abstract
- © 2021 Elsevier LtdThis paper considers the multi-item inventory lot-sizing problem with supplier selection. The problem consists of determining an optimal purchasing plan in order to satisfy dynamic deterministic demands for multiple items over a finite planning horizon, taking into account the fact that multiple suppliers are available to purchase from. As the complexity of the problem was an open question, we show that it is NP-hard. We propose a facility location extended formulation for the problem which can be preprocessed based on the cost structure and describe new valid inequalities in the original space of variables. Furthermore, we study the projection of the extended formulation into the original space and show the connection between the inequalities generated by this projection and the newly proposed inequalities. Additionally, we present a simple and easy to implement yet very effective MIP (mixed integer programming) heuristic using the extended formulation. Besides, we introduce two new benchmark sets of instances to assess the performance of the approaches under different cost structures. Computational results show that the preprocessing approach can significantly reduce the size of the formulation to be solved, allowing both an increase in the number of instances solved to optimality within the time limit and a reduction on the average time to solve them. Moreover, the described inequalities can improve the performance of a standard formulation for nearly all instance groups. They can also be used to provide strong lower bounds for certain large instances for which the preprocessed facility location formulation fails even to provide a linear relaxation bound due to memory limitations. Furthermore, the proposed MIP heuristic outperforms the heuristics available in the literature as it obtains solution values which at least match those reported for all instance groups, strictly improving most of them. The results also show that the performance of the approaches can vary considerably under different cost structures.