Optimal Stochastic Package Delivery Planning with Deadline: A Cardinality Minimization in Routing

Sawadsitang, Suttinee; Jiang, Siwei; Niyato, Dusit; Wang, Ping

doi:10.1109/vtcfall.2017.8288239

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…Thus, the CPLEX solver may return a suboptimal solution instead of the optimal solution. In this regard, the suboptimal solution achieves much higher payment than that of the decomposition while the computational time of the decomposition may not significantly improve [25]. In summary, the decomposition enables the GADOP to handle larger number of scenarios.…”

Section: J Decompositionmentioning

confidence: 99%

Joint Ground and Aerial Package Delivery Services: A Stochastic Optimization Approach

Sawadsitang

Niyato

Tan

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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Unmanned aerial vehicles (UAVs), also known as drones, have emerged as a promising mode of fast, energyefficient, and cost-effective package delivery. A considerable number of works have studied different aspects of drone package delivery service by a supplier, one of which is delivery planning. However, existing works addressing the planning issues consider a simple case of perfect delivery without service interruption, e.g., due to accident which is common and realistic. Therefore, this paper introduces the joint ground and aerial delivery service optimization and planning (GADOP) framework. The framework explicitly incorporates uncertainty of drone package delivery, i.e., takeoff and breakdown conditions. The GADOP framework aims to minimize the total delivery cost given practical constraints, e.g., traveling distance limit. Specifically, we formulate the GADOP framework as a three-stage stochastic integer programming model. To deal with the high complexity issue of the problem, a decomposition method is adopted. Then, the performance of the GADOP framework is evaluated by using two data sets including Solomon benchmark suite and the real data from one of the Singapore logistics companies. The performance evaluation clearly shows that the GADOP framework can achieve significantly lower total payment than that of the baseline methods which do not take uncertainty into account.

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Section: J Decompositionmentioning

confidence: 99%

Joint Ground and Aerial Package Delivery Services: A Stochastic Optimization Approach

Sawadsitang

Niyato

Tan

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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“…Added parameters such as time that it takes the vehicle to pass through all garbage transport points and the time needed to move waste from TWD to the vehicle is called time windows [6,18,19]. Other parameter which is deadline [20][21][22] means time that takes a vehicle to complete garbage transport.…”

mentioning

confidence: 99%

Demand robust counterpart open capacitated vehicle routing problem time windows and deadline model of garbage transportation with LINGO 13.0

Puspita

Simanjuntak

Melati

et al. 2020

IJECE

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Demand robust counterpart-open capacitated vehicle routing problem with time windows and deadline (DRC-OCVRPtw,d) model formed and explained in this paper, is the model used to find the minimum distance and the time needed for vehicles to transport garbage in Sukarami Sub-District, Palembang that consists of the time it takes for the vehicle to pass through the route. Time needed to transport garbage to the vehicle is called time windows. Combination of the thoses times is called deadline. The farther the distance passed by vehicle and the more garbage transported, the longer the deadline is needed. This DRC-OCVRPtw,d model is completed by LINGO 13.0 to obtain the optimal route and time deadline for Sukarami Sub-District. The model shows that the improved model of open vehicle routing problem involving the robustness, time windows and deadline can achieve the optimal routes that enable driver to save operational time in picking up the garbage compared to similar problem not involving no-time windows and deadline stated in previous research.

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“…In this regard, the suboptimal solution achieves much higher payment than that of the decomposition while the computational time of the decomposition may not significantly improve [126]. In summary, the decomposition enables the GADOP to handle larger number of scenarios.…”

Section: Decompositionmentioning

confidence: 99%

Stochastic optimisation of unmanned aerial vehicle assisted package delivery planning

Sawadsitang¹

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I would like to express my sincere gratitude to my supervisor professor Dusit Niyato for his constant support and guidance throughout my Ph.D. study at Nanyang Technological University (NTU). I would like to thank him for his immense enthusiasm and patience. He always motivates me even when I am lost. I could not have imagined having a better adviser and mentor for my Ph.D. study.Moreover, I would like to give a sincere thank to my co-supervisors, Dr. Puay Siew Tan and associate professor Ping Wang for their constant guidance as well as for providing the necessary information and the direction.Beside my supervisor and my co-supervisors, I would like to thank all the collaborators and my lab-mates, who have shared research idea and knowledge with me.Especially, I would like to thank associate professor Sarana Nutanong for giving valuable comments on my research works. In addition, I am deeply indebted to Mr.Rakpong Kaewpuang and Dr. Thanomwong Poorisat who helped me settle down when I first moved to Singapore. They gave me practical and helpful guidance about research and how to live in this multi-culture country.

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Optimal Stochastic Package Delivery Planning with Deadline: A Cardinality Minimization in Routing

Cited by 4 publications

References 11 publications

Joint Ground and Aerial Package Delivery Services: A Stochastic Optimization Approach

Joint Ground and Aerial Package Delivery Services: A Stochastic Optimization Approach

Demand robust counterpart open capacitated vehicle routing problem time windows and deadline model of garbage transportation with LINGO 13.0

Stochastic optimisation of unmanned aerial vehicle assisted package delivery planning

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