Solving the winner determination problem with discounted bids in transportation auctions

Abstract

Continuing advances in modern technologies have transformed the procedure of transportation procurement through auctions in supply chain management (SCM). This study examines the online combinatorial auction (CA), which serves customers placed at the nodes of a transportation network, with particular consideration given to carbon emissions. The CA mechanism allows early shipments of the carriers to improve their load consolidation and reduce their repositioning trips. Sustainability and carbon emissions are considered by prioritizing the carrier’s carbon reduction performances. Two models are examined under the carbon emission regulations (Carbon tax and Cap-and-offset) to choose the winners in the CA. Our aim is to minimize the cost of transportation procurement and reduce carbon emissions by incorporating the green reputation-based winner determination problem within the procurement model. Computational experiments reveal the positive impact of prioritization and discounted offers in reducing both transportation costs and the number of empty trips. Indeed, our results show the introduction of the discounted bids allows a reduction of about 2% in the transportation cost for the shipper and 24% of empty movements, on average, for the carriers.

An Effective Two-Stage Algorithm for the Bid Generation Problem in the Transportation Service Market

“…To address this challenge, by considering both winning probability and conditional profit, Tan et al [10] broadened standard auction models by introducing the Cobb-Douglas function, enabling a more intricate evaluation of bidder preferences. Owing to the great complexity of BGPs in nature, most research has considered various factors such as capacity [7], auction game strategy [8], route optimization and collaborative transportation [5,6,9], bi-level programming and bundle generation problems (BuGP) [11,12], the online combinatorial auction (CA) [13,14], truckload (TL) transportation [5,[15][16][17], and fourth-party logistics (4PL) [18,19].…”

“…This involves carefully choosing transportation requests, service periods, and routing options for both reserved and additional requests. Utilizing location techniques, Triki [16] crafted an optimization approach aimed at bolstering the synergistic effects between auctioned bundle loads and the intricate interaction between auctioned and pre-existing loads; utilizing the central limit theorem (CLT), Zhang et al [17] propose a unique data-driven approach for fashioning polyhedral uncertainty sets. Their methodology incorporates two distinct scenarios: shipment volumes varying independently, as well as those exhibiting correlated patterns.…”