Aicha Aguezzoul - Automatic Laboratory of Grenoble, France
Pierre Ladet - Automatic Laboratory of Grenoble, France

In today’s increasing competitive business environment, the buyer must establish and work with a cost-effective and responsive network of suppliers in order to success. The suppliers selection and evaluation is a strategic purchasing decision that commit significant resources (40% to 80% of total product cost) and impact the total performance of the firm. The studies in that field show that this decision is a complex process involving various criteria (quality, price, delivery, etc). These criteria may vary depending on the type of product considered and are often in conflict with one another. The problem is how to select suppliers that perform satisfactorily on the desired criteria. The literature survey reveals that various methodologies to formulate this problem are suggested even if only a few mathematical programming models have been published to date. However, very little attention is given to transportation although its cost may be significantly important to the suppliers selection. Moreover, transportation and inventory elements are highly interrelated and contribute most to the total logistics costs: costs incurred in the suppliers while the products wait to be shipped, costs represented by the products in transit and costs incurred in the buyer while the products wait to be used. In this paper, we propose a mixed nonlinear programming approach to simultaneously determine the optimal number of suppliers to employ and the order quantities to allocate to them, taking into account the transportation policy. We study the case where the products can be shipped directly or via consolidation terminals from suppliers to buyer. The objective of the model is to minimize the total cost, which includes transportation, ordering and storage costs under suppliers, buyer and transportation constraints. The model is implemented in a specialized optimization software such as MATLAB and it is illustrated using a numerical example.

Mixed Nonlinear Programming, Supplier Selection, Consolidation Terminals, Inventory