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Citywide package deliveries via crowdshipping: minimizing the efforts from crowdsourcers

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Abstract

Most current crowdsourced logistics aim to minimize systems cost and maximize delivery capacity, but the efforts of crowdsourcers such as drivers are almost ignored. In the delivery process, drivers usually need to take long-distance detours in hitchhiking rides based package deliveries. In this paper, we propose an approach that integrates offline trajectory data mining and online route-and-schedule optimization in the hitchhiking ride scenario to find optimal delivery routes for packages and drivers. Specifically, we propose a two-phase framework for the delivery route planning and scheduling. In the first phase, the historical trajectory data are mined offline to build the package transport network. In the second phase, we model the delivery route planning and package-taxi matching as an integer linear programming problem and solve it with the Gurobi optimizer. After that, taxis are scheduled to deliver packages with optimal delivery paths via a newly designed scheduling strategy. We evaluate our approach with the real-world datasets; the results show that our proposed approach can complete citywide package deliveries with a high success rate and low extra efforts of taxi drivers.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61872050), in part by the Chongqing Basic and Frontier Research Program (cstc2018jcyjAX0551), Foundation of Chongqing Municipal Key Laboratory of Institutions of Higher Education ([2017]3), Foundation of Chongqing Development and Reform Commission (2017[1007]), and Foundation of Chongqing Three Gorges University. Sijing Cheng and Chao Chen contributed equally to this work. Wei Zhang is the corresponding authors for this paper.

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Author notes

  1. These authors contributed equally to this work.

Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Sijing Cheng, Chao Chen & Hongyu Huang

  2. Centre de Gestion Scientifigue-I3-UMR CNRS 9217, Mines ParisTech, PSL Research University, Paris, 75272, France

    Shenle Pan

  3. Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education, Chongqing Three Gorges University, Chongqing, 404100, China

    Wei Zhang & Yuming Feng

Authors
  1. Sijing Cheng
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  2. Chao Chen
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  3. Shenle Pan
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  4. Hongyu Huang
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  5. Wei Zhang
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  6. Yuming Feng
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Corresponding author

Correspondence to Wei Zhang.

Additional information

Sijing Cheng is pursuing his master degree at the College of Computer Science, Chongqing University, China. He received the BSc degree from the College of River and Ocean Engineering, Chongqing Jiaotong University, China in 2017. His research interests include taxi GPS trajectory data mining and last-mile delivery.

Chao Chen is a Full Professor at College of Computer Science, Chongqing University, China. He obtained his PhD degree from Pierre and Marie Curie University and TELECOM SudParis, France in 2014. He received the BSc and MSc degrees in control science and control engineering from Northwestern Polytechnical University, China in 2007 and 2010, respectively. Dr. Chen got published over 100 papers including 20 IEEE Transactions. His work on taxi trajectory data mining was featured by IEEE Spectrum three times, in 2011, 2016 and 2020, respectively. He was also the recipient of the Best Paper Runner-Up Award at MobiQuitous 2011. His research interests include pervasive computing, mobile computing, urban logistics, data mining from large-scale GPS trajectory data, and big data analytics for smart cities.

Shenle Pan is associate professor of sustainable logistics and supply chain management and co-director of the Physical Internet Chair at MINES ParisTech-PSL University in France. He received his Habilitation (HDR) degree from Université Polytechnique Hauts-de-France, PhD degree from MINES ParisTech, Engineer’s degree from Arts et Métiers ParisTech, France. Since 2010, he has been PI or co-PI for 15 research projects (H2020, FP7, ANR, etc.), published more than 60 papers in peer reviewed journals and conferences, and served as guest editor for four special issues on top academic journals. His research interests include Physical Internet, sustainability, smart logistics, freight transportation, inventory management, operations research and applications in logistics and supply chain.

Hongyu Huang (Member, IEEE) received the BS degree from Chongqing Normal University, China in 2002, the MS degree from Chongqing University, China in 2005, and the PhD degree from Shanghai Jiao Tong University, China in 2009. He is currently an Associate Professor with the College of Computer Science, Chongqing University, China. His research interests include mobile crowd-sensing, privacy preserving computing, and vehicular ad hoc networks.

Wei Zhang received the BS degree in computer engineering and application from Chongqing University, China in 1992, and the MS degree in computer engineering and application from Southwest University, China in 1999, and the PhD degree in computer software and theory from Chongqing University, China in 2003. In 2007, he was a Postdoctoral Research Fellow of control science and engineering with Chongqing University, China. He mainly engages in computational intelligence, information security, and data mining.

Yuming Feng received the BS and MS degrees in mathematics from Yunnan University, China in 2003 and 2006, respectively, and the PhD degree in applied mathematics from Southwest University, China in December 2016. From January 2012 to October 2012, he served as a Research Scholar with Udine University, Italy. From December 2014 to April 2015, he served as a Research Scholar with Texas A&M University at Qatar, Qatar. Since December 2018, he has been a Professor with Chongqing Three Gorges University, China. His current research interests include distributed optimization theory, neural networks, chaos control and synchronization, fuzzy algebra, and hyperalgebra.

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Cheng, S., Chen, C., Pan, S. et al. Citywide package deliveries via crowdshipping: minimizing the efforts from crowdsourcers. Front. Comput. Sci. 16, 165327 (2022). https://doi.org/10.1007/s11704-021-0568-5

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