TY - JOUR ID - 3406 TI - A Hybrid Truck-Drone Routing Problem Considering Deprivation Cost in the Post-Disaster Situation JO - Journal of Quality Engineering and Production Optimization JA - JQEPO LA - en SN - AU - Khalaj Rahimi, Sanaz AU - Rahmani, Donya AD - Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran Y1 - 2021 PY - 2021 VL - 6 IS - 1 SP - 233 EP - 256 KW - Deprivation Cost KW - Disaster Response KW - Humanitarian Logistics KW - Hybrid Truck–Drone Routing Problem KW - Unmanned Aerial Vehicles (UAVs) DO - 10.22070/jqepo.2021.14935.1201 N2 - Natural disasters and their destructive effects on life and property are the most important issues these days.  Implementation time of the relief operation and limitations associated with the ground infrastructure in disaster situations is the main priority of humanitarian logistics. So the necessity of using Unmanned Aerial Vehicles (UAVs) to reduce the service time is evident. On the Other hand, considering the deprivation cost function as an appropriate objective function can improve existing problems in disaster situations. This paper proposes a hybrid vehicle-drone routing model in the post-disaster phase. Multi trucks and multi drones with complementary capabilities are applied. Trucks are restricted to travel to the nodes with undamaged road networks, known as LD nodes. Drones can fly from the trucks to the nodes with damaged roads, known as DN nodes. They are used multiple times from each truck at LD nodes. The objective is to minimize the deprivation cost as a function of the deprivation time of both types of nodes. So by minimizing the deprivation cost, the vehicles' arrival times are minimized, and better routes are selected for vehicles. The effect of the population rate of the affected area on the route determination also is considered by the deprivation cost function. Finally, the validation of the proposed model is tested by solving it in GAMS software. Some examples are solved to show how applying the deprivation cost can improve the selecting routes by this model. UR - https://jqepo.shahed.ac.ir/article_3406.html L1 - https://jqepo.shahed.ac.ir/article_3406_0385b43bb403a33809e0f897aa67b5fb.pdf ER -