scholarly journals Balanced COD-CLARANS: A Constrained Clustering Algorithm to Optimize Logistics Distribution Network

2016 ◽  
Author(s):  
Tong Zhang ◽  
Dong Wang ◽  
Haonan Chen
Keyword(s):  
Clustering Algorithm ◽  
Distribution Network ◽  
Constrained Clustering ◽  
Logistics Distribution

scholarly journals B2C E-COMMERCE CUSTOMIZED LOGISTICS AREA INVENTORY ALLOCATION STRATEGY BASED ON CONSTRAINED CLUSTERING ALGORITHM

2018 ◽  
Vol 48 (3) ◽  
pp. 217-222
Author(s):  
J. K. HUANG
Keyword(s):  
Growth Rate ◽  
Clustering Algorithm ◽  
Online Shopping ◽  
Distribution Network ◽  
Distribution Area ◽  
Logistics Network ◽  
Logistics Distribution ◽  
Routing Optimization ◽  
Inventory Allocation ◽  
At Home

Logistics network includes regional logistics network and urban logistics network. In this paper, the urban logistics network is taken as the research object. With the improvement of the consumption level of the residents, the attention to quality has become an important reference standard for consumers to choose e-commerce, which makes the electric business shift from "price war" to "service war". Compared with past purchases in physical stores, most consumers prefer to choose convenient and fast online shopping. As a result, the size of the online shopping market has increased rapidly. According to statistics, the growth rate of the online shopping market in the past five years is over 100%, and the growth rate will slow down in the next few years, but it will still maintain steady growth. The importance of logistics for an e-business enterprise is obvious. The improvement and perfection of logistics distribution network is imminent. Scholars at home and abroad have studied this aspect for a long time. This research is based on the optimization of ecommerce logistics distribution network. By summing up the ideas and solutions proposed by researchers at home and abroad for this problem, and combining with the actual situation, a method of optimizing the B2C e-commerce logistics distribution network is designed. Considering the special traffic situation and the actual order demand in the city, the distribution area division, the distribution site stratification, the vehicle routing optimization and the logistics network optimization model are set up, and a combination of various methods is used to solve the problem.

scholarly journals A Customer Clustering Algorithm for Power Logistics Distribution Network Structure and Distribution Volume Constraints

2021 ◽  
Vol 15 ◽  
pp. 1051-1056
Author(s):  
Jianying Zhong ◽  
Jibin Zhu ◽  
Yonghao Guo ◽  
Yunxin Chang ◽  
Chaofeng Zhu
Keyword(s):  
Network Structure ◽  
Clustering Algorithm ◽  
Distribution Network ◽  
Distribution Volume ◽  
Distribution Center ◽  
Vehicle Scheduling ◽  
Route Optimization ◽  
Scheduling Optimization ◽  
Logistics Distribution ◽  
Center Location

Customer clustering technology for distribution process is widely used in location selection, distribution route optimization and vehicle scheduling optimization of power logistics distribution center. Aiming at the problem of customer clustering with unknown distribution center location, this paper proposes a clustering algorithm considering distribution network structure and distribution volume constraint, which makes up for the defect that the classical Euclidean distance does not consider the distribution road information. This paper proposes a logistics distribution customer clustering algorithm, which improves CLARANS algorithm to make the clustering results meet the constraints of customer distribution volume. By using the single vehicle load rate, the sufficient conditions for logistics distribution customer clustering to be solvable under the condition of considering the ubiquitous and constraints are given, which effectively solves the problem of logistics distribution customer clustering with sum constraints. The results state clearly that the clustering algorithm can effectively deal with large-scale spatial data sets, and the clustering process is not affected by isolated customers, The clustering results can be effectively applied to the distribution center location, distribution cost optimization, distribution route optimization and distribution area division of vehicle scheduling optimization.

scholarly journals Research on Enterprise Supply Chain Optimization Model and Algorithm Based on Fuzzy Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Shizhen Bai ◽  
Hongbin Sun
Keyword(s):  
Supply Chain ◽  
Fuzzy Clustering ◽  
Network Structure ◽  
Optimization Model ◽  
Economies Of Scale ◽  
Clustering Algorithm ◽  
Distribution Network ◽  
Supply Chain Optimization ◽  
Logistics Distribution ◽  
Logistics Enterprises

Reasonable logistics distribution network structure can not only effectively reduce the cost of logistics enterprises themselves but also reduce the social cost. Through effective supply chain management, enterprises can significantly reduce costs, improve competitiveness, and enhance their ability to resist risks. Because the single-level distribution network structure of production enterprises is not suitable for large-scale logistics distribution, this paper proposes a distribution network structure design that accords with economies of scale and establishes an enterprise supply chain optimization model based on the fuzzy clustering algorithm. Using this optimization method to optimize the inventory of enterprise logistics supply chain, the operation is fast, the result is correct and reasonable, and it can provide good decision support for the distribution network of logistics enterprises. Through information technology and modern management technology, we should effectively control and coordinate the logistics, information flow, and capital flow in the production and operation process and organically integrate the internal supply chain with the external supply chain for management, so as to achieve the goal of global optimization.

Construction of Community Logistics Distribution Network Based on K-Means Algorithm

2014 ◽  
Vol 641-642 ◽  
pp. 1271-1274 ◽  
Author(s):  
Mei Li ◽  
Jian Zhang ◽  
Xiao Peng Zhou
Keyword(s):  
Community Service ◽  
Goal Programming ◽  
Distribution Network ◽  
Suitable Model ◽  
Distribution Centers ◽  
Two Stage ◽  
Logistics Distribution ◽  
Carry Capacity ◽  
Multi Objective Programming ◽  
Network’S Optimization

The paper take the distribution radius and carry capacity as constraint conditions, the author uses two-stage K-means algorithm to cluster community service shops, and determines the distribution region of distribution centers, and constructs a suitable model for distribution center’s locating. Basing on the clustering result, the incompatible two kinds items, i.e. fresh items and the items shopped online, are united in a model to be solved. Since bottom-up approach is used to build distribution network step by step, a multi-objective programming is converted into two relatively independent single goal programming, so the network’s optimization result is of good controllability, and the algorithm’s complexity is greatly reduced. Finally, take 100 communities fresh items as examples to implement algorithm.

scholarly journals Active Learning for Constrained Document Clustering with Uncertainty Region

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
M. A. Balafar ◽  
R. Hazratgholizadeh ◽  
M. R. F. Derakhshi
Keyword(s):  
Iterative Process ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Distance Matrix ◽  
Support Vector ◽  
Constrained Clustering ◽  
Similarity Matrix ◽  
Improve Accuracy ◽  
Degree Of Similarity ◽  
Accuracy And Stability

Constrained clustering is intended to improve accuracy and personalization based on the constraints expressed by an Oracle. In this paper, a new constrained clustering algorithm is proposed and some of the informative data pairs are selected during an iterative process. Then, they are presented to the Oracle and their relation is answered with “Must-link (ML) or Cannot-link (CL).” In each iteration, first, the support vector machine (SVM) is utilized based on the label produced by the current clustering. According to the distance of each document from the hyperplane, the distance matrix is created. Also, based on cosine similarity of word2vector of each document, the similarity matrix is created. Two types of probability (similarity and degree of similarity) are calculated and they are smoothed for belonging to neighborhoods. Neighborhoods form the samples that are labeled by Oracle, to be in the same cluster. Finally, at the end of each iteration, the data with a greater level of uncertainty (in term of probability) is selected for questioning the oracle. In order to evaluate, the proposed method is compared with famous state-of-the-art methods based on two criteria and over a standard dataset. The result demonstrates an increased accuracy and stability of the obtained result with fewer questions.

Sign in / Sign up

Export Citation Format

Share Document