Optimisation of a multilevel logistics network for prepositioned warehouses under an

Background description and model assumptions

The prepositioned warehouse expands coverage and shortens delivery time through a high-density layout in the city, which also increases construction and operation costs. Moreover, the prepositioned warehouse requires a certain amount of inventory due to the high freshness requirements for fresh agricultural products. Some companies integrate stores with prepositioned warehouses to create an omni-channel sales model that offers services both online and offline simultaneously. In addition to online transactions, consumers can also make offline purchases when feasible, enjoying the shopping experience in-store. This situation broadens consumer choices and introduces uncertainty into logistics network design.

The platform purchases goods from suppliers according to the inventory plan, and regional distribution centres replenish prepositioned warehouses according to the unified arrangement. Consumers may visit the nearest prepositioned warehouse for offline shopping, or they may place an order through the platform. The platform delivers goods immediately after receiving the order, and the goods are delivered to the consumer within a few hours of the prepositioned warehouse (Fig. 3 below). In practice, regional warehouses typically have only one or two locations in a region and are mostly situated in suburban areas rather than urban areas. The location of prepositioned warehouses is a key research question addressed in this paper. The number of consumers is large and dense, and this study clusters geographically proximate consumers, who are regarded as consumer groups, and demand can be predicted by historical data.

Note: The variables in the figure are defined in Section “Notation description”.

In the process of transporting commodities from regional distribution centres to prepositioned warehouses, the distance between RDCs and prepositioned warehouses is relatively long, and the replenishment quantity is greater each time. Therefore, cold chain trucks are selected for transportation. In the distribution process of commodities from front warehouses to consumer groups, considering the short distance, dense demand points, and proximity in urban areas, electric bicycles are used for distribution due to their flexibility.

Figure 3 is a schematic diagram of logistics network planning for a prepositioned warehouse under the omni-channel retail model. The variables in the figure are defined in Section “Notation description”.

Since a prepositioned warehouse logistics network under the omni-channel retail model faces strong economic pressures, cost is an important objective of the model. Considering that prepositioned warehouses need to be operated for a long period of time, customer stickiness is very important, and time is an important indicator that affects consumer satisfaction. The timeliness of the logistics network cannot be disregarded to minimise cost, so the time factor is also taken into account as an objective of the model.

According to the basic assumptions of the LRIP, a certain number of vehicles can satisfy the task demand, and the load can satisfy the demand of any single logistics node. Based on the above description, this paper makes the following additional assumption about the model:

1. (1)

   Regional distribution centres to prepositioned warehouses use the direct transportation mode, and prepositioned warehouses to consumer demand points use the itinerant distribution mode;

2. (2)

   A prepositioned warehouse can provide both online and offline services but will not provide only offline services;

3. (3)

   If a prepositioned warehouse exists within a certain range near the consumer’s location, then the consumer may make in-store purchases. When there are multiple prepositioned warehouses in the consumer’s vicinity, the consumer will choose only one prepositioned warehouse for offline consumption;

4. (4)

   The inventory strategy of the prepositioned warehouse conforms to the classical EOQ model;

5. (5)

   The freshness penalty occurs only when goods are stocked in the prepositioned warehouse and is caused by a decrease in consumer satisfaction;

6. (6)

   Out-of-stock, delayed delivery, and returns are not considered.

Notation description

To better construct the joint optimisation model of the site selection-inventory path for a prepositioned warehouse agricultural product logistics network in omni-channel retailing mode, this paper sets the parameters and decision variables of the model as follows:

Sets

\(A\): the set of regional distribution centres;

\(I\): the set of alternative points for site selection for prepositioned warehouses;

\(J\): the set of consumer groups;

\(V\): the set of vehicles used in the regional…