In this study, a vehicle routing problem with hard time windows (VRPHTW) that appears to meet demands of customers' service within time intervals in a supermarket chain is solved. In VRPHTW, to reach a solution by...
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In this study, a vehicle routing problem with hard time windows (VRPHTW) that appears to meet demands of customers' service within time intervals in a supermarket chain is solved. In VRPHTW, to reach a solution by an exact method is quite difficult and sometimes impossible if number of constraints is large enough (i.e., NP-hard), and solution time of a VRPHTW grows exponentially. As the size of the problem grows, a near optimal solution can be found using a heuristic method. A hierarchical approach consisting of two stages as "cluster-first route-second" is proposed. In the first stage, customers are assigned to vehicles using three different clusteringalgorithms (i.e., k-means, k-medoids and DBSCAN). In the second stage, a VRPHTW is solved using a MILP. The main contribution of the article is that the proposed hierarchical approach enables us to deal with a large size real problem and to solve it in a short time using the exact method. Finally, the proposed approach is employed on a supermarket chain. An instance of the algorithm is demonstrated to illustrate the applicability of the proposed approach and the results obtained are highly favourable.
PurposeSubway systems are highly susceptible to external disturbances from emergencies, triggering a series of consequences such as the paralysis of the internal network transportation functions, causing significant e...
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PurposeSubway systems are highly susceptible to external disturbances from emergencies, triggering a series of consequences such as the paralysis of the internal network transportation functions, causing significant economic and safety losses to cities. Therefore, it is necessary to analyze the factors affecting the resilience of the subway system to reduce the impact of disaster ***/methodology/approachUsing the interval type-2 fuzzy linguistic term set and the k-medoids clustering algorithm, this paper improves the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to construct a subway resilience factor analysis model for emergencies. Through comparative analysis, this study confirms the superior performance of the proposed approach in enhancing the precision of the DEMATEL *** The results indicate that the operation and management level of emergency command organizations is the key resilience factors of subway operations in China. Furthermore, based on real case analyses, the corresponding suggestions and measures are put forward to improve the overall operation resilience level of the ***/value This paper identifies four emergency scenarios and 15 resilience factors affecting subway operations through literature review and expert consultation. The improved fuzzy DEMATEL method is applied to explore the levels of influence and causal mechanisms among the resilience factors of the subway system under the four emergency scenarios.
In this paper, moving object detection under the dynamic background in aerial videos has been studied, and a moving vehicle detection algorithm based on the motion vector is proposed. using the kLT algorithm for match...
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ISBN:
(纸本)9783642273339
In this paper, moving object detection under the dynamic background in aerial videos has been studied, and a moving vehicle detection algorithm based on the motion vector is proposed. using the kLT algorithm for matching feature points, according to k-medoids clustering algorithm on the feature points, finally the location of the target vehicle has been found. Experiments show that the method in this article can accurately detect the moving vehicles in the aerial videos, and then extract the corresponding traffic information.
Search and retrieval of multimedia content based on the evoked emotion comprises an interesting scientific field with numerous applications. This paper proposes a method that fuses two heterogeneous modalities, i.e. m...
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ISBN:
(纸本)9781728127118
Search and retrieval of multimedia content based on the evoked emotion comprises an interesting scientific field with numerous applications. This paper proposes a method that fuses two heterogeneous modalities, i.e. music and electroencephalographic signals, both for predicting emotional dimensions in the valence-arousal plane and for addressing four binary classification tasks, namely i.e. high/low arousal, positive/negative valence, high/low dominance, high/low liking. The proposed solution exploits Mel-scaled and EEG spectrograms feeding a k-medoidsclustering scheme based on canonical correlation analysis. A thorough experimental campaign carried out on a publicly available dataset confirms the efficacy of such an approach. Despite its low computational cost, it was able to surpass state of the art results, and most importantly, in a user-independent manner.
The classic TSP problem was researched on and CHN144 was chosen to be the data for research. The method that combined MDP and k-medoids was proposed to solve TSP problem in this paper. First of all, cluster CHN144 dat...
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ISBN:
(纸本)9783642319679
The classic TSP problem was researched on and CHN144 was chosen to be the data for research. The method that combined MDP and k-medoids was proposed to solve TSP problem in this paper. First of all, cluster CHN144 data through k-medoids and find out the representative objects respectively. Furthermore, the simple TSP problem that consists of representative objects was solved to acquire the optimal path through the Markov Decision Process. Finally, the global optimal path was acquired as 30445km by using the solution above iteratively to the clustering of each object respectively. The feasibility and superiority of this method was proved by analyzing the experiments we conducted in this paper.
Rapid urbanization in mountainous areas has caused unbalanced development, land use conflicts, and decreased urban spatial efficiency (USE) due to terrain constraints and disorderly land expansion, and this issue has ...
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Rapid urbanization in mountainous areas has caused unbalanced development, land use conflicts, and decreased urban spatial efficiency (USE) due to terrain constraints and disorderly land expansion, and this issue has not been fully explored. To address this gap, this study constructs a framework to quantify the impacts of landscape gradients on urban spatial efficiency. By developing indicator systems to characterize both landscape heterogeneity and urban spatial efficiency, the relationships between these factors are analyzed using the Boosting Regression Trees model, and clusters are identified to reveal spatial differentiation through key landscape gradient indicators. The results indicate that (1) the Summit Density (Sds) of Normalized Difference Vegetation Index (NDVI) exhibits the most significant negative impact on urban spatial efficiency, especially on spatial utilization efficiency;(2) the Root Mean Square Slope (Sdq) of Digital Elevation negatively affects traffic and public services efficiency;(3) the Texture Direction Index (Stdi) of building distribution has the most significant positive impact on public service efficiency. In mountainous environments, different landscape gradient types exhibit clear contrasts in urban spatial efficiency. Varied elevations lead to diverse construction sites and building layouts within urban blocks, resulting in spatial differentiation of urban spatial efficiency. This study enhances the use of gradient surface metrics in urban spatial research by describing landscape patterns and heterogeneity at the block scale. It offers valuable insights for urban planning and design with a focus on equity and sustainability.
A novel human action recognition algorithm based on key posture is proposed in this *** the method,the mesh features of each image in human action sequences are firstly calculated;then the key postures of the human me...
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A novel human action recognition algorithm based on key posture is proposed in this *** the method,the mesh features of each image in human action sequences are firstly calculated;then the key postures of the human mesh features are generated through k-medoids clustering algorithm;and the motion sequences are thus represented as vectors of key *** component of the vector is the occurrence number of the corresponding posture included in the *** human action recognition,the observed action is firstly changed into key posture vector;then the correlevant coefficients to the training samples are calculated and the action which best matches the observed sequence is chosen as the final *** experiments on Weizmann dataset demonstrate that our method is effective for human action *** average recognition accuracy can exceed 90%.
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