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Drone Delivery Scheduling Optimization Considering Payload-induced Battery Consumption Rates

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Abstract

This paper addresses the design of a parcel delivery system using drones, which includes the strategic planning of the system and operational planning for a given region. The amount of payload affects the battery consumption rate (BCR), which can cause a disruption in delivery of goods if the BCR was under-estimated in the planning stage or cause unnecessarily higher expenses if it was over-estimated. Hence, a reliable parcel delivery schedule using drones is proposed to consider the BCR as a function of payload in the operational planning optimization. A minimum set covering approach is used to model the strategic planning and a mixed integer linear programming problem (MILP) is used for operational planning. A variable preprocessing algorithm and primal and dual bound generation methods are developed to improve the computational time for solving the operational planning model. The optimal solution provides the least number of drones and their flight paths to deliver parcels while ensuring the safe return of the drones with respect to the battery charge level. Experimental data show that the BCR is a linear function of the payload amount. The results indicate the impact of including the BCR in drone scheduling, 3 out of 5 (60%) flight paths are not feasible if the BCR is not considered. The numerical results show that the sequence of visiting customers impacts the remaining charge.

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Correspondence to Gino J. Lim.

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Appendices

Appendix A

Table 11 Phantom 4 Pro+ specifications used in data collection

Appendix B

The data was collected when the battery’s SOC was between 95% and 15%. For example, it took 2.35 minutes for the battery charge to drop from 95% to 85% when the payload was 0.22 lb.

Table 12 Flight time data (in minutes) collected with Phantom 4 Pro+

Appendix C

The parameter M should be large enough that does not eliminate any feasible solution. This parameter appears in Constraints (16) and (17).

$$ \begin{array}{@{}rcl@{}} &&Constraints (16): \forall i\in \{C\cup D\} , \forall j\!\in\! C, i\!\neq\! j, \forall k\!\in\! K : \\ &&SOC_{j} \leq SOC_{i}-t_{ijk}(\alpha_{k} l_{ij} +\beta_{k})+M(1-x_{ijk}),\\ &&\to M\geq \frac{SOC_{j} - SOC_{i}+t_{ijk}(\alpha_{k} l_{ij} +\beta_{k})}{1-x_{ijk}}\\ & &if\ x_{ijk}=1,\quad then\ M\geq 0\\ &&if\ x_{ijk}=0, \quad then\\ &&\qquad \qquad M\geq \underset{i,j,k}{\max} {\{SOC_{j} - SOC_{i}+t_{ijk}(\alpha_{k} l_{ij} +\beta_{k})\}}.\\ &&\to M \geq \underset{j}{\max}{\{SOC_{j}\}}-\underset{i}{\min}{\{SOC_{i}\}}+\\ &&\qquad \underset{i,j,k}{\max}{\{t_{ijk}\}} \cdot \underset{i,j,k}{\max} {\{\alpha_{k} l_{ij} +\beta_{k}\}}\\ &&\to M \geq 100-\underset{k}{\min}{\{MinCh_{k}\}}+\\ &&\qquad \underset{i,j,k}{\max}{\{t_{ijk}\}} \cdot \underset{k}{\max} {\{\alpha_{k} MaxP_{k} +\beta_{k}\}} \end{array} $$

The same for the Constraint (17).

Appendix D

In the test case problem, there are 5 candidate locations to open depots. Table 13 shows whether a customer is within the covering range of each candidate location (value of 1) or not (value of 0).

Table 13 Coverage area by each candidate location in the test case problem

Appendix E

Table 14 Path reverse of optimal solution for the case study

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Torabbeigi, M., Lim, G.J. & Kim, S.J. Drone Delivery Scheduling Optimization Considering Payload-induced Battery Consumption Rates. J Intell Robot Syst 97, 471–487 (2020). https://doi.org/10.1007/s10846-019-01034-w

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