An Optimization Approach to Minimize the Expected Loss of Demand Considering Drone Failures in Drone Delivery Scheduling

Torabbeigi, Maryam; Lim, Gino J.; Ahmadian, Navid; Kim, Seon Jin

doi:10.1007/s10846-021-01370-w

Cited by 11 publications

(2 citation statements)

References 41 publications

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“…A study suggested solving the Capacitated Vehicle Routing Problem with Pickup and Alternative Delivery (CVRPPAD) through a hybrid approach of CLP [21] to pre-solve the model and MP with heuristic optimization creation inference. To generate an initial solution, another study used the SA heuristic algorithm [22] based on the petal algorithm to find path selection via a binary integer programming model using a local neighborhood search algorithm. The main objective of the model is to reduce the Expected Loss Of Demand (ELOD) by utilizing the Drone Delivery Schedule model with drone Failures (DDS-F) which is determine each drone task to Subset customers with trips sequence and compares the results with the Makespan problem model to find a better path, increase flight time, and payload.…”

Section: ) Optimizations Approaches In Uavmentioning

confidence: 99%

Routing Approaches used for Electrical Vehicles Navigation: A Survey

Hejres,

Mahjoub,

Hewahi

2024

IJCDS

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The growing demand for Electric Vehicles (EVs) depends on the high integration of this technology in many areas. Therefore, an important area of research raises interest in finding the optimal path-planning solution for electric vehicles. This paper discusses several reviews and analyzes some of the constraints of the techniques used to improve these systems. The paper discusses common models used in Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGV). This paper investigates the planning approaches that lead to finding the optimal route for the tour from the source to the destination. The review outlines the different models and systems of Unmanned Ariel Vehicles (UAV) and Unmanned Ground Vehicles (UGV). This paper can be considered as comprehensive survey research for EV routing techniques to assist researchers choose the appropriate approach for developing a system based on optimization techniques, machine learning, or Hybrid Approaches (HAs) techniques. Optimization techniques are mostly used to find the optimal path and achieve multi-objective goals. Some findings were approved as the best models inspired by natural biological as genetic algorithms, Particle Swarm Optimization, and Ant Colony Optimization. In addition to machine learning techniques as Reinforcement Learning. The hybrid approach techniques that combine optimization and machine learning techniques can increase robustness in solving routing problems.

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Section: ) Optimizations Approaches In Uavmentioning

confidence: 99%

Routing Approaches used for Electrical Vehicles Navigation: A Survey

Hejres,

Mahjoub,

Hewahi

2024

IJCDS

View full text Add to dashboard Cite

show abstract

“…A major disadvantage of these devices is their limited energy capacity, which adds a certain complexity to implementing these types of services, especially when serving remote sites [8]. Several operational research lines arise to cope with this limitation: (i) the optimal drone placement (ODP) problem, which guarantees the coverage of static or dynamic targets minimizing the required energy [9,10]; (ii) the problem of selecting the optimum charging station once the drones have finished their tasks [11]; and (iii) the route-planning problem and all its variants [12,13]. Considering that a fleet of drones can work in a coordinated manner to achieve a common goal, such as the aerial surveillance of an extended area, the problem of coordinating their individual paths accounting for their available energy can be envisioned as a team orienteering problem (TOP).…”

Section: Introductionmentioning

confidence: 99%

Combining Parallel Computing and Biased Randomization for Solving the Team Orienteering Problem in Real-Time

et al. 2021

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In smart cities, unmanned aerial vehicles and self-driving vehicles are gaining increased concern. These vehicles might utilize ultra-reliable telecommunication systems, Internet-based technologies, and navigation satellite services to locate their customers and other team vehicles to plan their routes. Furthermore, the team of vehicles should serve their customers by specified due date efficiently. Coordination between the vehicles might be needed to be accomplished in real-time in exceptional cases, such as after a traffic accident or extreme weather conditions. This paper presents the planning of vehicle routes as a team orienteering problem. In addition, an ‘agile’ optimization algorithm is presented to plan these routes for drones and other autonomous vehicles. This algorithm combines an extremely fast biased-randomized heuristic and a parallel computing approach.

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Scheduling Diagnostic Testing Kit Deliveries with the Mothership and Drone Routing Problem

Park

Mirjalili

Côté

et al. 2022

J Intell Robot Syst

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A critical component in the public health response to pandemics is the ability to determine the spread of diseases via diagnostic testing kits. Currently, diagnostic testing kits, treatments, and vaccines for the COVID-19 pandemic have been developed and are being distributed to communities worldwide, but the spread of the disease persists. In conjunction, a strong level of social distancing has been established as one of the most basic and reliable ways to mitigate disease spread. If home testing kits are safely and quickly delivered to a patient, this has the potential to significantly reduce human contact and reduce disease spread before, during, and after diagnosis. This paper proposes a diagnostic testing kit delivery scheduling approach using the Mothership and Drone Routing Problem (MDRP) with one truck and multiple drones. Due to the complexity of solving the MDRP, the problem is decomposed into 1) truck scheduling to carry the drones and 2) drone scheduling for actual delivery. The truck schedule (TS) is optimized first to minimize the total travel distance to cover patients. Then, the drone flight schedule is optimized to minimize the total delivery time. These two steps are repeated until it reaches a solution minimizing the total delivery time for all patients. Heuristic algorithms are developed to further improve the computational time of the proposed model. Experiments are made to show the benefits of the proposed approach compared to the commonly performed face-to-face diagnosis via the drive-through testing sites. The proposed solution method significantly reduced the computation time for solving the optimization model (less than 50 minutes) compared to the exact solution method that took more than 10 hours to reach a 20% optimality gap. A modified basic reproduction rate (i.e., m R 0 ) is used to compare the performance of the drone-based testing kit delivery method to the face-to-face diagnostic method in reducing disease spread. The results show that our proposed method ( m R 0 = 0.002) outperformed the face-to-face diagnostic method ( m R 0 = 0.0153) by reducing m R 0 by 7.5 times.

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An Optimization Approach to Minimize the Expected Loss of Demand Considering Drone Failures in Drone Delivery Scheduling

Cited by 11 publications

References 41 publications

Routing Approaches used for Electrical Vehicles Navigation: A Survey

Routing Approaches used for Electrical Vehicles Navigation: A Survey

Combining Parallel Computing and Biased Randomization for Solving the Team Orienteering Problem in Real-Time

Scheduling Diagnostic Testing Kit Deliveries with the Mothership and Drone Routing Problem

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