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.

Reduce Transportation Costs Using the Milk-run System and Dynamo Stages in the Vehicle Manufacturing Industry

“…Based on the problems shown in Figure 1, several other studies have also made improvements to the milk-run system (Urru et al, 2018). Based on research (Ranjbaran et al, 2020;Adriano et al, 2020) Milk-run can increase company productivity by reducing costs. According to Tellini et al (2019) and Biswas & Das (2020), the milk-run system can overcome loading and unloading times, so that an effective and efficient operational time is obtained.…”

Simplified Swarm Optimization for the Heterogeneous Fleet Vehicle Routing Problem with Time-Varying Continuous Speed Function

“…Wang, Assogba, Fan, Xu, Liu, and Wang [14] presented a bi-objective model which focused on freight distribution to minimize total carbon emission and operating cost. Adriano, Montez, Novaes, and Wangham [12] also proposed a dynamic model to simulate the milk-run tours with time windows, using the auxiliary vehicle to deal with traffic jams. Liu et al [33] proposed TDVRPTW to minimize the sum of the fixed costs of the vehicle used, as well as the costs of drivers, fuel consumption, and carbon emissions.…”

“…Logistics need to find a balance between costs and quality of service. This route construction problem has been encountered by the authors in several contexts: e.g., cold chain logistics [10], fresh logistics [11], milk run [12], automated guided vehicle (AGV) systems [13], freight distribution [14], JIT production [15], B2C e-commerce [16], inter-vehicle communication [17], vaccine distribution [18], and so on. Nonetheless, there are few cases involving optimization of vehicle type selection and route distribution under the minimizing of total cost based on a continuous time-varying speed model which are closer to the actual distribution situation.…”

Using genetic algorithm for fleet assignment of small-scale LNG supply chain

“…This paper aims to develop a ship assignment or deployment from a particular set of ships in a milkrun operation using a genetic algorithm with the objective function to minimize logistics costs. The milk-run operation is a logistic strategy where vehicles (ships) depart from one location to another followong a predetermined route to collect/deliver good to be distributed [16]. The logistics costs considered in this paper consist of transportation and infrastructures costs both for capital expenditures and operating expenditures.…”