Abstract
This paper studies the Bidding Generation Problem (BGP) by using bi-level programming. The carrier, as the upper level decision maker, tries to generate its optimal bidding strategy considering the possible reaction of the shipper; and the shipper, as the lower level decision maker, tries to select the proper carriers to provide the transportation services in order to minimize its total cost. Then after establishing the bi-level programming, a particle swarm optimization (PSO) is designed to solve the proposed model.
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Crainic TG, Gendreau M (2002) Freight exchanges and carrier operations: Issues, models, and tools.cole polytechnique
Kuyzu G, Akyol CG et al (2015) Bid price optimization for truckload carriers in simultaneous transportation procurement auctions. Trans Res Part B 73:34–58
Lee CG, Kwon RH, Ma Z (2007) A carriers optimal bid generation problem in combinatorial auctions for transportation procurement. Trans Res Part E Logist Trans Rev 43(2):173–191
Li Y, Chen H, Prins C (2016) Adaptive large neighborhood search for the pickup and delivery problem with time windows, profits, and reserved requests. Eur J Oper Res 252(1):27–38
Park S, Rothkopf MH (2005) Auctions with bidder-determined allowable combinations. Eur J Oper Res 161(2):399–415
Parkes DC (2000) Optimal auction design for agents with hard valuation problems. Springer, Berlin Heidelberg
Peke A, Rothkopf MH (2003) Combinatorial auction design. Manag Sci 49(11):1485–1503
Sheffi Y (2004) Combinatorial auctions in the procurement of transportation services. Interfaces 34(4):245–252
Triki C, Oprea S et al (2014) The stochastic bid generation problem in combinatorial transportation auctions. Eur J Oper Res 236(3):991–999
Ueasangkomsate P, Lohatepanont M (2012) Bidding strategies for carrier in combinatorial transportation auction. Int J Bus Res Manag 3(1):1–17
Yan F, Wang Y (2017) Modeling and solving the vehicle routing problem with multiple fuzzy time windows. In: International conference on management science and engineering management, pp 847–857
Zhang H, Cai S et al (2017) An efficient local search algorithm for the winner determination problem. J Heuristics 23(2):1–30
Acknowledgements
This research was supported by NSFC (Grant No. 71401020, Grant No. 71640013) and Chongqing Social Science Planning Project (Grant No. 2017YBGL154) and Innovative Education Fund for graduate students of Chongqing Jiaotong University(20160122) and Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No.KJ1600608) and Chongqing basic and frontier research project project (Grant No.cstc2016jcyjA-0442).
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Yan, F., Chen, K., Wu, K. (2019). Solving the Bidding Generation Problem in Transportation Services Procurement by Using Bi-level Programming. In: Xu, J., Cooke, F., Gen, M., Ahmed, S. (eds) Proceedings of the Twelfth International Conference on Management Science and Engineering Management. ICMSEM 2018. Lecture Notes on Multidisciplinary Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-93351-1_28
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DOI: https://doi.org/10.1007/978-3-319-93351-1_28
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