Drone-Truck Cooperated Delivery under Time Varying Dynamics

Rapid technological developments in autonomous unmanned aerial vehicles (or drones) could soon lead to their large-scale implementation in the last-mile delivery of products. However, drones have a number of problems such as limited energy budget, limited carrying capacity, etc. On the other hand, trucks have a larger carrying capacity, but they cannot reach all the places easily. Intriguingly, last-mile delivery cooperation between drones and trucks can synergistically improve delivery efficiency. In this paper, we present a drone-truck co-operated delivery framework under time-varying dynamics. Our framework minimizes the total delivery time while considering low energy consumption as the secondary objective. The empirical results support our claim and show that our algorithm can help to complete the deliveries time efficiently and saves energy

Developing a Vans-and-Drones System for Last-Mile Delivery

The e-commerce industry is experiencing rapid growth, and growing customer expectations and demand challenges the industry to find more cost-efficient ways of performing the last-mile deliveries. Drones have in recent years been a hot topic, and with high versatility and several application areas it may be the answer to the challenge. In this project a Vans-and-Drones System for Last-Mile Delivery have been developed considering effective task allocation and route scheduling. A literature review is presented on the topic of drone technology and application areas, especially emphasizing utilization of drones in logistic operations and routing problems. A mathematical model for the Vehicle Routing Problem with Drones is derived based on the classical Capacitated Vehicle Routing Problem, and the formulation is modeled in Jupyter Notebook with Python programming language and solved with CPLEX solver. A case study is carried out to examine the effects of integrating drones into the delivery system for a vaccine distribution scenario in a sparsely populated area, Ofoten region, considering vehicle employment cost, delivery time and emission impact. Results show that the proposed vans-and-drones system outperforms a truck-only delivery system for this purpose

Congestion based Truck Drone intermodal delivery optimization

Commerce companies have experienced a rise in the number of parcels that need to be delivered each day. The goal of this study is to provide a decision-making procedure to assist carriers in taking a more significant role in selecting cost and risk-efficient truck-drone intermodal delivery routing plan. The congestion-based model is developed to select the method of parcel delivery utilizing a truck and a drone for optimizing cost and time. A study also has been conducted to compare drone-only and truck-only delivery routing plan. The proposed A* Heuristic algorithm and the OSRM application generate the travel path for drone and a truck along with the time of travel. Case studies have been conducted by varying the weight provided to cost and risk variable, studies indicate that there is a significant change in drone delivery travel time and cost with increase of cost weightage

Otimização da entrega de encomendas por drones

A tecnologia está em constante evolução. Um dos frutos dessa evolução foi a criação de drones. Com a evolução destes, houve uma perceção que os drones teriam aplicação no ramo da logística, pelo menos para encomendas de pequeno a pequenomédio porte (telemóveis, por exemplo). A sua possível aplicabilidade deriva das suas vantagens económicas e ambientais, devido ao seu consumo reduzido de energia. Isto quando comparado com um veículo terrestre comum, como um camião. É por essa razão que este trabalho se foca em fazer um modelo para solucionar o problema “Travelling Salesman Problem with a Drone”, TSP-D, com foco na redução do impacto ambiental. Este foco ambiental foi implementado no modelo através da sua função objetivo ser a minimização das emissões de 2 da rota. Para o camião foram consideradas as emissões de 2 derivadas da queima de combustível. No caso do drone foram consideradas as emissões de 2 derivadas da produção de energia elétrica em Portugal. O trabalho consistiu em usar o modelo de (Jeong, Song, and Lee 2019) como base e alterá-lo. As alterações feitas foram: a correção de erros presentes no modelo, a remoção de restrições e parâmetros/variáveis relativas às zonas de voo proibidas (que o modelo considerava) e acrescentar novas restrições e parâmetros/variáveis de vertente ambiental. A realização deste trabalho permitiu chegar a duas conclusões principais. A primeira é que o drone é uma solução mais benéfica para o ambiente, em comparação com o camião, mas as suas desvantagens (limite de carga) impedem que este seja usado em vez do camião. A sua vantagem ambiental é vista quer através da bibliografia e quer através da rota exemplo apresentada. Nesta é possível ver que o drone emite muito menos gramas de 2 por km que o camião, 0,336 2/ do drone contra os 200 2/ emitidos pelo camião. A segunda conclusão foi que o fator de maior influência do tempo de processamento do modelo é o número de clientes a servir. Com o aumento do número de clientes viu-se um aumento exponencial do tempo de processamento, principalmente a partir de rotas com cinco ou mais clientes.The technology is always evolving. One of the results of this evolution is the creation of drones. With the continuous improvements to this technology, it had become clear that the drones had a role to play in the logistics business, at least for distribution of small to small-medium size packages (mobile phones, for example). Its possible applicability is due to its advantages, both economical and environmentally, due to low resource requirement to operate. That is when compared to a common land vehicle, such as a truck. This makes it a very good candidate to make a routing “Travelling Salesman Problem with a Drone” model, TSP-D, that focuses in reducing environmental pollution, derived from the distribution of goods process. This environmental focus is implemented into the model through its objective function of minimizing the 2 emission of the route. For the truck, it was considered the emissions of 2 derived from the burning of fuel. In case of the drone, the emissions considered where derived from the emission of 2 resulting from the electricity production in Portugal. This work consisted on using the model from (Jeong et al. 2019) as a basis and modifying it. The changes made were: correction of errors present in the base model, removing restrictions and parameters/variables related to the no-fly zones (which the original model delt with) and add new restrictions and parameters/variables related to the environmental component. This paper allowed to make two primary conclusions. The first one is that the usage of a drone, instead of a truck, is more environmentally friendly. But we also see that its drawback (cargo limit) stops it from taking over the place of the truck. Its environmental advantage is seen in the bibliographic research and in the route presented as an example. In this example, we see that the drone generates much less pollution than the truck, 0,336 2/ generated by the drone versus the 200 2/ emitted by the truck. The second one is that the process time is primarily influenced by the number of customers served. The results show that the process time increases exponentially with the increase of the number of customers. This can be seen more clearly in the routes with 5 or more customers