A Hybrid Genetic Algorithm on Routing and Scheduling for Vehicle-Assisted Multi-Drone Parcel Delivery

Peng, Kai; Du, Jingxuan; Lu, Fang; Sun, Qianguo; Dong, Yan; Zhou, Pan; Hu, Menglan

doi:10.1109/access.2019.2910134

Cited by 97 publications

(37 citation statements)

References 39 publications

(44 reference statements)

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“…Constraint set (4) guarantees that each supplier, manufacturer, and customer node is arrived at and departed from by the same vehicle. Constraint set (5) implies that each trip starts from the depot. Constraint sets (6) and (7) indicate that a vehicle cannot travel to the dummy depot from a supplier node or a manufacturer node, respectively.…”

Section: B Model Formulationmentioning

confidence: 99%

“…Different supply chains have witnessed significant changes over the past decades, which can be attributed to several factors, including a high degree of flexibility, low-volume, low-cost production, and short delivery times [1]- [5]. Moreover, a diverse and dynamic nature of market demand has driven some of the innovative methods used by manufacturers to satisfy customer demands on time.…”

Section: Introductionmentioning

confidence: 99%

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An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”

Pasha

Dulebenets

Kavoosi³

et al. 2020

IEEE Access

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The "factory-in-a-box" concept involves assembling production modules (i.e., factories) in containers and transporting the containers to different customer locations. Such a concept could be highly effective during emergencies, when there is an urgent demand for products (e.g., the COVID-19 pandemic). The "factory-in-a-box" planning problem can be divided into two sub-problems. The first sub-problem deals with the assignment of raw materials to suppliers, sub-assembly decomposition, assignment of subassembly modules to manufacturers, and assignment of tasks to manufacturers. The second sub-problem focuses on the transport of sub-assembly modules between suppliers and manufacturers by assigning vehicles to locations, deciding the order of visits for suppliers, manufacturers, and customers, and selecting the appropriate routes within the transportation network. This study addresses the second sub-problem, which resembles the vehicle routing problem, by developing an optimization model and solution algorithms in order to optimize the "factory-in-a-box" supply chain. A mixed-integer linear programming model, which aims to minimize the total cost of the "factory-in-a-box" supply chain, is presented in this study. CPLEX is used to solve the model to the global optimality, while four metaheuristic algorithms, including the Evolutionary Algorithm, Variable Neighborhood Search, Tabu Search, and Simulated Annealing, are employed to solve the model for large-scale problem instances. A set of numerical experiments, conducted for a case study of "factory-in-a-box", demonstrate that the Evolutionary Algorithm outperforms the other metaheuristic algorithms developed for the model. Some managerial insights are outlined in the numerical experiments as well. INDEX TERMS Factory-in-a-box, metaheuristics, supply chains, urgent demand, vehicle routing problem.

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Section: B Model Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”

Pasha

Dulebenets

Kavoosi³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Therefore, it is necessary to design portable or tiny AI models dedicated for UAVs. For example, there are few studies on investigating low-complex algorithms for UAVs' navigation [228], pathfinding [229], and moving-targets' tracking [188]. The future IoE applications require lightweight AI algorithms to support autonomous intelligence with quick and precise responses.…”

Section: Light-weight Ai Algorithmsmentioning

confidence: 99%

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

Liu

Dai

Wang

et al. 2020

Computer Communications

161

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The recent advances in information and communication technology (ICT) have further extended Internet of Things (IoT) from the sole "things" aspect to the omnipotent role of "intelligent connection of things". Meanwhile, the concept of internet of everything (IoE) is presented as such an omnipotent extension of IoT. However, the IoE realization meets critical challenges including the restricted network coverage and the limited resource of existing network technologies. Recently, Unmanned Aerial Vehicles (UAVs) have attracted significant attentions attributed to their high mobility, low cost, and flexible deployment. Thus, UAVs may potentially overcome the challenges of IoE. This article presents a comprehensive survey on opportunities and challenges of UAV-enabled IoE. We first present three critical expectations of IoE: 1) scalability requiring a scalable network architecture with ubiquitous coverage, 2) intelligence requiring a global computing plane enabling intelligent things, 3) diversity requiring provisions of diverse applications. Thereafter, we review the enabling technologies to achieve these expectations and discuss four intrinsic constraints of IoE (i.e., coverage constraint, battery constraint, computing constraint, and security issues). We then present an overview of UAVs. We next discuss the opportunities brought by UAV to IoE. Additionally, we introduce a UAV-enabled IoE (Ue-IoE) solution by exploiting UAVs's mobility, in which we show that Ue-IoE can greatly enhance the scalability, intelligence and diversity of IoE. Finally, we outline the future directions in Ue-IoE.

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“…In order to overcome it, there appeared a variety of meta-heuristic algorithms with the advantage of jumping out of the local optimal and seek the global optimal, such as genetic algorithm, simulated annealing algorithm, and tabu search algorithm [26,27]. Among meta-heuristic algorithms, the genetic algorithm is popularly applied for solving VRP, including VRP with UAVs [28,29]. However, the mode of UAV delivery in these studies is that the truck is just used as a mobile base, which is very different with ours.…”

Section: Algorithms For Vrpmentioning

confidence: 99%

Impact of UAV Delivery on Sustainability and Costs under Traffic Restrictions

Yang

Huang

2020

Mathematical Problems in Engineering

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Compared with traditional vehicles delivery, unmanned aerial vehicle (UAV) delivery can reduce energy consumption and greenhouse gas emissions, which benefits environmental sustainability. Besides, UAVs can overcome traffic restrictions, which are the big obstacle in parcel delivery. In reality, there are two kinds of most popular traffic restrictions, vehicle-type restriction, and half-side traffic. We propose a mixed-integer (0-1 linear) green routing model with these two kinds of traffic restrictions for UAVs to exploit the environmental aspects of the use of UAVs in logistics. A genetic algorithm is proposed to efficiently solve the complex routing problem, and an experimental analysis is made to illustrate and validate our model and the algorithm. We found that, under both these two traffic restrictions, UAV delivery can accomplish deliveries that cannot be carried out or are carried out at much higher costs by vehicles only and can always effectively save costs and cut CO2 emissions, which is environmentally friendly. Furthermore, UAV delivery saves more cost and cuts more CO2 emission under the first kind of traffic restriction than that under the second.

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A Hybrid Genetic Algorithm on Routing and Scheduling for Vehicle-Assisted Multi-Drone Parcel Delivery

Cited by 97 publications

References 39 publications

An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”

An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

Impact of UAV Delivery on Sustainability and Costs under Traffic Restrictions

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