{"type":"ScholarlyArticle","title":"An Exact Single-Agent Task Selection Algorithm for the Crowdsourced Logistics","authors":[{"name":"Chung-Kyun Han","slug":"chung-kyun-han","given":"Chung-Kyun","family":"Han","affiliation":"School of Information Systems , Singapore Management University"},{"name":"Shih-Fen Cheng","slug":"shih-fen-cheng","given":"Shih-Fen","family":"Cheng","affiliation":"School of Information Systems , Singapore Management University"}],"abstract":"The trend of moving online in the retail industry has created great pressure for the logistics industry to catch up both in terms of volume and response time. On one hand, volume is fluctuating at greater magnitude, making peaks higher; on the other hand, customers are also expecting shorter response time. As a result, logistics service providers are pressured to expand and keep up with the demands. Expanding fleet capacity, however, is not sustainable as capacity built for the peak seasons would be mostly vacant during ordinary days. One promising solution is to engage crowdsourced workers, who are not employed full-time but would be willing to help with the deliveries if their schedules permit. The challenge, however, is to choose appropriate sets of tasks that would not cause too much disruption from their intended routes, while satisfying each delivery task's delivery time window requirement. In this paper, we propose a decision-support algorithm to select delivery tasks for a single crowdsourced worker that best fit his/her upcoming route both in terms of additional travel time and the time window requirements at all stops along his/her route, while at the same time satisfies tasks' delivery time windows. Our major contributions are in the formulation of the problem and the design of an efficient exact algorithm based on the branch-and-cut approach. The major innovation we introduce is the efficient generation of promising valid inequalities via our separation heuristics. In all numerical instances we study, our approach manages to reach optimality yet with much fewer computational resource requirement than the plain integer linear programming formulation. The greedy heuristic, while efficient in time, only achieves around 40-60% of the optimum in all cases. To illustrate how our solver could help in advancing the sustainability objective, we also quantify the reduction in the carbon footprint.","doi":"10.24963/ijcai.2020/600","url":"https://ijcai.org/proceedings/2020/600","pdf":"https://ijcai.org/proceedings/2020/0600.pdf","markdown":"https://ijcai.org/proceedings/2020/600.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/600","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Special track on AI for CompSust and Human well-being","track":"Special track on AI for CompSust and Human well-being","number":600,"pages":{"first":4352,"last":4358},"keywords":["Multidisciplinary Topics and Applications: Transportation","Planning and Scheduling: Planning Algorithms","Humans and AI: Human Computation and Crowdsourcing","Planning and Scheduling: Search in Planning and Scheduling"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/26728/file_26728.m3u8","length_sec":562,"provider":"hls"}],"references":[{"position":1,"raw":"[Arslan et al., 2019] Alp M Arslan, Niels Agatz, Leo Kroon, and Rob Zuidwijk. Crowdsourced delivery-a dynamic pickup and delivery problem with ad hoc drivers. Trans- portation Science, 53(1):222-235, 2019.","title":"Crowdsourced delivery-a dynamic pickup and delivery problem with ad hoc drivers","authors":"Niels Alp M Arslan, Leo Agatz, Rob Kroon, Zuidwijk","year":"2019","venue":"Transportation Science","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Behrend et al., 2019] Moritz Behrend, Frank Meisel, Kjetil Fagerholt, and Henrik Andersson. An exact solution method for the capacitated item-sharing and crowdship- ping problem. European Journal of Operational Research, 279(2):589-604, 2019.","title":"An exact solution method for the capacitated item-sharing and crowdshipping problem","authors":"Moritz Behrend, Frank Meisel, Kjetil Fagerholt, Henrik Andersson","year":"2019","venue":"European Journal of Operational Research","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chen et al., 2015] Cen Chen, Shih-Fen Cheng, Hoong Chuin Lau, and Archan Misra. Towards city- scale mobile crowdsourcing: Task recommendations under trajectory uncertainties. Proceedings of the 24th International Conference on Artificial Intelligence, pages 1113-1119, 2015.","title":"Towards cityscale mobile crowdsourcing: Task recommendations under trajectory uncertainties","authors":"Cen Chen, Shih-Fen Cheng, Hoong, Chuin Lau, Archan Misra","year":"2015","venue":"Proceedings of the 24th International Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cheng et al., 2017] Shih-Fen Cheng, Cen Chen, Thivya Kandappu, Hoong Chuin Lau, Archan Misra, Nikita Jaiman, Randy Tandriansyah, and Desmond Koh. Scal- able urban mobile crowdsourcing: Handling uncertainty in worker movement. ACM Transactions on Intelligent Sys- tems and Technology, 9(3):1-24, 2017.","title":"Scalable urban mobile crowdsourcing: Handling uncertainty in worker movement","authors":"Shih-Fen Cheng, Cen Chen, Thivya Kandappu, Chuin Hoong, Archan Lau, Nikita Misra, Randy Jaiman, Desmond Tandriansyah, Koh","year":"2017","venue":"ACM Transactions on Intelligent Systems and Technology","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Deng et al., 2016] Dingxiong Deng, Cyrus Shahabi, Ugur Demiryurek, and Linhong Zhu. Task selection in spatial crowdsourcing from worker's perspective. Geoinformat- ica, 20(3):529-568, 2016.","title":"Task selection in spatial crowdsourcing from worker's perspective","authors":"Deng","year":"2016","venue":"Geoinformatica","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Fischetti et al., 1998] Matteo Fischetti, Juan José Salazar González, and Paolo Toth. Solving the orienteering prob- lem through branch-and-cut. INFORMS Journal on Com- puting, 10:133-148, 1998.","title":"Solving the orienteering problem through branch-and-cut","authors":"Matteo Fischetti, Juan José Salazar González, Paolo Toth","year":"1998","venue":"INFORMS Journal on Computing","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Gansterer et al., 2017] Margaretha Gansterer, Murat Küc ¸üktepe, and Richard F. Hartl. The multi-vehicle profitable pickup and delivery problem. OR Spectrum, 39:303-319, 2017.","title":"The multi-vehicle profitable pickup and delivery problem","authors":"Margaretha Gansterer, Murat Küc, Richard F Hartl","year":"2017","venue":"OR Spectrum","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Guo et al., 2019] Xuezhen Guo, Yngrid Jaqueline Lujan Jaramillo, Jacqueline Bloemhof-Ruwaard, and G.D.H. Claassen. On integrating crowdsourced delivery in last- mile logistics: A simulation study to quantify its feasibil- ity. Journal of Cleaner Production, 241:118365, 2019.","title":"On integrating crowdsourced delivery in lastmile logistics: A simulation study to quantify its feasibility","authors":"Guo","year":"2019","venue":"Journal of Cleaner Production","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Holland et al., 2017] Chuck Holland, Jack Levis, Ran- ganath Nuggehalli, Bob Santilli, and Jeff Winters. UPS optimizes delivery routes. INFORMS Journal on Applied Analytics, 47(1):8-23, 2017.","title":"UPS optimizes delivery routes","authors":"Holland","year":"2017","venue":"INFORMS Journal on Applied Analytics","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Huang et al., 2018] Yixiao Huang, Martin Savelsbergh, and Lei Zhao. Designing logistics systems for home delivery in densely populated urban areas. Transportation Research Part B, 115:95-125, 2018.","title":"Designing logistics systems for home delivery in densely populated urban areas","authors":"Huang","year":"2018","venue":"Transportation Research Part B","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Kazemi and Shahabi, 2012] Leyla Kazemi and Cyrus Sha- habi. Geocrowd: Enabling query answering with spa- tial crowdsourcing. Proceedings of the 20th Interna- tional Conference on Advances in Geographic Informa- tion, pages 189-198, 2012.","title":"Geocrowd: Enabling query answering with spatial crowdsourcing","authors":"Shahabi Kazemi, Leyla Kazemi, Cyrus Shahabi","year":"2012","venue":"Proceedings of the 20th International Conference on Advances in Geographic Information","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Kim, 2015] Yongsung Kim. Libero: On-the-go crowdsourc- ing for package delivery. Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems, pages 121-126, 2015.","title":"Libero: On-the-go crowdsourcing for package delivery","authors":"Yongsung Kim, Kim","year":"2015","venue":"Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Ruland and Rodin, 1997] K.S. Ruland and E.Y. Rodin. The pickup and delivery problem: Faces and branch-and-cut algorithm. Computers & Mathematics with Applications, 33(12):1-13, 1997.","title":"The pickup and delivery problem: Faces and branch-and-cut algorithm","authors":"K S Ruland, E Y Rodin","year":"1997","venue":"Computers & Mathematics with Applications","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Stenger et al., 2013] Andreas Stenger, Daniele Vigo, Stef- fen Enz, and Michael Schwind. An adaptive variable neighborhood search algorithm for a vehicle routing prob- lem arising in small package shipping. Transportation Sci- ence, 47(1):64-80, 2013.","title":"An adaptive variable neighborhood search algorithm for a vehicle routing problem arising in small package shipping","authors":"Andreas Stenger, Daniele Vigo, Steffen Enz, Michael Schwind","year":"2013","venue":"Transportation Science","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Sun et al., 2018] Dezhi Sun, Ke Xu, Hao Cheng, Yuanyuan Zhang, Tianshu Song, Rui Liu, and Yi Xu. Online deliv- ery route recommendation in spatial crowdsourcing. World Wide Web, 2018.","title":"Online delivery route recommendation in spatial crowdsourcing","authors":"Dezhi Sun, Ke Xu, Hao Cheng, Yuanyuan Zhang, Tianshu Song, Rui Liu, Yi Xu","year":"2018","venue":"World Wide Web","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Tran et al., 2018] Luan Tran, Hien To, Liyue Fan, and Cyrus Shahabi. A real-time framework for task assignment in hyperlocal spatial crowdsourcing. ACM Transactions on Intelligent Systems and Technology, 9(3):37:1-37:26, 2018.","title":"A real-time framework for task assignment in hyperlocal spatial crowdsourcing","authors":"Luan Tran, Hien To, Liyue Fan, Cyrus Shahabi","year":"2018","venue":"ACM Transactions on Intelligent Systems and Technology","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Wang et al., 2016] Yuan Wang, Dongxiang Zhang, Qing Liu, Fumin Shen, and Loo Hay Lee. Towards enhancing the last-mile delivery: An effective crowd-tasking model with scalable solutions. Transportation Research Part E, 93:279-293, 2016.","title":"Towards enhancing the last-mile delivery: An effective crowd-tasking model with scalable solutions","authors":"Yuan Wang, Dongxiang Zhang, Qing Liu, Fumin Shen, Loo Hay, Lee","year":"2016","venue":"Transportation Research Part E","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Winkenbach et al., 2016] Matthias Winkenbach, Paul R. Kleindorfer, and Stefan Spinler. Enabling urban logis- tics services at la poste through multi-echelon location- routing. Transportation Science, 50(2):520-540, 2016.","title":"Enabling urban logistics services at la poste through multi-echelon locationrouting","authors":"Matthias Winkenbach, Paul R Kleindorfer, Stefan Spinler","year":"2016","venue":"Transportation Science","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}