[Journal:] BuR - Business Research [ISSN:] 1866-8658 [Volume:] 3 [Year:] 2010 [Issue:] 2 [Pages:] 192-213
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.
combinatorial auction transportation procurement set covering problem multi-criteria decision making Pareto optimization GRASP