GRASP with Hybrid Path Relinking for Bi-Objective Winner Determination in Combinatorial Transportation Auctions

Artikel

The procurement of transportation services via large-scale combinatorial auctions involves a couple of complex decisions whose outcome highly influences the performance of the tender process. This paper examines the shipper's task of selecting a subset of the submitted bids which efficiently trades off total procurement cost against expected carrier performance. To solve this bi-objective winner determination problem, we propose a Pareto-based greedy randomized adaptive search procedure (GRASP). As a post-optimizer we use a path relinking procedure which is hybridized with branch-and-bound. Several variants of this algorithm are evaluated by means of artificial test instances which comply with important real-world characteristics. The two best variants prove superior to a previously published Pareto-based evolutionary algorithm.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: BuR - Business Research ; ISSN: 1866-8658 ; Volume: 3 ; Year: 2010 ; Issue: 2 ; Pages: 192-213 ; Göttingen: VHB - Verband der Hochschullehrer für Betriebswirtschaft, German Academic Association of Business Research

Classification and Topics

• Classification: Management
• Subject: combinatorial auction ; transportation procurement ; set covering problem ; multi-criteria decision making ; Pareto optimization ; GRASP ; Beschaffungsplanung ; Transportlogistik ; Containerverkehr ; Evolutionärer Algorithmus ; Multikriteria-Verfahren ; Pareto-Optimum

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Buer, Tobias ; Pankratz, Giselher
• Event: Veröffentlichung
• (who): VHB - Verband der Hochschullehrer für Betriebswirtschaft, German Academic Association of Business Research
• (where): Göttingen
• (when): 2010

Further information

• DOI: doi:10.1007/BF03342722
• Handle: http://hdl.handle.net/10419/103693
• URN: urn:nbn:de:0009-20-26859
• Last update: 10.03.2025, 11:43 AM CET