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Optimisation models for the procurement through reverse combinatorial auctions in the logistics and food industries

Triki, Chefi, Akil, Jamila, Asmakh, Huda Al (2023) Optimisation models for the procurement through reverse combinatorial auctions in the logistics and food industries. International Journal of Procurement Management, 16 (4). Article Number 530. ISSN 1753-8432. (doi:10.1504/IJPM.2023.129555) (KAR id:101207)

Abstract

Nowadays, procurement managers in the different industries rely on the Reverse Combinatorial Auctions (RCAs) as a trading mechanism that had shown to be very efficient in allocating resources in several applications. RCAs allow the bidders to optimally express their economies of scope, since they can formulate their bids as a bundle of items, rather than on single item. Each bundle should be then either accepted or rejected all together without splitting. Here, we review and discuss the underlying mathematical optimisation models that represent the basis of the decision support system and discuss the possible benefits of using such paradigms for the different actors involved in the auctioning process. In addition, the paper highlights the advantages of employing RCAs in two major application fields, namely the logistics and food industries, in which this advanced trading paradigm had remarkable success by allowing the bidders to exploit better the synergies among the auctioned items and concede the auctioneers to minimise their procurement costs.

Item Type: Article
DOI/Identification number: 10.1504/IJPM.2023.129555
Uncontrolled keywords: combinatorial auctions, bid generation, winner determination, logistics procurement, food industry, optimisation models
Subjects: H Social Sciences > HF Commerce > HF5351 Business
Divisions: Divisions > Kent Business School - Division > Department of Analytics, Operations and Systems
Funders: University of Kent (https://ror.org/00xkeyj56)
Depositing User: Chefi Triki
Date Deposited: 09 May 2023 10:56 UTC
Last Modified: 05 Nov 2024 13:06 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/101207 (The current URI for this page, for reference purposes)

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