This paper addresses the Multi-Vehicle Routing Problem with Time Windows and Simultaneous Pickup and Delivery(MVRPTWSPD),aiming to optimize logistics distribution routes and minimize total costs.A vehicle routing opti...This paper addresses the Multi-Vehicle Routing Problem with Time Windows and Simultaneous Pickup and Delivery(MVRPTWSPD),aiming to optimize logistics distribution routes and minimize total costs.A vehicle routing optimization model is developed based on the operational requirements of the KS Logistics Center,focusing on minimizing vehicle dispatch,loading and unloading,operating,and time window penalty costs.The model incorporates constraints such as vehicle capacity,time windows,and travel distance,and is solved using a genetic algorithm to ensure optimal route planning.Through MATLAB simulations,34 customer points are analyzed,demonstrating that the simultaneous pickup and delivery model reduces total costs by 30.13%,increases vehicle loading rates by 20.04%,and decreases travel distance compared to delivery-only or pickup-only models.The results demonstrate the significant advantages of the simultaneous pickup and delivery mode in reducing logistics costs and improving vehicle utilization,offering valuable insights for enhancing the operational efficiency of the KS Logistics Center.展开更多
The Vehicle Routing Problem with Time Windows(VRPTW)presents a significant challenge in combinatorial optimization,especially under real-world uncertainties such as variable travel times,service durations,and dynamic ...The Vehicle Routing Problem with Time Windows(VRPTW)presents a significant challenge in combinatorial optimization,especially under real-world uncertainties such as variable travel times,service durations,and dynamic customer demands.These uncertainties make traditional deterministic models inadequate,often leading to suboptimal or infeasible solutions.To address these challenges,this work proposes an adaptive hybrid metaheuristic that integrates Genetic Algorithms(GA)with Local Search(LS),while incorporating stochastic uncertainty modeling through probabilistic travel times.The proposed algorithm dynamically adjusts parameters—such as mutation rate and local search probability—based on real-time search performance.This adaptivity enhances the algorithm’s ability to balance exploration and exploitation during the optimization process.Travel time uncertainties are modeled using Gaussian noise,and solution robustness is evaluated through scenario-based simulations.We test our method on a set of benchmark problems from Solomon’s instance suite,comparing its performance under deterministic and stochastic conditions.Results show that the proposed hybrid approach achieves up to a 9%reduction in expected total travel time and a 40% reduction in time window violations compared to baseline methods,including classical GA and non-adaptive hybrids.Additionally,the algorithm demonstrates strong robustness,with lower solution variance across uncertainty scenarios,and converges faster than competing approaches.These findings highlight the method’s suitability for practical logistics applications such as last-mile delivery and real-time transportation planning,where uncertainty and service-level constraints are critical.The flexibility and effectiveness of the proposed framework make it a promising candidate for deployment in dynamic,uncertainty-aware supply chain environments.展开更多
Seismic data reconstruction can provide high-density sampling and regular input data for inversion and imaging,playing a crucial role in seismic data processing.In seismic data reconstruction,a common scenario involve...Seismic data reconstruction can provide high-density sampling and regular input data for inversion and imaging,playing a crucial role in seismic data processing.In seismic data reconstruction,a common scenario involves a significant distance between the source and the first receiver,which makes it unattainable to acquire near-offset data.A new workflow for seismic data extrapolation is proposed to address this issue,which is based on a multi-scale dynamic time warping(MS-DTW)algorithm.MS-DTW can accurately calculate the time-shift between two time series and is a robust method for predicting time-offset(t-x)domain data.Using the time-shift calculated by the MS-DTW as the basic input,predict the two-way traveltime(TWT)of other traces based on the TWT of the reference trace.Perform autoregressive polynomial fitting on TWT and extrapolate TWT based on the fitted polynomial coefficients.Extract amplitude information from the TWT curve,fit the amplitude curve,and extrapolate the amplitude using polynomial coefficients.The proposed workflow does not necessitate data conversion to other domains and does not require prior knowledge of underground geological information.It applies to both isotropic and anisotropic media.The effectiveness of the workflow was verified through synthetic data and field data.The results show that compared with the method of predictive painting based on local slope,this approach can accurately predict missing near-offset seismic signals and demonstrates good robustness to noise.展开更多
To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand,customer cancellation service,and change of customer delivery address,...To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand,customer cancellation service,and change of customer delivery address,based on the ideas of pre-optimization and real-time optimization,a two-stage planning model of dynamic demand based vehicle routing problem with time windows was established.At the pre-optimization stage,an improved genetic algorithm was used to obtain the pre-optimized distribution route,a large-scale neighborhood search method was integrated into the mutation operation to improve the local optimization performance of the genetic algorithm,and a variety of operators were introduced to expand the search space of neighborhood solutions;At the real-time optimization stage,a periodic optimization strategy was adopted to transform a complex dynamic problem into several static problems,and four neighborhood search operators were used to quickly adjust the route.Two different scale examples were designed for experiments.It is proved that the algorithm can plan the better route,and adjust the distribution route in time under the real-time constraints.Therefore,the proposed algorithm can provide theoretical guidance for suppliers to solve the dynamic demand based vehicle routing problem.展开更多
Face detection is applied to many tasks such as auto focus control, surveillance, user interface, and face recognition. Processing speed and detection accuracy of the face detection have been improved continuously. Th...Face detection is applied to many tasks such as auto focus control, surveillance, user interface, and face recognition. Processing speed and detection accuracy of the face detection have been improved continuously. This paper describes a novel method of fast face detection with multi-scale window search free from image resizing. We adopt statistics of gradient images (SGI) as image features and append an overlapping cell array to improve detection accuracy. The SGI feature is scale invariant and insensitive to small difference of pixel value. These characteristics enable the multi-scale window search without image resizing. Experimental results show that processing speed of our method is 3.66 times faster than a conventional method, adopting HOG features combined to an SVM classifier, without accuracy degradation.展开更多
Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a ...Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.展开更多
在Matlab/Simulink中应用Real-Time Windows Target模块重构状态观测器的实时仿真模型,采用该方法开发的实验系统既便于实现,又能和实际物理系统良好对接,有利于学生掌握状态观测器的相关知识和促进对Matlab的了解和应用。给出的应用实...在Matlab/Simulink中应用Real-Time Windows Target模块重构状态观测器的实时仿真模型,采用该方法开发的实验系统既便于实现,又能和实际物理系统良好对接,有利于学生掌握状态观测器的相关知识和促进对Matlab的了解和应用。给出的应用实例验证了方法的可行性。展开更多
The time dependent vehicle routing problem with time windows（TDVRPTW） is considered. A multi-type ant system（MTAS） algorithm hybridized with the ant colony system（ACS）and the max-min ant system（MMAS） algorithm...The time dependent vehicle routing problem with time windows（TDVRPTW） is considered. A multi-type ant system（MTAS） algorithm hybridized with the ant colony system（ACS）and the max-min ant system（MMAS） algorithms is proposed. This combination absorbs the merits of the two algorithms in solutions construction and optimization separately. In order to improve the efficiency of the insertion procedure, a nearest neighbor selection（NNS） mechanism, an insertion local search procedure and a local optimization procedure are specified in detail. And in order to find a balance between good scouting performance and fast convergence rate, an adaptive pheromone updating strategy is proposed in the MTAS. Computational results confirm the MTAS algorithm＇s good performance with all these strategies on classic vehicle routing problem with time windows（VRPTW） benchmark instances and the TDVRPTW instances, and some better results especially for the number of vehicles and travel times of the best solutions are obtained in comparison with the previous research.展开更多
The petrol truck routing problem is an important part of the petrol supply chain.This study focuses on determining routes for distributing petrol products from a depot to petrol stations with the objective of minimizi...The petrol truck routing problem is an important part of the petrol supply chain.This study focuses on determining routes for distributing petrol products from a depot to petrol stations with the objective of minimizing the total travel cost and the fixed cost required to use the trucks.We propose a mathematical model that considers petrol trucks returning to a depot multiple times and develop a heuristic algorithm based on a local branch-and-bound search with a tabu list and the Metropolis acceptance criterion.In addition,an approach that accelerates the solution process by adding several valid inequalities is presented.In this study,the trucks are homogeneous and have two compartments,and each truck can execute at most three tasks daily.The sales company arranges the transfer amount and the time windows for each station.The performance of the proposed algorithm is evaluated by comparing its results with the optimal results.In addition,a real-world case of routing petrol trucks in Beijing is studied to demonstrate the effectiveness of the proposed approach.展开更多
Unmanned combat air vehicles(UCAVs) mission planning is a fairly complicated global optimum problem. Military attack missions often employ a fleet of UCAVs equipped with weapons to attack a set of known targets. A UCA...Unmanned combat air vehicles(UCAVs) mission planning is a fairly complicated global optimum problem. Military attack missions often employ a fleet of UCAVs equipped with weapons to attack a set of known targets. A UCAV can carry different weapons to accomplish different combat missions. Choice of different weapons will have different effects on the final combat effectiveness. This work presents a mixed integer programming model for simultaneous weapon configuration and route planning of UCAVs, which solves the problem optimally using the IBM ILOG CPLEX optimizer for simple missions. This paper develops a heuristic algorithm to handle the medium-scale and large-scale problems. The experiments demonstrate the performance of the heuristic algorithm in solving the medium scale and large scale problems. Moreover, we give suggestions on how to select the most appropriate algorithm to solve different scale problems.展开更多
The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with dive...The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP(MO-MTPDPTWMP) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection(MOILS-ANS) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed.展开更多
Objective To evaluate the utility of computed tomography perfusion(CTP)both at admission and during delayed cerebral ischemia time-window(DCITW)in the detection of delayed cerebral ischemia(DCI)and the change in CTP p...Objective To evaluate the utility of computed tomography perfusion(CTP)both at admission and during delayed cerebral ischemia time-window(DCITW)in the detection of delayed cerebral ischemia(DCI)and the change in CTP parameters from admission to DCITW following aneurysmal subarachnoid hemorrhage.Methods Eighty patients underwent CTP at admission and during DCITW.The mean and extreme values of all CTP parameters at admission and during DCITW were compared between the DCI group and non-DCI group,and comparisons were also made between admission and DCITW within each group.The qualitative color-coded perfusion maps were recorded.Finally,the relationship between CTP parameters and DCI was assessed by receiver operating characteristic(ROC)analyses.Results With the exception of cerebral blood volume(P=0.295,admission;P=0.682,DCITW),there were significant differences in the mean quantitative CTP parameters between DCI and non-DCI patients both at admission and during DCITW.In the DCI group,the extreme parameters were significantly different between admission and DCITW.The DCI group also showed a deteriorative trend in the qualitative color-coded perfusion maps.For the detection of DCI,mean transit time to the center of the impulse response function(Tmax)at admission and mean time to start(TTS)during DCITW had the largest area under curve(AUC),0.698 and 0.789,respectively.Conclusion Whole-brain CTP can predict the occurrence of DCI at admission and diagnose DCI during DCITW.The extreme quantitative parameters and qualitative color-coded perfusion maps can better reflect the perfusion changes of patients with DCI from admission to DCITW.展开更多
As a new variant of vehicle routing problem( VRP),a finished vehicle routing problem with time windows in finished vehicle logistics( FVRPTW) is modeled and solved. An optimization model for FVRPTW is presented with t...As a new variant of vehicle routing problem( VRP),a finished vehicle routing problem with time windows in finished vehicle logistics( FVRPTW) is modeled and solved. An optimization model for FVRPTW is presented with the objective of scheduling multiple transport routes considering loading constraints along with time penalty function to minimize the total cost. Then a genetic algorithm( GA) is developed. The specific encoding and genetic operators for FVRPTW are devised.Especially,in order to accelerate its convergence,an improved termination condition is given. Finally,a case study is used to evaluate the effectiveness of the proposed algorithm and a series of experiments are conducted over a set of finished vehicle routing problems. The results demonstrate that the proposed approach has superior performance and satisfies users in practice. Contributions of the study are the modeling and solving of a complex FVRPTW in logistics industry.展开更多
The paper aims to optimize the therapeutic dose and time window of picroside II by orthogonal test in cerebral ischemic injury in rats. The forebrain ischemia models were established by bilateral common carotid artery...The paper aims to optimize the therapeutic dose and time window of picroside II by orthogonal test in cerebral ischemic injury in rats. The forebrain ischemia models were established by bilateral common carotid artery occlusion (BCCAO) methods. The successful models were randomly divided into sixteen groups according to orthogonal experimental design and treated by injecting picroside II intraperitonenally at different ischemic time with different dose. The concentrations of neuron-specific enolase (NSE), neuroglial marker protein S100B and myelin basic protein (MBP) in serum were determined by enzyme linked immunosorbent assay to evaluate the therapeutic effect of picroside II in cerebral ischemic injury. The results indicated that best therapeutic time window and dose of picroside II in cerebral ischemic injury were ischemia 1.5 h with 20 mg/kg body weight according to the concentrations of NSE, S100B and MBP in serum. It is concluded that according to the principle of lowest therapeutic dose with longest time window, the optimized therapeutic dose and time window are injecting picroside II intraperitonenally with 20 mg/kg body weight at ischemia 1.5 h in cerebral ischemic injury in rats.展开更多
A preliminary study from our research group showed that picroside II inhibited neuronal apop- tosis in ischemic penumbra, reduced ischemic volume, and improved neurobehavioral function in rats with cerebral ischemia. ...A preliminary study from our research group showed that picroside II inhibited neuronal apop- tosis in ischemic penumbra, reduced ischemic volume, and improved neurobehavioral function in rats with cerebral ischemia. The aim of the present study was to validate the neuroprotective effects of picroside II and optimize its therapeutic time window and dose in a rat model of cerebral ischemia. We found that picroside Ⅱ inhibited cell apoptosis and reduced the expression of neuron-specific enolase, a marker of neuronal damage, in rats after cerebral ischemic injury. The optimal treatment time after ischemic injury and dose were determined, respectively, as follows： （1） 2.0 hours and 10 mg/kg according to the results of toluidine blue staining; （2） 1.5 hours and 10 mg/kg according to early apoptotic ratio by flow cytometry; （3） 2.0 hours and 10 mg/kg according to immunohistochemical and western blot analysis; and （4） 1.5 hours and 10 mg/kg according to reverse transcription polymerase chain reaction. The present findings suggest that an intraperitoneal injection of 10 mg/kg picroside II 1.5-2.0 hours after cerebral ischemic injury in rats is the optimal dose and time for therapeutic benefit.展开更多
Based on the analysis of the refined oil distribution plan which includes the various vehicles models-single oil-multiple gas stations. This paper puts forward the dynamic time window of oil supplementation based on e...Based on the analysis of the refined oil distribution plan which includes the various vehicles models-single oil-multiple gas stations. This paper puts forward the dynamic time window of oil supplementation based on every moment, and establishes the mathematical model of the refined oil distribution plan, using C language, taking various circumstances of the model into account to find the optimal solution through several operations. This process can make the refined oil distribution plan and the distribution route is more reasonable. At the same time, the distribution cost is lowest. Through the analysis of the experimental results, the validity and algorithm of the model are proved.展开更多
Time series anomaly detection is an important task in many applications,and deep learning based time series anomaly detection has made great progress.However,due to complex device interactions,time series exhibit dive...Time series anomaly detection is an important task in many applications,and deep learning based time series anomaly detection has made great progress.However,due to complex device interactions,time series exhibit diverse abnormal signal shapes,subtle anomalies,and imbalanced abnormal instances,which make anomaly detection in time series still a challenge.Fusion and analysis of multivariate time series can help uncover their intrinsic spatio-temporal characteristics,and contribute to the discovery of complex and subtle anomalies.In this paper,we propose a novel approach named Multi-scale Convolution Fusion and Memory-augmented Adversarial AutoEncoder(MCFMAAE)for multivariate time series anomaly detection.It is an encoder-decoder-based framework with four main components.Multi-scale convolution fusion module fuses multi-sensor signals and captures various scales of temporal information.Self-attention-based encoder adopts the multi-head attention mechanism for sequence modeling to capture global context information.Memory module is introduced to explore the internal structure of normal samples,capturing it into the latent space,and thus remembering the typical pattern.Finally,the decoder is used to reconstruct the signals,and then a process is coming to calculate the anomaly score.Moreover,an additional discriminator is added to the model,which enhances the representation ability of autoencoder and avoids overfitting.Experiments on public datasets demonstrate that MCFMAAE improves the performance compared to other state-of-the-art methods,which provides an effective solution for multivariate time series anomaly detection.展开更多
With the expansion of the application scope of social computing problems,many path problems in real life have evolved from pure path optimization problems to social computing problems that take into account various so...With the expansion of the application scope of social computing problems,many path problems in real life have evolved from pure path optimization problems to social computing problems that take into account various social attributes,cultures,and the emotional needs of customers.The actual soft time window vehicle routing problem,speeding up the response of customer needs,improving distribution efficiency,and reducing operating costs is the focus of current social computing problems.Therefore,designing fast and effective algorithms to solve this problem has certain theoretical and practical significance.In this paper,considering the time delay problem of customer demand,the compensation problem is given,and the mathematical model of vehicle path problem with soft time window is given.This paper proposes a hybrid tabu search(TS)&scatter search(SS)algorithm for vehicle routing problem with soft time windows(VRPSTW),which mainly embeds the TS dynamic tabu mechanism into the SS algorithm framework.TS uses the scattering of SS to avoid the dependence on the quality of the initial solution,and SS uses the climbing ability of TS improves the ability of optimizing,so that the quality of search for the optimal solution can be significantly improved.The hybrid algorithm is still based on the basic framework of SS.In particular,TS is mainly used for solution improvement and combination to generate new solutions.In the solution process,both the quality and the dispersion of the solution are considered.A simulation experiments verify the influence of the number of vehicles and maximum value of tabu length on solution,parameters’control over the degree of convergence,and the influence of the number of diverse solutions on algorithm performance.Based on the determined parameters,simulation experiment is carried out in this paper to further prove the algorithm feasibility and effectiveness.The results of this paper provide further ideas for solving vehicle routing problems with time windows and improving the efficiency of vehicle routing problems and have strong applicability.展开更多
The aim is to optimize the anti-inflammatory effect and the therapeutic dose and time window of picrosede II by orthogonal test in cerebral ischemic injury in rats. The forebrain ischemia models were established by bi...The aim is to optimize the anti-inflammatory effect and the therapeutic dose and time window of picrosede II by orthogonal test in cerebral ischemic injury in rats. The forebrain ischemia models were established by bilateral common carotid artery occlusion (BCCAO) methods in 30 Wistar rats. The successful models were randomly divided into sixteen groups according to orthogonal experimental design and treated by injecting picroside II intraperitoneally at different ischemic time with different dose. The concentrations of aquaporins 4 (AQP4), matrix metalloproteinases9 (MMP9) and cyclooxygenase 2 (COX2) in serum and brain tissue were determined by enzyme linked immunosorbent assay to evaluate the therapeutic effect of picroside II in cerebral ischemic injury. The best therapeutic time window and dose of picroside II in cerebral ischemic injury were 1) ischemia 2.0 h with 20 mg/kg and 1.5 h with 20 mg/kg body weight according to the concentration of AQP4 in serum and brain tissue;2) ischemia 1.5 h with 20 mg/kg and ischemia 2.0 h with 20 mg/kg according to the concentrations of MMP9 in serum and brain tissue;and 3) ischemia 1.5 h with 10 mg/kg and ischemia 1.5 h with 20 mg/kg according to the concentrations of COX2 in serum and brain tissue respectively. According to the principle of the lowest therapeutic dose with the longest time window, the optimized therapeutic dose and time window were injecting picroside II intraperitoneally with 10 - 20 mg/kg body weight at ischemia 1.5 - 2.0 h in cerebral ischemic injury.展开更多
The development of the cotton fiber is very sensitive to temperature variation, and high temperature stress often causes reduced fiber yield and fiber quality. Short-term high temperature stress often occurs during co...The development of the cotton fiber is very sensitive to temperature variation, and high temperature stress often causes reduced fiber yield and fiber quality. Short-term high temperature stress often occurs during cotton production, but little is known about the specific timing and duration of stress that affects fiber development. To make this clear, pot experiments were carried in 2014 and 2015 in a climate chamber using cotton cultivars HY370WR（less sensitive variety） and Sumian 15（heat sensitive variety）, which present different temperature sensitivities. Changes of the most important fiber quality indices（i.e., fiber length, fiber strength and marcironaire） and three very important fiber development components（i.e., cellulose, sucrose and callose） were analyzed to define the time window and critical duration to the high temperature stress at 34°C（max38°C/min30°C）. When developing bolls were subjected to 5 days of high temperature stress at different days post-anthesis（DPA）, the changes（Δ%） of fiber length, strength and micronire, as a function of imposed time followed square polynomial eq. as y=a＋bx＋cx^2, and the time around 15 DPA was the most sensitive period for fiber quality development in response to heat stress. When 15 DPA bolls were heat-stressed for different durations（2, 3, 4, 5, 6, 7 days）, the changes（Δ%） of fiber length, strength and micronire, as a function of stress duration followed logistic equations y=A_1-A_2/1＋（x/x_0）~p＋A_2. Referred to that 5, 10 and 15% are usually used as criteria to decide whether techniques are effective or changes are significant in crop culture practice and reguard to the fiber quality indices change range, we suggested that 5% changes of the major fiber quality indices（fiber length, fiber strength and micronaire） and 10% changes of fiber development components（cellulose, sucrose and callose） could be taken as criteria to judge whether fiber development and fiber quality have been significantly affected by high temperature stress. The key time window for cotton fiber development in response to the high temperature stress was 13–19 DPA, and the critical duration was about 5 days.展开更多
文摘This paper addresses the Multi-Vehicle Routing Problem with Time Windows and Simultaneous Pickup and Delivery(MVRPTWSPD),aiming to optimize logistics distribution routes and minimize total costs.A vehicle routing optimization model is developed based on the operational requirements of the KS Logistics Center,focusing on minimizing vehicle dispatch,loading and unloading,operating,and time window penalty costs.The model incorporates constraints such as vehicle capacity,time windows,and travel distance,and is solved using a genetic algorithm to ensure optimal route planning.Through MATLAB simulations,34 customer points are analyzed,demonstrating that the simultaneous pickup and delivery model reduces total costs by 30.13%,increases vehicle loading rates by 20.04%,and decreases travel distance compared to delivery-only or pickup-only models.The results demonstrate the significant advantages of the simultaneous pickup and delivery mode in reducing logistics costs and improving vehicle utilization,offering valuable insights for enhancing the operational efficiency of the KS Logistics Center.
文摘The Vehicle Routing Problem with Time Windows(VRPTW)presents a significant challenge in combinatorial optimization,especially under real-world uncertainties such as variable travel times,service durations,and dynamic customer demands.These uncertainties make traditional deterministic models inadequate,often leading to suboptimal or infeasible solutions.To address these challenges,this work proposes an adaptive hybrid metaheuristic that integrates Genetic Algorithms(GA)with Local Search(LS),while incorporating stochastic uncertainty modeling through probabilistic travel times.The proposed algorithm dynamically adjusts parameters—such as mutation rate and local search probability—based on real-time search performance.This adaptivity enhances the algorithm’s ability to balance exploration and exploitation during the optimization process.Travel time uncertainties are modeled using Gaussian noise,and solution robustness is evaluated through scenario-based simulations.We test our method on a set of benchmark problems from Solomon’s instance suite,comparing its performance under deterministic and stochastic conditions.Results show that the proposed hybrid approach achieves up to a 9%reduction in expected total travel time and a 40% reduction in time window violations compared to baseline methods,including classical GA and non-adaptive hybrids.Additionally,the algorithm demonstrates strong robustness,with lower solution variance across uncertainty scenarios,and converges faster than competing approaches.These findings highlight the method’s suitability for practical logistics applications such as last-mile delivery and real-time transportation planning,where uncertainty and service-level constraints are critical.The flexibility and effectiveness of the proposed framework make it a promising candidate for deployment in dynamic,uncertainty-aware supply chain environments.
基金the National Natural Science Foundation of China(42374133)the Beijing Nova Program(2022056)for their funding of this research。
文摘Seismic data reconstruction can provide high-density sampling and regular input data for inversion and imaging,playing a crucial role in seismic data processing.In seismic data reconstruction,a common scenario involves a significant distance between the source and the first receiver,which makes it unattainable to acquire near-offset data.A new workflow for seismic data extrapolation is proposed to address this issue,which is based on a multi-scale dynamic time warping(MS-DTW)algorithm.MS-DTW can accurately calculate the time-shift between two time series and is a robust method for predicting time-offset(t-x)domain data.Using the time-shift calculated by the MS-DTW as the basic input,predict the two-way traveltime(TWT)of other traces based on the TWT of the reference trace.Perform autoregressive polynomial fitting on TWT and extrapolate TWT based on the fitted polynomial coefficients.Extract amplitude information from the TWT curve,fit the amplitude curve,and extrapolate the amplitude using polynomial coefficients.The proposed workflow does not necessitate data conversion to other domains and does not require prior knowledge of underground geological information.It applies to both isotropic and anisotropic media.The effectiveness of the workflow was verified through synthetic data and field data.The results show that compared with the method of predictive painting based on local slope,this approach can accurately predict missing near-offset seismic signals and demonstrates good robustness to noise.
基金supported by Natural Science Foundation Project of Gansu Provincial Science and Technology Department(No.1506RJZA084)Gansu Provincial Education Department Scientific Research Fund Grant Project(No.1204-13).
文摘To provide the supplier with the minimizum vehicle travel distance in the distribution process of goods in three situations of new customer demand,customer cancellation service,and change of customer delivery address,based on the ideas of pre-optimization and real-time optimization,a two-stage planning model of dynamic demand based vehicle routing problem with time windows was established.At the pre-optimization stage,an improved genetic algorithm was used to obtain the pre-optimized distribution route,a large-scale neighborhood search method was integrated into the mutation operation to improve the local optimization performance of the genetic algorithm,and a variety of operators were introduced to expand the search space of neighborhood solutions;At the real-time optimization stage,a periodic optimization strategy was adopted to transform a complex dynamic problem into several static problems,and four neighborhood search operators were used to quickly adjust the route.Two different scale examples were designed for experiments.It is proved that the algorithm can plan the better route,and adjust the distribution route in time under the real-time constraints.Therefore,the proposed algorithm can provide theoretical guidance for suppliers to solve the dynamic demand based vehicle routing problem.
文摘Face detection is applied to many tasks such as auto focus control, surveillance, user interface, and face recognition. Processing speed and detection accuracy of the face detection have been improved continuously. This paper describes a novel method of fast face detection with multi-scale window search free from image resizing. We adopt statistics of gradient images (SGI) as image features and append an overlapping cell array to improve detection accuracy. The SGI feature is scale invariant and insensitive to small difference of pixel value. These characteristics enable the multi-scale window search without image resizing. Experimental results show that processing speed of our method is 3.66 times faster than a conventional method, adopting HOG features combined to an SVM classifier, without accuracy degradation.
基金The National Natural Science Foundation of China(No.61074147)the Natural Science Foundation of Guangdong Province(No.S2011010005059)+2 种基金the Foundation of Enterprise-University-Research Institute Cooperation from Guangdong Province and Ministry of Education of China(No.2012B091000171,2011B090400460)the Science and Technology Program of Guangdong Province(No.2012B050600028)the Science and Technology Program of Huadu District,Guangzhou(No.HD14ZD001)
文摘Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.
文摘The time dependent vehicle routing problem with time windows（TDVRPTW） is considered. A multi-type ant system（MTAS） algorithm hybridized with the ant colony system（ACS）and the max-min ant system（MMAS） algorithms is proposed. This combination absorbs the merits of the two algorithms in solutions construction and optimization separately. In order to improve the efficiency of the insertion procedure, a nearest neighbor selection（NNS） mechanism, an insertion local search procedure and a local optimization procedure are specified in detail. And in order to find a balance between good scouting performance and fast convergence rate, an adaptive pheromone updating strategy is proposed in the MTAS. Computational results confirm the MTAS algorithm＇s good performance with all these strategies on classic vehicle routing problem with time windows（VRPTW） benchmark instances and the TDVRPTW instances, and some better results especially for the number of vehicles and travel times of the best solutions are obtained in comparison with the previous research.
基金the Program of “Study on Optimization and Supply-side Reliability of Oil Product Supply Chain Logistics System” funded under the National Natural Science Foundation of China, Grant Number 51874325
文摘The petrol truck routing problem is an important part of the petrol supply chain.This study focuses on determining routes for distributing petrol products from a depot to petrol stations with the objective of minimizing the total travel cost and the fixed cost required to use the trucks.We propose a mathematical model that considers petrol trucks returning to a depot multiple times and develop a heuristic algorithm based on a local branch-and-bound search with a tabu list and the Metropolis acceptance criterion.In addition,an approach that accelerates the solution process by adding several valid inequalities is presented.In this study,the trucks are homogeneous and have two compartments,and each truck can execute at most three tasks daily.The sales company arranges the transfer amount and the time windows for each station.The performance of the proposed algorithm is evaluated by comparing its results with the optimal results.In addition,a real-world case of routing petrol trucks in Beijing is studied to demonstrate the effectiveness of the proposed approach.
基金supported by the National Natural Science Foundation of China(7147117571471174)
文摘Unmanned combat air vehicles(UCAVs) mission planning is a fairly complicated global optimum problem. Military attack missions often employ a fleet of UCAVs equipped with weapons to attack a set of known targets. A UCAV can carry different weapons to accomplish different combat missions. Choice of different weapons will have different effects on the final combat effectiveness. This work presents a mixed integer programming model for simultaneous weapon configuration and route planning of UCAVs, which solves the problem optimally using the IBM ILOG CPLEX optimizer for simple missions. This paper develops a heuristic algorithm to handle the medium-scale and large-scale problems. The experiments demonstrate the performance of the heuristic algorithm in solving the medium scale and large scale problems. Moreover, we give suggestions on how to select the most appropriate algorithm to solve different scale problems.
基金supported by the National Key R&D Program of China(2018AAA0101203)the National Natural Science Foundation of China(61673403,71601191)the JSPS KAKENHI(JP17K12751)。
文摘The multitrip pickup and delivery problem with time windows and manpower planning(MTPDPTW-MP)determines a set of ambulance routes and finds staff assignment for a hospital. It involves different stakeholders with diverse interests and objectives. This study firstly introduces a multiobjective MTPDPTW-MP(MO-MTPDPTWMP) with three objectives to better describe the real-world scenario. A multiobjective iterated local search algorithm with adaptive neighborhood selection(MOILS-ANS) is proposed to solve the problem. MOILS-ANS can generate a diverse set of alternative solutions for decision makers to meet their requirements. To better explore the search space, problem-specific neighborhood structures and an adaptive neighborhood selection strategy are carefully designed in MOILS-ANS. Experimental results show that the proposed MOILS-ANS significantly outperforms the other two multiobjective algorithms. Besides, the nature of objective functions and the properties of the problem are analyzed. Finally, the proposed MOILS-ANS is compared with the previous single-objective algorithm and the benefits of multiobjective optimization are discussed.
基金supported by the National Natural Science Foundation of China,Research on Brain Magnetic Resonance Image Segmentation Based on Particle Computation(No.61672386).
文摘Objective To evaluate the utility of computed tomography perfusion(CTP)both at admission and during delayed cerebral ischemia time-window(DCITW)in the detection of delayed cerebral ischemia(DCI)and the change in CTP parameters from admission to DCITW following aneurysmal subarachnoid hemorrhage.Methods Eighty patients underwent CTP at admission and during DCITW.The mean and extreme values of all CTP parameters at admission and during DCITW were compared between the DCI group and non-DCI group,and comparisons were also made between admission and DCITW within each group.The qualitative color-coded perfusion maps were recorded.Finally,the relationship between CTP parameters and DCI was assessed by receiver operating characteristic(ROC)analyses.Results With the exception of cerebral blood volume(P=0.295,admission;P=0.682,DCITW),there were significant differences in the mean quantitative CTP parameters between DCI and non-DCI patients both at admission and during DCITW.In the DCI group,the extreme parameters were significantly different between admission and DCITW.The DCI group also showed a deteriorative trend in the qualitative color-coded perfusion maps.For the detection of DCI,mean transit time to the center of the impulse response function(Tmax)at admission and mean time to start(TTS)during DCITW had the largest area under curve(AUC),0.698 and 0.789,respectively.Conclusion Whole-brain CTP can predict the occurrence of DCI at admission and diagnose DCI during DCITW.The extreme quantitative parameters and qualitative color-coded perfusion maps can better reflect the perfusion changes of patients with DCI from admission to DCITW.
基金Supported by the National Natural Science Foundation of China(No.51565036)
文摘As a new variant of vehicle routing problem( VRP),a finished vehicle routing problem with time windows in finished vehicle logistics( FVRPTW) is modeled and solved. An optimization model for FVRPTW is presented with the objective of scheduling multiple transport routes considering loading constraints along with time penalty function to minimize the total cost. Then a genetic algorithm( GA) is developed. The specific encoding and genetic operators for FVRPTW are devised.Especially,in order to accelerate its convergence,an improved termination condition is given. Finally,a case study is used to evaluate the effectiveness of the proposed algorithm and a series of experiments are conducted over a set of finished vehicle routing problems. The results demonstrate that the proposed approach has superior performance and satisfies users in practice. Contributions of the study are the modeling and solving of a complex FVRPTW in logistics industry.
文摘The paper aims to optimize the therapeutic dose and time window of picroside II by orthogonal test in cerebral ischemic injury in rats. The forebrain ischemia models were established by bilateral common carotid artery occlusion (BCCAO) methods. The successful models were randomly divided into sixteen groups according to orthogonal experimental design and treated by injecting picroside II intraperitonenally at different ischemic time with different dose. The concentrations of neuron-specific enolase (NSE), neuroglial marker protein S100B and myelin basic protein (MBP) in serum were determined by enzyme linked immunosorbent assay to evaluate the therapeutic effect of picroside II in cerebral ischemic injury. The results indicated that best therapeutic time window and dose of picroside II in cerebral ischemic injury were ischemia 1.5 h with 20 mg/kg body weight according to the concentrations of NSE, S100B and MBP in serum. It is concluded that according to the principle of lowest therapeutic dose with longest time window, the optimized therapeutic dose and time window are injecting picroside II intraperitonenally with 20 mg/kg body weight at ischemia 1.5 h in cerebral ischemic injury in rats.
基金supported by the National Natural Science Foundation of China,No.81041092,81274116
文摘A preliminary study from our research group showed that picroside II inhibited neuronal apop- tosis in ischemic penumbra, reduced ischemic volume, and improved neurobehavioral function in rats with cerebral ischemia. The aim of the present study was to validate the neuroprotective effects of picroside II and optimize its therapeutic time window and dose in a rat model of cerebral ischemia. We found that picroside Ⅱ inhibited cell apoptosis and reduced the expression of neuron-specific enolase, a marker of neuronal damage, in rats after cerebral ischemic injury. The optimal treatment time after ischemic injury and dose were determined, respectively, as follows： （1） 2.0 hours and 10 mg/kg according to the results of toluidine blue staining; （2） 1.5 hours and 10 mg/kg according to early apoptotic ratio by flow cytometry; （3） 2.0 hours and 10 mg/kg according to immunohistochemical and western blot analysis; and （4） 1.5 hours and 10 mg/kg according to reverse transcription polymerase chain reaction. The present findings suggest that an intraperitoneal injection of 10 mg/kg picroside II 1.5-2.0 hours after cerebral ischemic injury in rats is the optimal dose and time for therapeutic benefit.
文摘Based on the analysis of the refined oil distribution plan which includes the various vehicles models-single oil-multiple gas stations. This paper puts forward the dynamic time window of oil supplementation based on every moment, and establishes the mathematical model of the refined oil distribution plan, using C language, taking various circumstances of the model into account to find the optimal solution through several operations. This process can make the refined oil distribution plan and the distribution route is more reasonable. At the same time, the distribution cost is lowest. Through the analysis of the experimental results, the validity and algorithm of the model are proved.
基金supported by the National Key Research and Development Program of China(No.2021YFB3300503)the Regional Innovation and Development Joint Fund of NSFC(No.U22A20167).
文摘Time series anomaly detection is an important task in many applications,and deep learning based time series anomaly detection has made great progress.However,due to complex device interactions,time series exhibit diverse abnormal signal shapes,subtle anomalies,and imbalanced abnormal instances,which make anomaly detection in time series still a challenge.Fusion and analysis of multivariate time series can help uncover their intrinsic spatio-temporal characteristics,and contribute to the discovery of complex and subtle anomalies.In this paper,we propose a novel approach named Multi-scale Convolution Fusion and Memory-augmented Adversarial AutoEncoder(MCFMAAE)for multivariate time series anomaly detection.It is an encoder-decoder-based framework with four main components.Multi-scale convolution fusion module fuses multi-sensor signals and captures various scales of temporal information.Self-attention-based encoder adopts the multi-head attention mechanism for sequence modeling to capture global context information.Memory module is introduced to explore the internal structure of normal samples,capturing it into the latent space,and thus remembering the typical pattern.Finally,the decoder is used to reconstruct the signals,and then a process is coming to calculate the anomaly score.Moreover,an additional discriminator is added to the model,which enhances the representation ability of autoencoder and avoids overfitting.Experiments on public datasets demonstrate that MCFMAAE improves the performance compared to other state-of-the-art methods,which provides an effective solution for multivariate time series anomaly detection.
基金This work was supported by the National Natural Science Foundation of China(61772196,61472136)the Hunan Provincial Focus Social Science Fund(2016ZDB006)Thanks to Professor Weijin Jiang for his guidance and suggestions on this research.Funding Statement。
文摘With the expansion of the application scope of social computing problems,many path problems in real life have evolved from pure path optimization problems to social computing problems that take into account various social attributes,cultures,and the emotional needs of customers.The actual soft time window vehicle routing problem,speeding up the response of customer needs,improving distribution efficiency,and reducing operating costs is the focus of current social computing problems.Therefore,designing fast and effective algorithms to solve this problem has certain theoretical and practical significance.In this paper,considering the time delay problem of customer demand,the compensation problem is given,and the mathematical model of vehicle path problem with soft time window is given.This paper proposes a hybrid tabu search(TS)&scatter search(SS)algorithm for vehicle routing problem with soft time windows(VRPSTW),which mainly embeds the TS dynamic tabu mechanism into the SS algorithm framework.TS uses the scattering of SS to avoid the dependence on the quality of the initial solution,and SS uses the climbing ability of TS improves the ability of optimizing,so that the quality of search for the optimal solution can be significantly improved.The hybrid algorithm is still based on the basic framework of SS.In particular,TS is mainly used for solution improvement and combination to generate new solutions.In the solution process,both the quality and the dispersion of the solution are considered.A simulation experiments verify the influence of the number of vehicles and maximum value of tabu length on solution,parameters’control over the degree of convergence,and the influence of the number of diverse solutions on algorithm performance.Based on the determined parameters,simulation experiment is carried out in this paper to further prove the algorithm feasibility and effectiveness.The results of this paper provide further ideas for solving vehicle routing problems with time windows and improving the efficiency of vehicle routing problems and have strong applicability.
文摘The aim is to optimize the anti-inflammatory effect and the therapeutic dose and time window of picrosede II by orthogonal test in cerebral ischemic injury in rats. The forebrain ischemia models were established by bilateral common carotid artery occlusion (BCCAO) methods in 30 Wistar rats. The successful models were randomly divided into sixteen groups according to orthogonal experimental design and treated by injecting picroside II intraperitoneally at different ischemic time with different dose. The concentrations of aquaporins 4 (AQP4), matrix metalloproteinases9 (MMP9) and cyclooxygenase 2 (COX2) in serum and brain tissue were determined by enzyme linked immunosorbent assay to evaluate the therapeutic effect of picroside II in cerebral ischemic injury. The best therapeutic time window and dose of picroside II in cerebral ischemic injury were 1) ischemia 2.0 h with 20 mg/kg and 1.5 h with 20 mg/kg body weight according to the concentration of AQP4 in serum and brain tissue;2) ischemia 1.5 h with 20 mg/kg and ischemia 2.0 h with 20 mg/kg according to the concentrations of MMP9 in serum and brain tissue;and 3) ischemia 1.5 h with 10 mg/kg and ischemia 1.5 h with 20 mg/kg according to the concentrations of COX2 in serum and brain tissue respectively. According to the principle of the lowest therapeutic dose with the longest time window, the optimized therapeutic dose and time window were injecting picroside II intraperitoneally with 10 - 20 mg/kg body weight at ischemia 1.5 - 2.0 h in cerebral ischemic injury.
基金funded by the National Natural Science Foundation of China(31271654,31471444)the Natural Science Foundation of Jiangsu Province,China(BK20131318)
文摘The development of the cotton fiber is very sensitive to temperature variation, and high temperature stress often causes reduced fiber yield and fiber quality. Short-term high temperature stress often occurs during cotton production, but little is known about the specific timing and duration of stress that affects fiber development. To make this clear, pot experiments were carried in 2014 and 2015 in a climate chamber using cotton cultivars HY370WR（less sensitive variety） and Sumian 15（heat sensitive variety）, which present different temperature sensitivities. Changes of the most important fiber quality indices（i.e., fiber length, fiber strength and marcironaire） and three very important fiber development components（i.e., cellulose, sucrose and callose） were analyzed to define the time window and critical duration to the high temperature stress at 34°C（max38°C/min30°C）. When developing bolls were subjected to 5 days of high temperature stress at different days post-anthesis（DPA）, the changes（Δ%） of fiber length, strength and micronire, as a function of imposed time followed square polynomial eq. as y=a＋bx＋cx^2, and the time around 15 DPA was the most sensitive period for fiber quality development in response to heat stress. When 15 DPA bolls were heat-stressed for different durations（2, 3, 4, 5, 6, 7 days）, the changes（Δ%） of fiber length, strength and micronire, as a function of stress duration followed logistic equations y=A_1-A_2/1＋（x/x_0）~p＋A_2. Referred to that 5, 10 and 15% are usually used as criteria to decide whether techniques are effective or changes are significant in crop culture practice and reguard to the fiber quality indices change range, we suggested that 5% changes of the major fiber quality indices（fiber length, fiber strength and micronaire） and 10% changes of fiber development components（cellulose, sucrose and callose） could be taken as criteria to judge whether fiber development and fiber quality have been significantly affected by high temperature stress. The key time window for cotton fiber development in response to the high temperature stress was 13–19 DPA, and the critical duration was about 5 days.
