Open AccessClustering Control of Multi-Objective Problems with Application to E-commerce
Author(s) -
Chen Liang,
Xingwei Wang,
Jinwen Shi
Publication year - 2018
Publication title -
international journal of robotics and control
Language(s) - English
Resource type - Journals
eISSN - 2577-7769
pISSN - 2577-7742
DOI - 10.5430/ijrc.v1n1p41
Subject(s) - cluster analysis , computer science , vehicle routing problem , ant colony optimization algorithms , process (computing) , operations research , service quality , service level , service (business) , control (management) , quality (philosophy) , routing (electronic design automation) , distribution (mathematics) , mathematical optimization , transport engineering , engineering , mathematics , algorithm , business , artificial intelligence , computer network , mathematical analysis , philosophy , statistics , epistemology , marketing , operating system
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.