# DMRVR: Dynamic Milk-Run Vehicle Routing Solution Using Fog-Based Vehicular Ad Hoc Networks
## 2 Related Work
A good part of the integrated production and distribution scheduling models presented in the literature includes vehicle routing problems, which are concerned with the optimal route design to be assigned to a fleet of vehicles to perform a set of tasks (pickup, delivery, servicing, etc.). Due to the continuing developments in telecommunication, computing, and information technologies, dynamic vehicle routing and scheduling problems have received special research attention in the past years [37]. Refs. [38; 39; 40; 41] consider more general VRP problems. More specifically, Refs. [10; 11] consider VRP associated with scheduling vehicle problems in milk-run settings. One important point to consider in the solution of dynamic scheduling and routing problems, apart from the mathematical modeling itself, is to define real-time control policies and procedures, starting with the analysis of current disruptions such as machine breakdowns, material shortages, job cancellation, change in job priority, traffic delays, etc. [42], and followed by the establishment of corrective solutions in a dynamic way [43]. We selected eight related works [5; 10; 11; 12; 19; 20; 44; 45] focusing on VANETs in logistics and milk-run operations, which are discussed in this section. In [12], the authors analyzed the use of wireless vehicular networks to support the needs of multi-modal logistics in ports. The evaluation serves to test the practicability of an ITS that supports real data traffic. The solution uses dedicated short-range communications (DSRC) based wireless vehicular networks in a V2I architecture. The network coverage is approximately 1000 m, composed of four access points connected by an IP network and a central server. The vehicles used to transport goods within the seaport terminal represent the mobile nodes. The authors analyzed four scenarios, assuming that vehicles were moving at a maximum of 30 mph, and the loading and unloading cycle time was 20 min. In the most complicated scenario, the maximum communication delay was 2.7 s. For the authors, this indicates that the use of wireless vehicular networks in multimodal logistics solutions could be appropriate. Although the authors measured a low communication delay, they did not demonstrate that V2V communication could enhance the solution's effectiveness. M-DVRP [19] describes a solution for a multi-objective dynamic vehicle routing problem that uses vehicular networks (802.11p communication protocol). The authors used a time seed-based solution using particle swarm optimization (TS-PSO) to split the problem into smaller routing problems. Five variables for modeling the problem were defined: geographical position of orders, customers, service time, the expected time for attendance, and level of customer satisfaction. NS-2 and SUMO simulators were used for the M-DVRP evaluation and compared the particle swarm optimization efficiency against the genetic algorithm (GA). The comparison demonstrated that TS-PSO optimizes the identified problem's multi-objective function better than GA, using a smaller amount of vehicles. It is essential to highlight that the authors do not consider any delay due to traffic jams or customer problems. Moreover, the simulation did not assess the vehicular network performance. Another solution [11] examined the dynamic vehicle designation to perform milk-run operation pickups in an area subjected to traffic jams. The routing solution consists of a pickup vehicle fleet composed of the main and auxiliary vehicles. The latter is activated when the main vehicles cannot perform their pickups due to traffic congestions caused by accidents, heavy storms, etc. The solution regularly verifies if the vehicle is behind schedule. In case of delays, the solution transfers part of the tasks from the main to the auxiliary vehicle. The results showed that the dynamic formulation considerably improved the general service level compared with the static version.