Integration of Drones in Last-Mile Delivery: The Vehicle Routing Problem with Drones

Author(s):  
Daniel Schermer
Kybernetes ◽  
2019 ◽  
Vol 49 (4) ◽  
pp. 1267-1284 ◽  
Author(s):  
Yandong He ◽  
Xu Wang ◽  
Fuli Zhou ◽  
Yun Lin

Purpose This paper aims to study the vehicle routing problem with dynamic customers considering dual service (including home delivery [HD] and customer pickup [CP]) in the last mile delivery in which three decisions have to be made: determine routes that lie along the HD points and CP facilities; optimize routes in real time, which mode is better between simultaneous dual service (SDS, HD points and CP facilities are served simultaneously by the same vehicle); and respective dual service (RDS, HD points and CP facilities are served by different vehicles)? Design/methodology/approach This paper establishes a mixed integer linear programing model for the dynamic vehicle routing problem considering simultaneous dual services (DVRP-SDS). To increase the practical usefulness and solve large instances, the authors designed a two-phase matheuristic including construction-improvement heuristics to solve the deterministic model and dynamic programing to adjust routes to dynamic customers. Findings The computational experiments show that the CP facilities offer greater flexibility for adjusting routes to dynamic customers and that the SDS delivery system outperforms the RDS delivery system in terms of cost and number of vehicles used. Practical implications The results provide managerial insights for express enterprises from the perspective of operation research to make decisions. Originality/value This paper is among the first papers to study the DVRP-SDS. Moreover, this paper guides the managers to select better delivery mode in the last mile delivery.


2019 ◽  
Vol 39 ◽  
pp. 314-324 ◽  
Author(s):  
Patchara Kitjacharoenchai ◽  
Seokcheon Lee

2020 ◽  
Vol 225 ◽  
pp. 107598 ◽  
Author(s):  
Patchara Kitjacharoenchai ◽  
Byung-Cheol Min ◽  
Seokcheon Lee

Author(s):  
Nahry Nahry ◽  
Talitha Ayu

The development of e-commerce business in Jakarta, Indonesia, in recent years has made the Last Mile Delivery (LMD) business sector develop rapidly. Increased demand for LMD makes the resulting kilometer trips even greater, resulting in negative externalities. On the other hand, logistics costs in Indonesia are only affected by vehicle operating costs and no external cost component. Optimization of LMD services that takes into account internal and external costs is needed to minimize the total cost of LMD and in reducing the impact of negative externalities. The purpose of this paper is to optimize the LMD distribution system on the Heterogeneous Fleet Vehicle Routing Problem with Time Window and External Costs (HFVRPTW-EC) models. The optimization is done by applying the HFVRPTW-EC model using data from one of the parcel delivery companies in Jakarta and then doing a simulation by forming several operational scenarios. The results show that the optimization of LMD has reduced internal and external costs by more than 50% compared to existing conditions. The detailed results show that, for the short-term program, a scenario with a one-tier distribution system and type of motorcycle vehicle can reduce total costs compared to existing conditions by 66.22% on peak day and 59.41% on off peak day. Whereas for long-term program optimization, scenarios with multiple tier distribution systems and types of motorized vehicles for drop mileage and pick up truck for stem mileage can reduce total costs by 69.23% on peak day and 60.24% on off peak day.


2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Yong Zhang ◽  
Lei Shi ◽  
Jing Chen ◽  
Xuefeng Li

The application of automated vehicles in logistics can efficiently reduce the cost of logistics and reduce the potential risks in the last mile. Considering the path restriction in the initial stage of the application of automated vehicles in logistics, the conventional model for a vehicle routing problem (VRP) is modified. Thus, the automated vehicle routing problem with time windows (AVRPTW) model considering path interruption is established. Additionally, an improved particle swarm optimisation (PSO) algorithm is designed to solve this problem. Finally, a case study is undertaken to test the validity of the model and the algorithm. Four automated vehicles are designated to execute all delivery tasks required by 25 stores. Capacities of all of the automated vehicles are almost fully utilised. It is of considerable significance for the promotion of automated vehicles in last-mile situations to develop such research into real problems arising in the initial period.


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