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Centralized bundle generation in auction-based collaborative transportation.

Margaretha Gansterer1, Richard F Hartl1

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Summary
This summary is machine-generated.

This study introduces an efficient method to reduce the number of bundles in combinatorial auctions for logistics collaborations. It ensures maximum coalition profit and feasibility, making large-scale auctions practical.

Keywords:
CollaborationsCombinatorial auctionsGenetic algorithmsLogisticsTransportation

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Area of Science:

  • Operations Research
  • Logistics Management
  • Computational Economics

Background:

  • Horizontal collaborations in logistics involve carriers jointly performing operations to enhance efficiency and sustainability.
  • Combinatorial auctions are used to organize the exchange of transportation requests among carriers.
  • The exponential growth in the number of possible bundles makes offering all combinations computationally intractable for real-world applications.

Purpose of the Study:

  • To develop an efficient method for reducing the set of bundles in combinatorial auctions for logistics.
  • To address the Bundle Generation Problem (BuGP) by maximizing total coalition profit while ensuring feasible bundle assignments.
  • To overcome the challenge of evaluating the objective function with incomplete information.

Main Methods:

  • Definition of the Bundle Generation Problem (BuGP).
  • Development of a proxy for the objective function to assess bundle attractiveness under incomplete information.
  • Implementation of a genetic algorithms-based framework to generate feasible and attractive bundles.

Main Results:

  • An efficient reduction of the complete set of bundles to a manageable subset.
  • Achievement of very good solution quality in bundle generation.
  • Significant reduction in the computational time for the auction procedure.

Conclusions:

  • The proposed approach makes combinatorial auctions of real-world size computationally tractable.
  • The method effectively balances maximizing coalition profit with ensuring feasible assignments.
  • This work represents a significant advancement for applying combinatorial auctions in practical logistics settings.