Homogeneous grouping of non-prime steel products for online auctions: a case study

Not all products meet customers’ quality expectations after the steelmaking process. Some of them, labelled as ‘non-prime’ products, are sold in a periodic online auction. These products need to be grouped into the smallest feasible number of bundles as homogeneous as possible, as this increases the attractiveness of the bundles and hence their selling prices. This results in a highly complex optimisation problem, also conditioned by other requirements, with large economic implications. It may be interpreted as a variant of the well-known bin packing problem. In this article, we formalise it mathematically by studying the real problem faced by a multinational in the steel industry. We also propose a structured, three-stage solution procedure: (i) initial division of the products according to their characteristics; (ii) cluster analysis; and (iii) allocation of products to bundles via optimisation methods. In the last stage, we implement three heuristic algorithms: FIFO, greedy, and distance-based. Building on previous works, we develop 80 test instances, which we use to compare the heuristics. We observe that the greedy algorithm generally outperforms its competitors; however, the distance-based one proves to be more appropriate for large sets of products. Last, we apply the proposed solution procedure to real-world datasets and discuss the benefits obtained by the organisation.