A Clustering Based Routing Heuristic for Last-Mile Logistics in Fresh Food E-Commerce

2020 ◽  
pp. 097215091988979 ◽  
Author(s):  
Dhirendra Prajapati ◽  
Arjun R Harish ◽  
Yash Daultani ◽  
Harpreet Singh ◽  
Saurabh Pratap
Keyword(s):  
Clustering Algorithm ◽  
Feasible Solution ◽  
Computational Time ◽  
City Logistics ◽  
Local Distribution ◽  
Algorithm Performance ◽  
Speed Of Response ◽  
Fresh Food ◽  
Last Mile ◽  
Single Vehicle

This study considers the fresh food city logistics that involves the last-mile distribution of commodities to the customer locations from the local distribution centres (LDCs) established by the e-commerce firms. In this scenario, the last-mile logistics is crucial for its speed of response and the effectiveness in distribution of packages to the target destinations. We propose a clustering-based routing heuristic (CRH) to manage the vehicle routing for the last-mile logistic operations of fresh food in e-commerce. CRH is a clustering algorithm that performs repetitive clustering of demand nodes until the nodes within each cluster become serviceable by a single vehicle. The computational complexity of the algorithm is reduced due to the downsizing of the network through clustering and, hence, produces an optimum feasible solution in less computational time. The algorithm performance was analysed using various operating scenarios and satisfactory results were obtained.

Competing through the last mile: Strategic 3D printing in a city logistics context

2021 ◽  
Vol 131 ◽  
pp. 105248
Author(s):  
Emrah Demir ◽  
Daniel Eyers ◽  
Yuan Huang
Keyword(s):  
3D Printing ◽  
City Logistics ◽  
Last Mile

MPID Control Tuning for a Flexible Manipulator Using a Neural Network

2010 ◽  
Vol 22 (1) ◽  
pp. 82-90 ◽  
Author(s):  
Tamer Mansour ◽  
◽  
Atsushi Konno ◽  
Masaru Uchiyama
Keyword(s):  
Neural Network ◽  
Vibration Control ◽  
Learning Algorithm ◽  
The Other ◽  
Gradient Algorithm ◽  
Flexible Manipulator ◽  
Computational Time ◽  
Speed Of Response ◽  
Control Gain ◽  
The Neural Network

This paper studies the use of neural networks as a tuning tool for the gain in Modified Proportional-Integral-Derivative (MPID) control used to control a flexible manipulator. The vibration control gain in the MPID controller has been determined in an empirical way so far. It is a considerable time consuming process because the vibration control performance depends not only on the vibration control gain but also on the other parameters such as the payload, references and PD joint servo gains. Hence, the vibration control gain must be tuned considering the other parameters. In order to find optimal vibration control gain for the MPID controller, a neural network based approach is proposed in this paper. The proposed neural network finds an optimum vibration control gain that minimizes a criteria function. The criteria function is selected to represent the effect of the vibration of the end effector in addition to the speed of response. The scaled conjugate gradient algorithm is used as a learning algorithm for the neural network. Tuned gain response results are compared to results for other types of gains. The effectiveness of using the neural network appears in the reduction of the computational time and the ability to tune the gain with different loading condition.

An Efficient Genetic Fuzzy Approach to UAV Swarm Routing

2016 ◽  
Vol 04 (02) ◽  
pp. 117-127 ◽  
Author(s):  
Anoop Sathyan ◽  
Nicholas D. Ernest ◽  
Kelly Cohen
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Search Algorithm ◽  
Task Assignment ◽  
Search Space ◽  
Computational Time ◽  
Rule Base ◽  
Heuristic Search Algorithm ◽  
Multiple Traveling Salesman Problem ◽  
Uav Swarm

Fuzzy logic is used in a variety of applications because of its universal approximator attribute and nonlinear characteristics. But, it takes a lot of trial and error to come up with a set of membership functions and rule-base that will effectively work for a specific application. This process could be simplified by using a heuristic search algorithm like Genetic Algorithm (GA). In this paper, genetic fuzzy is applied to the task assignment for cooperating Unmanned Aerial Vehicles (UAVs) classified as the Polygon Visiting Multiple Traveling Salesman Problem (PVMTSP). The PVMTSP finds a lot of applications including UAV swarm routing. We propose a method of genetic fuzzy clustering that would be specific to PVMTSP problems and hence more efficient compared to k-means and c-means clustering. We developed two different algorithms using genetic fuzzy. One evaluates the distance covered by each UAV to cluster the search-space and the other uses a cost function that approximates the distance covered thus resulting in a reduced computational time. We compare these two approaches to each other as well as to an already benchmarked fuzzy clustering algorithm which is the current state-of-the-art. We also discuss how well our algorithm scales for increasing number of targets. The results are compared for small and large polygon sizes.

scholarly journals Clustering for Probability Density Functions by New k-Medoids Method

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
D. Ho-Kieu ◽  
T. Vo-Van ◽  
T. Nguyen-Trang
Keyword(s):  
Probability Density ◽  
Clustering Algorithm ◽  
Real Life ◽  
Probability Density Functions ◽  
Rand Index ◽  
Computational Time ◽  
Adjusted Rand Index ◽  
Density Functions ◽  
Potential Applications ◽  
Iteration Number

This paper proposes a novel and efficient clustering algorithm for probability density functions based on k-medoids. Further, a scheme used for selecting the powerful initial medoids is suggested, which speeds up the computational time significantly. Also, a general proof for convergence of the proposed algorithm is presented. The effectiveness and feasibility of the proposed algorithm are verified and compared with various existing algorithms through both artificial and real datasets in terms of adjusted Rand index, computational time, and iteration number. The numerical results reveal an outstanding performance of the proposed algorithm as well as its potential applications in real life.

scholarly journals Design of an Unsupervised Machine Learning-Based Movie Recommender System

2020 ◽  
Author(s):  
Debby Cintia Ganesha Putri ◽  
Jenq-Shiou Leu ◽  
Pavel Seda
Keyword(s):  
Recommender System ◽  
Clustering Algorithm ◽  
System Development ◽  
Clustering Algorithms ◽  
Mean Shift ◽  
Computational Time ◽  
Agglomerative Clustering ◽  
Method Performance ◽  
Cluster Validity Indices ◽  
Validity Indices

This research aims to determine the similarities in groups of people to build a film recommender system for users. Users often have difficulty in finding suitable movies due to the increasing amount of movie information. The recommender system is very useful for helping customers choose a preferred movie with the existing features. In this study, the recommender system development is established by using several algorithms to obtain groupings, such as the K-Means algorithm, birch algorithm, mini-batch K-Means algorithm, mean-shift algorithm, affinity propagation algorithm, agglomerative clustering algorithm, and spectral clustering algorithm. We propose methods optimizing K so that each cluster may not significantly increase variance. We are limited to using groupings based on Genre and, Tags for movies. This research can discover better methods for evaluating clustering algorithms. To verify the quality of the recommender system, we adopted the mean square error (MSE), such as the Dunn Matrix and Cluster Validity Indices, and social network analysis (SNA), such as Degree Centrality, Closeness Centrality, and Betweenness Centrality. We also used Average Similarity, Computational Time, Association Rule with Apriori algorithm, and Clustering Performance Evaluation as evaluation measures to compare method performance of recommender systems using Silhouette Coefficient, Calinski-Harabaz Index, and Davies-Bouldin Index.

scholarly journals Dbscan Assisted by Hybrid Genetic K Means Algorithm

2020 ◽  
Vol 8 (6) ◽  
pp. 1973-1979
Keyword(s):  
Data Mining ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Hybrid Approach ◽  
Research Direction ◽  
Computational Time ◽  
Main Concern ◽  
Complex Data ◽  
Data Mining Algorithms ◽  
Mining Algorithms

The data mining algorithms functioning is main concern, when the data becomes to a greater extent. Clustering analysis is a active and dispute research direction in the region of data mining for complex data samples. DBSCAN is a density-based clustering algorithm with several advantages in numerous applications. However, DBSCAN has quadratic time complexity i.e. making it complicated for realistic applications particularly with huge complex data samples. Therefore, this paper recommended a hybrid approach to reduce the time complexity by exploring the core properties of the DBSCAN in the initial stage using genetic based K-means partition algorithm. The technological experiments showed that the proposed hybrid approach obtains competitive results when compared with the usual approach and drastically improves the computational time.

Enhancing Machine Learning Aptitude Using Significant Cluster Identification for Augmented Image Refining

2019 ◽  
Vol 34 (09) ◽  
pp. 2051009
Author(s):  
Dhanalakshmi Samiappan ◽  
S. Latha ◽  
T. Rama Rao ◽  
Deepak Verma ◽  
CSA Sriharsha
Keyword(s):  
Machine Learning ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Speckle Noise ◽  
Computational Time ◽  
Significant Cluster ◽  
Edge Preservation ◽  
Cluster Identification ◽  
Base Method ◽  
Window Selection

Enhancing the image to remove noise, preserving the useful features and edges are the most important tasks in image analysis. In this paper, Significant Cluster Identification for Maximum Edge Preservation (SCI-MEP), which works in parallel with clustering algorithms and improved efficiency of the machine learning aptitude, is proposed. Affinity propagation (AP) is a base method to obtain clusters from a learnt dictionary, with an adaptive window selection, which are then refined using SCI-MEP to preserve the semantic components of the image. Since only the significant clusters are worked upon, the computational time drastically reduces. The flexibility of SCI-MEP allows it to be integrated with any clustering algorithm to improve its efficiency. The method is tested and verified to remove Gaussian noise, rain noise and speckle noise from images. Our results have shown that SCI-MEP considerably optimizes the existing algorithms in terms of performance evaluation metrics.

Towards a Faster Image Segmentation Using the K-means Algorithm on Grayscale Histogram

2016 ◽  
Vol 8 (2) ◽  
pp. 57-69 ◽  
Author(s):  
Lamine Benrais ◽  
Nadia Baha
Keyword(s):  
Image Segmentation ◽  
Equilibrium State ◽  
Clustering Algorithm ◽  
Main Idea ◽  
Computation Time ◽  
Computational Time ◽  
Original Image ◽  
Grayscale Images

The K-means is a popular clustering algorithm known for its simplicity and efficiency. However the elapsed computation time is one of its main weaknesses. In this paper, the authors use the K-means algorithm to segment grayscale images. Their aim is to reduce the computation time elapsed in the K-means algorithm by using a grayscale histogram without loss of accuracy in calculating the clusters centers. The main idea consists of calculating the histogram of the original image, applying the K-means on the histogram until the equilibrium state is reached, and computing the clusters centers then the authors use the clusters centers to run the K-means for a single iteration. Tests of accuracy and computational time are presented to show the advantages and inconveniences of the proposed method.

The Utility of Nonlinear Programming Algorithms: A Comparative Study—Part II

1980 ◽  
Vol 102 (3) ◽  
pp. 547-551 ◽  
Author(s):  
E. Sandgren ◽  
K. M. Ragsdell
Keyword(s):  
Nonlinear Programming ◽  
Comparative Study ◽  
Engineering Design ◽  
Linear Approximation ◽  
Computational Time ◽  
Approximation Methods ◽  
Algorithm Performance ◽  
Nonlinear Programming Algorithms ◽  
Performance Curves ◽  
Programming Algorithms

A comprehensive comparative study of nonlinear programming algorithms, as applied to problems in engineering design, is presented. Linear approximation methods, interior penalty and exterior penalty methods were tested on a set of thirty problems and are rated on their ability to solve problems within a reasonable amount of computational time. In this paper, we give and discuss numerical results and algorithm performance curves.

Last Mile and City Logistics – der Kampf um die letzte Meile

2021 ◽  
Vol 35 (07-08) ◽  
pp. 43-45
Author(s):  
Rebecca Vlassakidis
Keyword(s):  
City Logistics ◽  
Last Mile ◽  
Online Business

Für Kurier-, Express- und Paket-Dienstleister könnten es goldene Zeiten sein: Dank des boomenden Online-Business steigt das Volumen an bestellten Waren in etlichen Märkten enorm. So wurden laut dem Bundesverband Paket und Expresslogistik (BIEK) 2018 in Deutschland insgesamt 3,5 Milliarden Warensendungen an Unternehmen und Endverbraucher ausgeliefert. Das ist ein neuer Rekord und entspricht einem Zuwachs von über 50 Prozent seit dem Jahr 2000.

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