Routing and scheduling optimization for UAV assisted delivery system: A hybrid approach

Sajid, Mohammad; Mittal, Himanshu; Pare, S.; Prasad, Mukesh

doi:10.1016/j.asoc.2022.109225

Cited by 44 publications

(10 citation statements)

References 61 publications

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“…T 173-P130-R7-B6-L5-T14-C0.2 30 30 0 2184 optimal (10) T 173-P130-R6-B7-L5-T14-C0.2 15 15 0 2165 optimal (11) T 173-P150-R6-B9-L4-T14-C0.2 261 261 0 2095 optimal (12) T 173-P130-R5-B8-L5-T14-C0.1 134 134 0 2094 optimal (13) T 173-P170-R7-B6-L5-T14-C0.2 30 30 0 1655 optimal (14) T 173-P150-R6-B7-L5-T14-C0.2 166 166 0 1498 optimal (15) T 173-P170-R6-B7-L5-T14-C0.1 21 21 0 1450 optimal (16) T 173-P130-R6-B9-L4-T14-C0.3 61 61 0 1448 optimal (17) T 173-P130-R6-B9-L4-T14-C0.2 33 33 0 1381 optimal (18) T 173-P150-R9-B6-L4-T14-C0.1 22 22 0 1186 optimal (19) T 173-P150-R7-B6-L5-T14-C0.1 15 15 0 1111 optimal (20) T 173-P170-R6-B7-L5-T14-C0.2 52 52 0 1067 optimal (23) T 173-P170-R5-B8-L5-T14-C0.2 41 41 0 834 optimal (30) T 173-P150-R5-B8-L5-T14-C0.2 52 52 0 633 optimal (31) T 173-P170-R6-B9-L4-T14-C0.1 76 76 0 632 optimal (32) T 173-P130-R7-B6-L5-T14-C0.1 24 24 0 629 optimal (33) T 173-P170-R5-B8-L5-T14-C0.1 40 40 0 611 optimal (34)…”

Section: Gurobi Resultsmentioning

confidence: 99%

“…An initial session assignment is generated and submitted to a local search algorithm based on simulated annealing (SA) to improve its quality. SA [16] is a well-known metaheuristic that has been used to solve many different problems such as routing problems [17][18][19][20], symbolic regression [21], feature selection and/or hyperparameter tuning for classification algorithms [22][23][24], influence maximization on social networks [25], and many other problems [26,27]. Furthermore, SA has been implemented for solving many different scheduling problems related to machine scheduling problems [28], scheduling of relief teams in natural disasters [29], for the multiobjective job-shop problem [30], in scheduling tasks in cloud computing applications [31], among others.…”

Section: Heuristic Approachmentioning

confidence: 99%

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A Track-Based Conference Scheduling Problem

et al. 2022

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The scheduling of conferences is a challenging task that aims at creating successful conference programs that fulfill an often wide variety of requirements. In this work, we focus on the problem of generating conference programs that organize talks into tracks: subevents within the conference that are group-related talks. The main contributions of this work can be organized into three scopes: literature review, problem formulation and benchmarking, and heuristic approach. We provide a literature review of conference scheduling approaches that organizes these approaches within a timetabling problem taxonomy. We also describe the main characteristics of the conference scheduling approaches in the literature and propose a classification scheme for such works. To study the scheduling of conferences that include tracks, we introduce the definition of the track-based conference scheduling problem, a new problem that incorporates tracks in the conference program. We provide a binary integer linear programming model formulation for this problem. Our formulation considers the availability of presenters, chairs, and organizers, the avoidance of parallel tracks, and best paper sessions, among other classical constraints of conference scheduling problems. Additionally, based on our formulation, we propose a simple instance-generation procedure that we apply to generate a set of artificial instances. We complete our work by proposing a heuristic method based on the simulated annealing metaheuristic for solving the track-based conference scheduling problem. We compare the results obtained by our heuristic approach and the Gurobi solver regarding execution time and solution quality. The results show that the proposed heuristic method is a practical approach for tackling the problem as it obtains solutions in a fraction of the time required by Gurobi, while Gurobi is also unable to obtain an optimal solution in the defined time for a subset of the instances. Finally, from a general perspective, this work provides a new conference scheduling problem formulation that can be extended in the future to include other features common in conference programs. Moreover, thanks to the instance generation procedure, this formulation can be used as a benchmark for designing and comparing new solving approaches.

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Section: Gurobi Resultsmentioning

confidence: 99%

Section: Heuristic Approachmentioning

confidence: 99%

A Track-Based Conference Scheduling Problem

et al. 2022

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“…Based on the mathematical model presented in the literature [65], constraint (2) indicates that delivery can be made to any customer point only by one of the vehicles or UAVs, which is the same as the constraint proposed by Sajid [37] in the UAV-assisted delivery system, that every customer must be visited once using a single UAV only. In addition, each vehicle must depart from the distribution station and return to the station after completing all delivery tasks, and the UAVs are required to take off and land at the most once at the take-off and landing nodes, and the total demand of the customer points delivered by the UAVs should not exceed the maximum load weight of the vehicles.…”

Section: Binding Conditionsmentioning

confidence: 99%

“…These schemes increase the complexity of the delivery system and are unsuitable for exploring reasonable routes. Therefore, Sajid et al [37] proposed that UAV routing with time windows, multiple UAV control, multi-mode transportation and multi-objectives should be comprehensively considered. For fixed cost, Shavarani et al [38] restricted the number of UAVs assigned to each facility, and calculated fixed cost based on the establishment costs of the facilities and costs of drone purchases.…”

Section: Introductionmentioning

confidence: 99%

Vehicle and UAV Collaborative Delivery Path Optimization Model

Líu²,

Lai³

et al. 2022

Mathematics

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In the context of frequent public emergencies, emergency logistics distribution is particularly critical, and because of the unique advantages of unmanned aerial vehicles (UAVs), the model of coordinated delivery of vehicles and UAVs is gradually becoming an essential form of emergency logistics distribution. However, the omission of start-up costs prevents the cost of UAV battery replacement and the sorting, assembly and verification of packages from being factored into the total cost. Furthermore, most existing models focus on route optimization and delivery cost, which cannot fully reflect the customer’s desire for service satisfaction under emergency conditions. It is necessary to convert the unsatisfactory degree of time window into a penalty cost rather than a model constraint. Additionally, there is a lack of analysis on the mutual waiting cost between vehicles and UAVs when one of them is performing delivery tasks. Considering the effects of the time window, customer demand, maximum load capacity, and duration of distribution benefits, we propose a collaborative delivery path optimization model for vehicles and UAVs to minimize the total distribution cost. A genetic algorithm is used to obtain the model solution under the constraints of distribution subloops, distribution order, and take-off and landing nodes. To assess the efficacy of the vehicle and UAV collaborative delivery path optimization model, this paper employs a county-level district in Xi’an city as a pilot area for an emergency delivery. Compared with the vehicle-alone delivery model, the UAV-alone delivery model and vehicle-UAV collaborative delivery model, this model can significantly reduce the utilization of distribution vehicles while also significantly lowering the start-up cost, waiting cost and penalty cost. Thus, the model can effectively improve delivery timeliness and customer satisfaction. The total cost of this model is 39.2% less than that of the vehicle-alone delivery model and 16.5% less than that of the UAV-alone delivery model. Although its delivery cost is slightly higher than the vehicle-UAV collaborative delivery model, the reduction in the start-up cost and penalty cost decrease the overall cost of distribution by 11.8%. This suggests that to cut costs of all sizes and conserve half of the resources used by vehicles, employing the vehicle-UAV collaborative delivery model for emergency distribution is preferable. Moreover, the model integrating the start-up cost, penalty cost, waiting cost, etc., can more effectively express the requirements of timeliness for UAV delivery under emergency conditions.

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“…Unmanned aerial vehicles (UAVs) have received extensive attention from the engineering community due to their promising potential in military and civilian applications, such as disaster rescue [1,2], intelligent surveillance [3,4], smart agriculture [5][6][7][8], aerial photography [9,10], mapping [11], industry inspection [12,13] logistics [14][15][16], forest fire-fighting [16], and crop disease monitoring [17][18][19], etc.. Numerous types of UAVs have been fabricated and developed as research platforms, among which the quadcopters (also called quadrotors) are the most popular type because of their considerable merits including autonomous flight, easy construction, simple maintenance, low cost, onboard vision, vertical take-off and landing [20], hovering ability, and manoeuvrability.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Neuro-Fuzzy Inference System Based on Sliding Mode Control for Quadcopter Trajectory Tracking with the Presence of External Disturbance

Darwito¹,

Indayu²

2023

JISC

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Objective of this study is to develop a novel, effective, and robust Sliding Mode Control (SMC) method for quadcopters (also called quadrotors) based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for the purposes of enhancing trajectory tracking performance and realizing safe and reliable flight. In the paper, the ANFIS was combined with SMC technology to propose a scheme of adaptive robust controller, which is composed of three sub-controllers, x position controller, y position controller, and z position (altitude) controller. The proposed method can realize position tracking control of quadcopters in the presence of external disturbances. With the help of ANFIS, an adjustable gain rather than a fixed gain was established for the SMC controller, the optimal output could be attained based on a set of rules, and the position control gain was updated by ANFIS, enabling the SMC to adapt to environmental changes. Through modelling, simulation and comparison, experimental data verified that the proposed ANFIS-SMC controller outperformed conventional SMC controller in terms of convergence speed, robustness, accuracy, and stability with a maximum mean error of 0.125 meters in trajectory tracking. Research findings of this paper could contribute to the development of robust and responsive control strategies for Unmanned aerial vehicles (UAVs) trajectory tracking by providing valuable insights into the design of more effective and efficient control systems for UAVs, particularly in the context of dynamic environmental conditions.

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Routing and scheduling optimization for UAV assisted delivery system: A hybrid approach

Cited by 44 publications

References 61 publications

A Track-Based Conference Scheduling Problem

A Track-Based Conference Scheduling Problem

Vehicle and UAV Collaborative Delivery Path Optimization Model

Adaptive Neuro-Fuzzy Inference System Based on Sliding Mode Control for Quadcopter Trajectory Tracking with the Presence of External Disturbance

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