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.

scholarly journals A Novel Intensive Distribution Logistics Network Design and Profit Allocation Problem considering Sharing Economy

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Mi Gan ◽  
Shuai Yang ◽  
Dandan Li ◽  
Mingfei Wang ◽  
Si Chen ◽  
...  
Keyword(s):  
Big Data ◽  
High Speed ◽  
Distribution Network ◽  
Sharing Economy ◽  
Shopping Behavior ◽  
Logistics Network ◽  
Stakeholder Satisfaction ◽  
Logistics Distribution ◽  
Distribution Logistics ◽  
Big Data Technology

The rapid growth of logistics distribution highlights the problems including the imperfect infrastructure of logistics distribution network, the serious shortage of distribution capacity of each individual enterprise, and the high cost of distribution in China. While the development of sharing economy makes it possible to achieve the integration of whole social logistic resources, big data technology can grasp customer’s logistics demand accurately on the basis of analyzing the customer’s logistics distribution preference, which contributes to the integration and optimization of the whole logistics resources. This paper proposes a kind of intensive distribution logistics network considering sharing economy, which assumes that all the social logistics suppliers build a strategic alliance, and individual idle logistics resources are also used to deal with distribution needs. Analyzing customer shopping behavior by the big data technology to determine customer’s logistics preference on the basis of dividing the customer’s logistics preference into high speed, low cost, and low pollution and then constructing the corresponding objective function model according to different logistics preferences, we obtain the intensive distribution logistics network model and solve it with heuristic algorithm. Furthermore, this paper analyzes the mechanism of interest distribution of the participants in the distribution network and puts forward an improved interval Shapley value method considering both satisfaction and contribution, with case verifying the feasibility and effectiveness of the model. The results showed that, compared with the traditional Shapley method, distribution coefficient calculated by the improved model could be fairer, improve stakeholder satisfaction, and promote the sustainable development of the alliance as well.

scholarly journals The Impact of Policy Measures on Human Mobility, COVID-19 Cases, and Mortality in the US: A Spatiotemporal Perspective

2021 ◽  
Vol 18 (3) ◽  
pp. 996
Author(s):  
Yun Li ◽  
Moming Li ◽  
Megan Rice ◽  
Haoyuan Zhang ◽  
Dexuan Sha ◽  
...  
Keyword(s):  
Growth Rate ◽  
Human Mobility ◽  
Public Information ◽  
Key Factors ◽  
Information Campaigns ◽  
Rapid Spread ◽  
The Us ◽  
Policy Measures ◽  
The Impact ◽  
At Home

Social distancing policies have been regarded as effective in containing the rapid spread of COVID-19. However, there is a limited understanding of policy effectiveness from a spatiotemporal perspective. This study integrates geographical, demographical, and other key factors into a regression-based event study framework, to assess the effectiveness of seven major policies on human mobility and COVID-19 case growth rates, with a spatiotemporal emphasis. Our results demonstrate that stay-at-home orders, workplace closures, and public information campaigns were effective in decreasing the confirmed case growth rate. For stay-at-home orders and workplace closures, these changes were associated with significant decreases (p < 0.05) in mobility. Public information campaigns did not see these same mobility trends, but the growth rate still decreased significantly in all analysis periods (p < 0.01). Stay-at-home orders and international/national travel controls had limited mitigation effects on the death case growth rate (p < 0.1). The relationships between policies, mobility, and epidemiological metrics allowed us to evaluate the effectiveness of each policy and gave us insight into the spatiotemporal patterns and mechanisms by which these measures work. Our analysis will provide policymakers with better knowledge regarding the effectiveness of measures in space–time disaggregation.

The Research of Emergency Logistics Routing Optimization Based on Improved Ant Colony Optimization

2012 ◽  
Vol 482-484 ◽  
pp. 2519-2523
Author(s):  
Teng Fei ◽  
Li Yi Zhang ◽  
Yun Shan Sun ◽  
Hong Wei Ren
Keyword(s):  
Mathematical Model ◽  
Ant Colony Algorithm ◽  
Model Solution ◽  
Ant Colony ◽  
Traffic Situation ◽  
Logistics System ◽  
Logistics Distribution ◽  
Routing Optimization ◽  
Emergency Logistics ◽  
Improved Ant Colony Algorithm

Emergency logistics system contains information on material reserves, emergency command and emergency distribution. In this paper, the aspect of emergency distribution only is analyzed in microscopic, mathematical model of emergency logistics distribution has been established in considering the traffic situation and shortage degree. On the aspect of model solution, improved ant colony algorithm, which can enhance the selectivity of finding the best solution in emergency logistics distribution routing, is used in solving the model.

scholarly journals An evaluation of three DoE-guided meta-heuristic-based solution methods for a three-echelon sustainable distribution network

2020 ◽  
Vol 296 (1-2) ◽  
pp. 421-469
Author(s):  
Sahar Validi ◽  
Arijit Bhattacharya ◽  
P. J. Byrne
Keyword(s):  
Distribution Network ◽  
Np Hard ◽  
Nsga Ii ◽  
Solution Approach ◽  
Logistics Network ◽  
Location Routing ◽  
Solution Methods ◽  
Order Preference ◽  
Operational Constraints ◽  
Transportation Routes

AbstractThis article evaluates the efficiency of three meta-heuristic optimiser (viz. MOGA-II, MOPSO and NSGA-II)-based solution methods for designing a sustainable three-echelon distribution network. The distribution network employs a bi-objective location-routing model. Due to the mathematically NP-hard nature of the model a multi-disciplinary optimisation commercial platform, modeFRONTIER®, is adopted to utilise the solution methods. The proposed Design of Experiment (DoE)-guided solution methods are of two phased that solve the NP-hard model to attain minimal total costs and total CO2 emission from transportation. Convergence of the optimisers are tested and compared. Ranking of the realistic results are examined using Pareto frontiers and the Technique for Order Preference by Similarity to Ideal Solution approach, followed by determination of the optimal transportation routes. A case of an Irish dairy processing industry’s three-echelon logistics network is considered to validate the solution methods. The results obtained through the proposed methods provide information on open/closed distribution centres (DCs), vehicle routing patterns connecting plants to DCs, open DCs to retailers and retailers to retailers, and number of trucks required in each route to transport the products. It is found that the DoE-guided NSGA-II optimiser based solution is more efficient when compared with the DoE-guided MOGA-II and MOPSO optimiser based solution methods in solving the bi-objective NP-hard three-echelon sustainable model. This efficient solution method enable managers to structure the physical distribution network on the demand side of a logistics network, minimising total cost and total CO2 emission from transportation while satisfying all operational constraints.

scholarly journals Clustering Control of Multi-Objective Problems with Application to E-commerce

2018 ◽  
Vol 1 (1) ◽  
pp. 41
Author(s):  
Liang Chen ◽  
Xingwei Wang ◽  
Jinwen Shi
Keyword(s):  
Ant Colony Algorithm ◽  
Time Window ◽  
Ant Colony ◽  
Distribution Area ◽  
Routing Problem ◽  
Logistics Distribution ◽  
Multi Objective ◽  
The Cost ◽  
Vehicle Capacity ◽  
Cost Of Transportation

In the existing logistics distribution methods, the demand of customers is not considered. The goal of these methods is to maximize the vehicle capacity, which leads to the total distance of vehicles to be too long, the need for large numbers of vehicles and high transportation costs. To address these problems, a method of multi-objective clustering of logistics distribution route based on hybrid ant colony algorithm is proposed in this paper. Before choosing the distribution route, the customers are assigned to the unknown types according to a lot of customers attributes so as to reduce the scale of the solution. The discrete point location model is applied to logistics distribution area to reduce the cost of transportation. A mathematical model of multi-objective logistics distribution routing problem is built with consideration of constraints of the capacity, transportation distance, and time window, and a hybrid ant colony algorithm is used to solve the problem. Experimental results show that, the optimized route is more desirable, which can save the cost of transportation, reduce the time loss in the process of circulation, and effectively improve the quality of logistics distribution service.

Sign in / Sign up

Export Citation Format

Share Document