Search

The truck-drone hybrid delivery system uses a truck as a station for drones in addition to its delivery function and is getting attention because the strengths of these individual vehicles can be selectively and synergistically exploited. In this study, we extended the previous vehicle routing models to the hybrid delivery systems by taking into account two important practical issues: the effect of parcel weight on drone energy consumption and restricted flying areas. The flight range of the drones is heavily susceptible to the loaded weight due to the limited battery life. Drones are also not allowed to fly over sensitive facilities regulated by the Federal Aviation Administration (FAA) or temporarily in certain areas due to weather-related conditions. We developed a mathematical model that incorporates these issues and propose a two-phase constructive and search heuristic algorithm to provide computational efficiency of the real-world cases problems. The result of the numerical study demonstrates the effectiveness and efficiency of the proposed algorithm.

In recent years, drone routing and scheduling has become a highly active area of research. This research introduces a new routing model that considers a synchronized truck-drone operation by allowing multiple drones to fly from a truck, serve one or multiple customers, and return to the same truck for a battery swap and package retrieval. The model addresses two levels (echelons) of delivery: primary truck routing from the main depot to serve assigned customers and secondary drone routing from the truck, which behaves like a moveable intermediate depot to serve other sets of customers. The model takes into account both trucks' and drones’ capacities with the objective of finding optimal routes of both trucks and drones that minimizes the total arrival time of both trucks and drones at the depot after completing the deliveries. The problem can be solved by formulated mixed integer programming (MIP) for the small-size problem, and two efficient heuristic algorithms are designed to solve the large-size problems: Drone Truck Route Construction (DTRC) and Large Neighborhood Search (LNS). Numeric results from the experiment compare the performance of both heuristics against the MIP method in small/medium-size instances from the literature. A sensitivity analysis is conducted to show the delivery time improvement of the proposed model over the previous truck-drone routing models.

The integration of environmental practices in a supply chain has been study for the past few decades. However, most of the work relies on centralized decisions made by one player. Few papers address the complex dynamics of environmental decentralized supply chains and how these dynamics can affect environmental and economic outcomes. To study this problem, we consider a supply chain with a manufacturer and two different suppliers: a recycled-material and a raw-material supplier. The players make individual inventory decisions to satisfy demand and reduce cost. Further, this supply chain encompasses stochastic elements such as in demands, returns, and collection leadtimes. These decentralized decisions and random factors can cause underperforming results; therefore, new inventory models and technologies are needed to help companies increase coordination within these systems. We model the implementation of Radio Frequency Identification (RFID) in the supply chain to determine if real-time inventory monitoring and information sharing can help the system attain higher environmental benefits (more returns) and higher economic benefits (less cost). We study two scenarios through a simulation-based analysis: No RFID and RFID. Numerical studies show that environmental benefits are significantly increased with the attainment of more returns. However, although economic benefits are realized, they are less significant than the environmental benefits. Further regression and sensitivity analyses on the cost performance measures reveal that economic benefits depend on several drivers inside the system. We present managerial insights that illustrate what configurations within this complex system can lead to the achieving of environmental as well as economic benefits.