# DMRVR: Dynamic Milk-Run Vehicle Routing Solution Using Fog-Based Vehicular Ad Hoc Networks
###### Abstract  
Milk-run tours with time windows are an essential strategy to collect goods to minimize production and transportation costs. Due to unexpected events at the supplier production or traffic congestion, delays can occur during the vehicle route execution, causing non-compliance between the logistics operator and the company. This paper describes the DMRVR (Dynamic Milk-Run Vehicle Routing) solution that uses a dynamic routing algorithm along with fog-based vehicular ad hoc networks for implementing the collection of goods in milk-run operations that respect the company's time window. When a production delay occurs, the supplier sends a message through the vehicular network to alert the pickup vehicle, forcing it to make dynamic route changes to satisfy the constraints. We have implemented a queue with a timeout and retransmission features to improve the vehicular network's message delivery. To assess the DMRVR solution, we analyzed the efficiency of the dynamic vehicle routing and the vehicular network impacts. In the experiments, we used an event-based network simulator OMNeT++ bidirectionally coupled with SUMO (Simulation of Urban Mobility), aiming to make the most realistic simulations. Simulation results show the average route time was lower than the time limit imposed by the company with the DMRVR solution. In dense vehicular network scenarios, the message delivery success rate is higher. Conversely, when the vehicular network scenario is sparse, it is necessary to balance network coverage and distribute more RSUs in specific places. dynamic vehicle routing; milk-run operation; vehicular ad hoc networks +
Footnote †: journal: 27  
0000-0001-5000-0000-00001-5000][PERSON]  
1,2,3,4,5,6,7,8,9,10,11,  
to the company that requested the service [4; 6]. Figure 1 illustrates a Milk-run operation. This strategy affords an economical transport approach, as it allows the aggregation of small and frequent orders in a single pickup vehicle [7]. In addition to reducing costs, this strategy provides a reduction in traffic and, consequently, in fuel consumption and gas emissions [4; 5; 8]. Milk-run has widely been used due to the following reasons [4]:  
* Reduction of warehouse spaces by small and frequent collections, and a decrease in maintenance and labor costs;
* Reduction of transportation costs by aggregating orders in a single pickup vehicle;
* Improvement of the production line because of a synchronization between suppliers and Original Equipment Manufacturer (OEM), and delivery with higher precision;
* Enhancement of vehicle loading rates due to shortening of the total distance traveled, and cargo reduction;
* Reduction of delivery of low-quality goods since the OEM company can now quickly identify a problem with the goods, inform the supplier, and minimize the production line impact
* A significant reduction of OEM inventories by outsourcing the manufacturing of certain goods. Collecting goods from different suppliers increases the operation time when compared to a direct shipment from the supplier to the OEM company [6]. However, this issue becomes less problematic if all route participants are located in the same geographic region. The milk-run pickup routing problem is a variation of the vehicle routing problem (VRP), where information about the location of the supplier and its collected cargo volume must be considered in the planning of the route [4]. The VRP consists of determining \(m\) routes. A vehicle route starts at a specific point, the pickup vehicle visits customers in a predefined sequence and returns to the starting point. Each customer must be visited once and the total cargo must not exceed the vehicle capacity. All relevant information for the planning of the routes, such as location and customer demands, and estimated service time, is assumed to be known by the planner before the routing process begins. This information does not change after the routes have been defined [9]. The vehicle routing aim is to  
Figure 1: Milk-run strategy. minimize the overall distribution costs.