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The selective pickup and delivery problem: Formulation and a memetic algorithm

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  • Ting, Chuan-Kang
  • Liao, Xin-Lan

Abstract

The pickup and delivery problem addresses the real-world issues in logistic industry and establishes an important category of vehicle routing problems. The problem is to find the shortest route to collect and distribute commodities under the assumption that the total supply and the total demand are in equilibrium. This study presents a novel problem formulation, called the selective pickup and delivery problem (SPDP), by relaxing the constraint that all pickup nodes must be visited. Specifically, the SPDP aims to find the shortest route that can supply delivery nodes with required commodities from some pickup nodes. This problem can substantially reduce the transportation cost and fits real-world logistic scenarios. Furthermore, this study proves that the SPDP is NP-hard and proposes a memetic algorithm (MA) based on genetic algorithm and local search to resolve the problem. A novel representation of candidate solutions is designed for the selection of pickup nodes. The related operators are also devised for the MA; in particular, it adapts the 2-opt operator to the sub-routes of the SPDP for enhancement of visiting order. The experimental results on several SPDP instances validate that the proposed MA can significantly outperform genetic algorithm and tabu search in terms of solution quality and convergence speed. In addition, the reduced route lengths on the test instances and the real-world application to rental bikes distribution demonstrate the benefit of the SPDP in logistics.

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  • Ting, Chuan-Kang & Liao, Xin-Lan, 2013. "The selective pickup and delivery problem: Formulation and a memetic algorithm," International Journal of Production Economics, Elsevier, vol. 141(1), pages 199-211.
    • Handle: RePEc:eee:proeco:v:141:y:2013:i:1:p:199-211
    DOI: 10.1016/j.ijpe.2012.06.009
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    2. Ramaekers Katrien & Caris An & Maes Tabitha & Janssens Gerrit K., 2015. "Pickup and Delivery Selection: Problem Formulation and Extension to Problem Variants," Information Technology and Management Science, Sciendo, vol. 18(1), pages 84-90, December.
    3. Guo, Zhaoxia & Shi, Leyuan & Chen, Longchao & Liang, Yong, 2017. "A harmony search-based memetic optimization model for integrated production and transportation scheduling in MTO manufacturing," Omega, Elsevier, vol. 66(PB), pages 327-343.
    4. Jeong, Ho Young & Song, Byung Duk & Lee, Seokcheon, 2019. "Truck-drone hybrid delivery routing: Payload-energy dependency and No-Fly zones," International Journal of Production Economics, Elsevier, vol. 214(C), pages 220-233.
    5. Margaretha Gansterer & Murat Küçüktepe & Richard F. Hartl, 2017. "The multi-vehicle profitable pickup and delivery problem," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 39(1), pages 303-319, January.
    6. Zhang, Ruiyou & Zhao, Haishu & Moon, Ilkyeong, 2018. "Range-based truck-state transition modeling method for foldable container drayage services," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 118(C), pages 225-239.
    7. Zhen, Lu & Wu, Yiwei & Wang, Shuaian & Yi, Wen, 2021. "Crowdsourcing mode evaluation for parcel delivery service platforms," International Journal of Production Economics, Elsevier, vol. 235(C).
    8. Qiu, Xiaoqiu & Feuerriegel, Stefan & Neumann, Dirk, 2017. "Making the most of fleets: A profit-maximizing multi-vehicle pickup and delivery selection problem," European Journal of Operational Research, Elsevier, vol. 259(1), pages 155-168.
    9. Yu, Junfang & Dong, Yuanyuan, 2013. "Maximizing profit for vehicle routing under time and weight constraints," International Journal of Production Economics, Elsevier, vol. 145(2), pages 573-583.
    10. Ho, Sin C. & Szeto, W.Y., 2017. "A hybrid large neighborhood search for the static multi-vehicle bike-repositioning problem," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 340-363.
    11. Zhang, Ruiyou & Lu, Jye-Chyi & Wang, Dingwei, 2014. "Container drayage problem with flexible orders and its near real-time solution strategies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 61(C), pages 235-251.
    12. Zhang, Jie & Meng, Meng & Wong, Yiik Diew & Ieromonachou, Petros & Wang, David Z.W., 2021. "A data-driven dynamic repositioning model in bicycle-sharing systems," International Journal of Production Economics, Elsevier, vol. 231(C).
    13. Szeto, W.Y. & Shui, C.S., 2018. "Exact loading and unloading strategies for the static multi-vehicle bike repositioning problem," Transportation Research Part B: Methodological, Elsevier, vol. 109(C), pages 176-211.
    14. Z. Al Chami & H. Manier & M.-A. Manier, 2019. "A lexicographic approach for the bi-objective selective pickup and delivery problem with time windows and paired demands," Annals of Operations Research, Springer, vol. 273(1), pages 237-255, February.
    15. Julio C. Londoño & Rafael D. Tordecilla & Leandro do C. Martins & Angel A. Juan, 2021. "A biased-randomized iterated local search for the vehicle routing problem with optional backhauls," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 387-416, July.
    16. Iassinovskaia, Galina & Limbourg, Sabine & Riane, Fouad, 2017. "The inventory-routing problem of returnable transport items with time windows and simultaneous pickup and delivery in closed-loop supply chains," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 570-582.
    17. Ho, Sin C. & Szeto, W.Y., 2014. "Solving a static repositioning problem in bike-sharing systems using iterated tabu search," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 69(C), pages 180-198.

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