An Ascending Auction for Freight Forwarder Collaboration in Capacity Sharing

Abstract

In the highly competitive transportation market, small freight forwarders can form an alliance to secure capacity in advance and then share the capacity to fulfill their shipping requests during the freight season. Because not all the requests can be accepted, the rejected requests can be outsourced to the spot market typically at low profit margins. The forwarders are self-interested and have private information on the revenues and outsourcing profit margins of their requests. The centralized capacity-allocation problem is NP-hard, and its optimal solution may erode the profits of some forwarders. We propose an ascending auction mechanism to achieve truthful and approximately optimal capacity sharing. At each iteration of the auction, the set of bundles of capacity for each request is updated. The mechanism then determines a provisional capacity allocation and updates dual prices. Each request is allocated with a convex combination of bundles because a continuous capacity resource is considered. Strategy-proofness is ensured because all the winning bundles for an accepted request have the same payment, and the payment is ascending during the iterations. The mechanism iteratively expands the set of bundles and approximates the revenue loss to search optimal allocations, using the dual solution derived from minimizing primal-dual slackness. Social welfare is significantly improved compared with the no-collaboration case. We prove that the mechanism is strategy-proof, individually rational, weakly budget balanced, and finitely convergent. Extensive computational experiments show that the mechanism has efficiency loss less than 3% and budget surplus less than 10% in most cases. To achieve high system efficiency and small budget surplus with a reasonable computational time, it is better to select an appropriately small bid increment, especially for large-scale problems.

Assignment constraints in shared transportation services

“…Studies regarding decentralized planning systems focus mostly on establishing efficient frameworks, especially for complex auction-based systems (Gansterer and Recently, several contributions to the related field of cooperative game theory have been published. Lai et al (2019), for instance, present an ascending auction for freight forwarder collaboration in capacity sharing. The proposed mechanism iteratively expands the set of bundles and approximates the revenue loss to search optimal allocations.…”

Masking primal and dual models for data privacy in network revenue management

“…Although information exchange is reduced with the decentralized coordination and negotiation-based approaches, the parties still have to share some information on their individual operations, which may reveal confidential information about their activities. Several studies have pointed out that revealing primal or dual optimal solutions can provide a strategic advantage to other parties in the cooperation (Albrecht and Stadtler, 2015;Singh and O'Keefe, 2016;Lai et al, 2019;Rius-Sorolla et al, 2020). There is no mechanism available in the literature to coordinate independent cooperating parties while ensuring that the information shared by the parties remains private.…”

“…Given the allocated capacities, each party decides on the reservation price to maximize its own local objective in the second stage. Recently, Lai et al (2019) have studied the capacity allocation problem for a freight alliance by considering the data privacy in revenues and profit margins. They assume that the alliance partners jointly book freight capacity from the market according to the forecasted shipping demand and then share this capacity during the planning period.…”

Differential Privacy in Multi-Party Resource Sharing

“…Review of Related Literature. Collaborations among organisations have received considerable attention in the literature as it is popular in many industries including airlines (Wright et al, 2010), logistics and maritime shipping (Agarwal and Ergun, 2010;Verdonck et al, 2013;Lai et al, 2019) and retail (Guo and Wu, 2018). Most of these studies assume complete information exchange among partners, which may not be realistic in practice due to the regulations.…”