable to automated conveyor-type loading systems.The algorithm offers improvements in computational efficiency and robustness by utilizing the concept of discre...This paper proposes a novel cargo loading algorithm applicable to automated conveyor-type loading systems.The algorithm offers improvements in computational efficiency and robustness by utilizing the concept of discrete derivatives and introducing logistics-related constraints.Optional consideration of the rotation of the cargoes was made to further enhance the optimality of the solutions,if possible to be physically implemented.Evaluation metrics were developed for accurate evaluation and enhancement of the algorithm’s ability to efficiently utilize the loading space and provide a high level of dynamic stability.Experimental results demonstrate the extensive robustness of the proposed algorithm to the diversity of cargoes present in Business-to-Consumer environments.This study contributes practical advancements in both cargo loading optimization and automation of the logistics industry,with potential applications in last-mile delivery services,warehousing,and supply chain management.展开更多
In-depth understanding of interactions between crude oil and CO2 provides insight into the CO2-based enhanced oil recovery（EOR） process design and simulation. When CO2 contacts crude oil, the dissolution process tak...In-depth understanding of interactions between crude oil and CO2 provides insight into the CO2-based enhanced oil recovery（EOR） process design and simulation. When CO2 contacts crude oil, the dissolution process takes place. This phenomenon results in the oil swelling, which depends on the temperature, pressure, and composition of the oil. The residual oil saturation in a CO2-based EOR process is inversely proportional to the oil swelling factor. Hence, it is important to estimate this influential parameter with high precision. The current study suggests the predictive model based on the least-squares support vector machine（LS-SVM） to calculate the CO2–oil swelling factor. A genetic algorithm is used to optimize hyperparameters（у and б^2） of the LS-SVM model. This model showed a high coefficient of determination（R^2= 0.9953） and a low value for the mean-squared error（MSE = 0.0003） based on the available experimental data while estimating the CO2–oil swelling factor. It was found that LS-SVM is a straightforward and accurate method to determine the CO2–oil swelling factor with negligible uncertainty. This method can be incorporated in commercial reservoir simulators to include the effect of the CO2–oil swelling factor when adequate experimental data are not available.展开更多
Taking the advantage of the nearly 14 000 items of muhi-source, multi-dimension practical dataset of type 2 diabetes, and a series of data mining experiments are designed to seek for important type 2 diabetes risk fac...Taking the advantage of the nearly 14 000 items of muhi-source, multi-dimension practical dataset of type 2 diabetes, and a series of data mining experiments are designed to seek for important type 2 diabetes risk factors and their relationships with blood glucose. The valuable pathological knowledge includes, the deci- sion tree is almost identical with the list of clinical diabetic risk factors; 9 items important risk factors of type 2 diabetes were found, and the relationship between the main risk factors and the blood glucose, and the feature of critical value of the risk factors were given too in this paper. These valuable results are good to the cure and macro-control type 2 diabetes.展开更多
基金supported in part by the National Research Foundation of Korea (NRF-2021H1D3A2A01082705).
文摘The dynamic traveling salesman problem(DTSP)is significant in logistics distribution in real-world applications in smart cities,but it is uncertain and difficult to solve.This paper proposes a scheme library-based ant colony optimization(ACO)with a two-optimization(2-opt)strategy to solve the DTSP efficiently.The work is novel and contributes to three aspects:problemmodel,optimization framework,and algorithmdesign.Firstly,in the problem model,traditional DTSP models often consider the change of travel distance between two nodes over time,while this paper focuses on a special DTSP model in that the node locations change dynamically over time.Secondly,in the optimization framework,the ACO algorithm is carried out in an offline optimization and online application framework to efficiently reuse the historical information to help fast respond to the dynamic environment.The framework of offline optimization and online application is proposed due to the fact that the environmental change inDTSPis caused by the change of node location,and therefore the newenvironment is somehowsimilar to certain previous environments.This way,in the offline optimization,the solutions for possible environmental changes are optimized in advance,and are stored in a mode scheme library.In the online application,when an environmental change is detected,the candidate solutions stored in the mode scheme library are reused via ACO to improve search efficiency and reduce computational complexity.Thirdly,in the algorithm design,the ACO cooperates with the 2-opt strategy to enhance search efficiency.To evaluate the performance of ACO with 2-opt,we design two challenging DTSP cases with up to 200 and 1379 nodes and compare them with other ACO and genetic algorithms.The experimental results show that ACO with 2-opt can solve the DTSPs effectively.
基金supported by the BK21 FOUR funded by the Ministry of Education of Korea and National Research Foundation of Korea,a Korea Agency for Infrastructure Technology Advancement(KAIA)grant funded by the Ministry of Land,Infrastructure,and Transport(Grant 1615013176)IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ICAN(ICT Challenge and Advanced Network of HRD)grant funded by the Korea government(Ministry of Science and ICT)(RS-2024-00438411).
文摘This paper proposes a novel cargo loading algorithm applicable to automated conveyor-type loading systems.The algorithm offers improvements in computational efficiency and robustness by utilizing the concept of discrete derivatives and introducing logistics-related constraints.Optional consideration of the rotation of the cargoes was made to further enhance the optimality of the solutions,if possible to be physically implemented.Evaluation metrics were developed for accurate evaluation and enhancement of the algorithm’s ability to efficiently utilize the loading space and provide a high level of dynamic stability.Experimental results demonstrate the extensive robustness of the proposed algorithm to the diversity of cargoes present in Business-to-Consumer environments.This study contributes practical advancements in both cargo loading optimization and automation of the logistics industry,with potential applications in last-mile delivery services,warehousing,and supply chain management.
基金financial support from Natural Sciences and Engineering Research Council of Canada (NSERC), Innovate NL, and Statoil Canada
文摘In-depth understanding of interactions between crude oil and CO2 provides insight into the CO2-based enhanced oil recovery（EOR） process design and simulation. When CO2 contacts crude oil, the dissolution process takes place. This phenomenon results in the oil swelling, which depends on the temperature, pressure, and composition of the oil. The residual oil saturation in a CO2-based EOR process is inversely proportional to the oil swelling factor. Hence, it is important to estimate this influential parameter with high precision. The current study suggests the predictive model based on the least-squares support vector machine（LS-SVM） to calculate the CO2–oil swelling factor. A genetic algorithm is used to optimize hyperparameters（у and б^2） of the LS-SVM model. This model showed a high coefficient of determination（R^2= 0.9953） and a low value for the mean-squared error（MSE = 0.0003） based on the available experimental data while estimating the CO2–oil swelling factor. It was found that LS-SVM is a straightforward and accurate method to determine the CO2–oil swelling factor with negligible uncertainty. This method can be incorporated in commercial reservoir simulators to include the effect of the CO2–oil swelling factor when adequate experimental data are not available.
基金Sponsored by the National Natural Science Foundation of China(60671008)the National Science and Technology Support Project(2006038070031)the National"863"Program Project(2006AA02Z429)
文摘Taking the advantage of the nearly 14 000 items of muhi-source, multi-dimension practical dataset of type 2 diabetes, and a series of data mining experiments are designed to seek for important type 2 diabetes risk factors and their relationships with blood glucose. The valuable pathological knowledge includes, the deci- sion tree is almost identical with the list of clinical diabetic risk factors; 9 items important risk factors of type 2 diabetes were found, and the relationship between the main risk factors and the blood glucose, and the feature of critical value of the risk factors were given too in this paper. These valuable results are good to the cure and macro-control type 2 diabetes.
