An optimization approach for winner determination problem considering transportation cost discounts

This study proposes a mixed-integer nonconvex programming (MINP) model for the winner determination problem (WDP) considering two discount functions in a combinatorial auction to save shipper’s transportation cost. For the WDP, the shipper allows carriers to submit bids for a bundle of lanes. Then t...

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Vydáno v:Journal of global optimization Ročník 80; číslo 3; s. 711 - 728
Hlavní autoři: Yang, Fang, Huang, Yao-Huei
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.07.2021
Springer
Springer Nature B.V
Témata:
Combinatorial analysis
Computer Science
Computing time
Cost control
Discounts
Linear programming
Linearization
Mathematics
Mathematics and Statistics
Mixed integer
Operating costs
Operations Research/Decision Theory
Optimization
Real Functions
Transportation
Mixed-integer nonconvex programming
Big-M constraints
Winner determination problem
Discount function
Branch-and-bound trees
ISSN:0925-5001, 1573-2916
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Shrnutí:This study proposes a mixed-integer nonconvex programming (MINP) model for the winner determination problem (WDP) considering two discount functions in a combinatorial auction to save shipper’s transportation cost. For the WDP, the shipper allows carriers to submit bids for a bundle of lanes. Then the winning carries are selected by solving the WDP. Specifically, this study considers the shipment distance-based and volume-based discounts for transportation cost, simultaneously. The state-of-the-art linearization technique is available to convert the MINP model into a mixed-integer linear program (MILP) to obtain a global optimum, but the solution time becomes inefficient when the problem size becomes large. To find efficient and effective linearization techniques for large-scale WDP, this study (1) proposes a novel WDP model with discount policies, (2) utilizes superior encoding formulation to avoid the unbalanced branch-and-bound trees in solving MILP, and (3) reduces big-M constraints to speed up the solving time. The proposed method leads to significant savings in computational efforts. Numerical experiments with real-world-sized truckload service procurement problems are solved by the proposed method and further confirmed the drastic reduction in computational time for solving the large-size WDP.
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ISSN:0925-5001
1573-2916
DOI:10.1007/s10898-021-01035-w