Re-Route Package Pickup and Delivery Planning with Random Demands

Sawadsitang, Suttinee; Niyato, Dusit; Suankaewmanee, Kongrath; Tan, Puay Siew

doi:10.1109/vtcfall.2019.8891547

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…However, their main goal is on the container allocation, not vehicle routing. Recently, the re-route delivery planning system were proposed with two components, i.e., the stochastic optimisation and the re-route algorithm in [121]. New dependency constraints and capacity constraints were also proposed.…”

Section: Stochastic and Dynamic Vrpmentioning

confidence: 99%

“…Unlike the above works, the study in [121] focus on defining reasonable scenarios and realistic system model as well as developing problem formulations, i.e., an exact method.…”

Section: Stochastic and Dynamic Vrpmentioning

confidence: 99%

“…a study consider either traffic traveling time, customer demand, or package weight unit). Recently, I proposed a re-route stochastic package delivery planning dedicated for a groundbased delivery system [121]. In fact, this work can be extended to a multiple stage stochastic delivery based on the number of customers to improve the delivery plan.…”

Section: Chapter 6 Conclusion and Future Work 143mentioning

confidence: 99%

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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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Section: Stochastic and Dynamic Vrpmentioning

confidence: 99%

“…Unlike the above works, the study in [121] focus on defining reasonable scenarios and realistic system model as well as developing problem formulations, i.e., an exact method.…”

Section: Stochastic and Dynamic Vrpmentioning

confidence: 99%

Section: Chapter 6 Conclusion and Future Work 143mentioning

confidence: 99%

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Stochastic optimisation of unmanned aerial vehicle assisted package delivery planning

Sawadsitang¹

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Enough Waiting for the Couriers: Learning to Estimate Package Pick-up Arrival Time from Couriers’ Spatial-Temporal Behaviors

Wen

Lin

Wu³

et al. 2023

ACM Trans. Intell. Syst. Technol.

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In intelligent logistics systems, predicting the Estimated Time of Pick-up Arrival (ETPA) of packages is a crucial task, which aims to predict the courier’s arrival time to all the unpicked-up packages at any time. Accurate prediction of ETPA can help systems to alleviate customers’ waiting anxiety and improve their experience. We identify three main challenges of this problem: i) Unlike the travel time estimation problem in other fields like ride-hailing, the ETPA task is distinctively a multi-destination and path-free prediction problem. ii) An intuitive idea for solving ETPA is to predict the pick-up route then the time in two stages. However, it is difficult to accurately and efficiently predict couriers’ future routes in the route prediction step since their behaviors are affected by multiple complex factors. iii) Furthermore, in the time prediction step, the requirement for providing a courier’s all unpicked-up packages’ ETPA at once in real-time makes the problem even more challenging. To tackle the above challenges, we propose RankETPA, which integrates the route inference into the ETPA prediction. First, a learning-based pick-up route predictor is designed to learn the route-ranking strategies of couriers from their massive spatial-temporal behaviors. Then, a spatial-temporal attention-based arrival time predictor is designed for real-time ETPA inference via capturing the spatial-temporal correlations between the unpicked-up packages. Extensive experiments on two real-world datasets and a synthetic dataset demonstrate that RankETPA achieves significant performance improvement against the baseline models.

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Re-Route Package Pickup and Delivery Planning with Random Demands

Cited by 2 publications

References 9 publications

Stochastic optimisation of unmanned aerial vehicle assisted package delivery planning

Stochastic optimisation of unmanned aerial vehicle assisted package delivery planning

Enough Waiting for the Couriers: Learning to Estimate Package Pick-up Arrival Time from Couriers’ Spatial-Temporal Behaviors

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