Abstract
One of the top issues in logistics management and related research is to establish an effective distribution system that is adaptive to new retail and capable of lowering the cost of logistics while enhancing consumer satisfaction. Aimed at reversing the weak points of current logistics distribution patterns, a dual-objective bipolar model with optimal logistics cost and consumer satisfaction by restraining distribution time and load is tested in this paper to figure out the proper nodes and vehicle routes. Data from general and front warehouses of PuPu mall, a Fuzhou-based online retail enterprise, are made into a case study. Moreover, the immune algorithm and genetic algorithm are adopted to achieve the model solution. It is found that the immune algorithm is more efficient than the genetic algorithm in searching solutions, thus having better adaptivity and effectiveness, and also that the type of distribution vehicle plays a significant role in determining the total distribution cost.
Highlights
In recent years, online retail has shown a rapid growth trend
The mainstream of the industry consists of three trends: integrating offline with online, integrating online with offline plus self-run logistics service, and online order plus front warehouse distribution and delivery. e distribution networks of these new retail logistics have the following common characteristics: they directly face the terminal customers, they have adjacent distribution outlets for customers and fast circulation, there are high requirements for customized and timely delivery, and there is low certainty of distribution quantity and frequency
A key problem of the new retail industry to solve as well as a research hotspot in related fields is to figure out how to adapt to the changes of new retail logistics distribution to reduce logistics costs and improve customer satisfaction, the core of which is to optimize the logistics path, including the optimization of logistics nodes and the paths from terminal nodes to distribution terminals
Summary
Online retail ( known as new retail) has shown a rapid growth trend. Scholars at home and abroad have taken large-scale online-to-offline (O2O) retail enterprises (such as Suning Appliance), their offline experience stores and distribution networks, and their terminal customers (mainly in cities where e-commerce is well developed) as research objects to reach various research targets [1, 2], such as minimizing total logistics cost, minimizing driving distance [3, 4], achieving greater customer satisfaction with the optimal distribution. In view of the limitations of the existing research results on new retail logistics terminal node layout and considering customer satisfaction and total logistics cost, this paper constructs a dual-objective bilevel programming model for site selection and layout optimization of new retail distribution centers and their front warehouses. An immune algorithm is adopted to seek solutions, aiming to provide technical support for solving new retail logistics problems
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