TY - GEN
T1 - Congestion-aware Bi-modal Delivery Systems Utilizing Drones
AU - Beliaev, Mark
AU - Mehr, Negar
AU - Pedarsani, Ramtin
N1 - Publisher Copyright:
© 2022 EUCA.
PY - 2022
Y1 - 2022
N2 - Bi-modal delivery systems are a promising solution to the challenges posed by the increasing demand of e-commerce. Due to the potential benefit drones can have on logistics networks such as delivery systems, some countries have taken steps towards integrating drones into their airspace. In this paper we aim to quantify this potential by developing a mathematical model for a Bi-modal delivery system composed of trucks and drones. We propose an optimization formulation that can be efficiently solved in order to design socially-optimal routing and allocation policies. We incorporate both societal cost in terms of road congestion and parcel delivery latency in our formulation. Our model is able to quantify the effect drones have on mitigating road congestion, and can solve for the path routing needed to minimize the chosen objective. To accurately capture the effect of stopping trucks on road latency, we model it using SUMO by simulating roads shared by trucks and cars. Based on this, we show that the proposed framework is computationally feasible to scale due to its reliance on convex quadratic optimization techniques.
AB - Bi-modal delivery systems are a promising solution to the challenges posed by the increasing demand of e-commerce. Due to the potential benefit drones can have on logistics networks such as delivery systems, some countries have taken steps towards integrating drones into their airspace. In this paper we aim to quantify this potential by developing a mathematical model for a Bi-modal delivery system composed of trucks and drones. We propose an optimization formulation that can be efficiently solved in order to design socially-optimal routing and allocation policies. We incorporate both societal cost in terms of road congestion and parcel delivery latency in our formulation. Our model is able to quantify the effect drones have on mitigating road congestion, and can solve for the path routing needed to minimize the chosen objective. To accurately capture the effect of stopping trucks on road latency, we model it using SUMO by simulating roads shared by trucks and cars. Based on this, we show that the proposed framework is computationally feasible to scale due to its reliance on convex quadratic optimization techniques.
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U2 - 10.23919/ECC55457.2022.9838052
DO - 10.23919/ECC55457.2022.9838052
M3 - Conference contribution
AN - SCOPUS:85136617339
T3 - 2022 European Control Conference, ECC 2022
SP - 1944
EP - 1951
BT - 2022 European Control Conference, ECC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 European Control Conference, ECC 2022
Y2 - 12 July 2022 through 15 July 2022
ER -