Solving a bi-objective winner determination problem in a transportation procurement auction

Abstract

This paper introduces a bi-objective winner determination problem which arises in the procurement of transportation contracts via combinatorial auctions where bundle bidding is possible. The problem is modelled as a bi-objective extension to the set covering problem. We consider both the minimisation of the total procurement costs and the maximisation of the service-quality level at which the transportation contracts are executed. Taking into account the size of real-world transport auctions, a solution method has to cope with problems of up to some hundred contracts and a few thousand bundle bids. To solve the problem, we propose a bi-objective branch-andbound algorithm and eight variants of a multiobjective genetic algorithm. Artificial benchmark instances that comply with important economic features of the transport domain are introduced to evaluate the methods. The branch-and-bound approach is able to find the optimal trade-off solutions in reasonable time for very small instances only. The eight variants of the genetic algorithm are compared among each other by means of large instances. The best variant is also evaluated using the small instances with known optimal solutions. The results indicate that the performance largely depends on the initialisation heuristic and suggest also that a well-balanced combination of genetic operators is crucial to obtain good solutions.

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

“…As an extension we apply a path relinking technique which is hybridized by an exact branch-and-bound procedure. Our results show that the GRASP approach clearly outperforms an evolutionary algorithm previously published in Buer and Pankratz (2010). The article is structured as follows: In Section 2 important features of the shipper's decision problem during the allocation stage are explained in detail.…”

“…This problem, described in the literature as the Winner Determination Problem (WDP), is an NP-hard, combinatorial optimization problem (Sandholm, Suri, Gilpin, and Levine 2002). In the literature there is as yet only a fairly limited number of papers that address a WDP in the context of the procurement of transport services (Cantillon and Pesendorfer 2006;Sheffi 2006, 2003;Elmaghraby and Keskinocak 2004;Ledyard, Olson, Porter, Swanson, and Torma 2002;Song and Regan 2003;Buer and Pankratz 2010). Nevertheless, in the recent past combinatorial auctions have already been applied in shipping with success.…”

“…Section 3), we use an enhanced model of the contract assignment problem. This model which was first introduced by Buer and Pankratz (2010) allows better consideration of the important features of the shipper's decision situation as stated above. For solving this problem we present a novel approach based on the GRASP metaheuristic (greedy randomized adaptive search procedure).…”

A strategic approach to improve sustainability in transportation service procurement

“…Almost all the literature in full truckload transportation service procurement has cost related objectives. Some of the researchers (Buer and Pankratz, 2010;Buer and Kopfer, 2014) formulated CAP as a bi-objective model by including the quality of service offered by the carrier as a second objective. Buer and Pankratz (2010) solved the bi-objective problem by using a branch and bound method and a multi objective evolutionary algorithm.…”

“…Some of the researchers (Buer and Pankratz, 2010;Buer and Kopfer, 2014) formulated CAP as a bi-objective model by including the quality of service offered by the carrier as a second objective. Buer and Pankratz (2010) solved the bi-objective problem by using a branch and bound method and a multi objective evolutionary algorithm. A hybrid metaheuristic based on GRASP (Greedy Randomized Adoptive Search Procedure) and ALNS (Adaptive Large Neighborhood Search) was proposed by Buer and Kopfer (2014) to solve the bi-objective model.…”