Advanced synergy-driven algorithms for bid generation in transportation auctions

Md. Rakibul Hasan (), Chefi Triki (), Adel Elomri () and Abrar Tasneem Abir ()
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Md. Rakibul Hasan: Hamad Bin Khalifa University
Chefi Triki: University of Kent
Adel Elomri: Hamad Bin Khalifa University
Abrar Tasneem Abir: Carnegie Mellon University Qatar

Annals of Operations Research, 2025, vol. 347, issue 3, No 7, 1339-1384

Abstract: Abstract Securing transportation services is crucial for supply chain management (SCM), as it constitutes the most significant expense within this domain. Implementing an effective bid generation strategy in combinatorial auctions (CAs) as a procurement mechanism to secure these services is vital for enhancing SCM efficiency. This study presents an advanced approach for quantifying synergy within the transportation network, aiming to identify the optimal bundles of auctioned contracts (AC). An efficient synergy-based bid generation algorithm has been developed to determine the optimal bids to be submitted within a transportation CA framework. This approach addresses two scenarios: one that considers service time limitations and another that allows for flexibility in early time constraints for the AC. The study investigates the potential of implementing a discount system by achieving savings through a relaxed time approach. It also examines the system’s effectiveness in promoting more efficient transportation by minimizing travel distances on roadways in relaxed early-time cases. This research provides an efficient solution to the bid generation problem (BGP) with high dimensionality. Results indicate that the BGP having 550 booked contracts (BC), 800 AC, and 220 cities is solved within a reasonable timeframe. Furthermore, the study found that the computational complexity of the synergy-based BGP relies more on the ratio of the number of AC to BC rather than solely on the number of AC.

Keywords: Bid generation; Combinatorial auction; Transportation procurement; Synergy; Algorithms; Discounts (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-025-06492-0

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