Efficiency in supply chains is critically affected by the performance of operations within warehouses. For this reason, the activities related to the disposition and management of inventories are crucial. This work addresses the multi-level storage locations assignment problem for SKU pallets, considering divisible locations in the first level to improve the picking operation and reduce the travel times associated with the routes of the cranes. A mathematical programming model is developed considering the objective of minimizing the total travel distance, and in the background, maximizing the use of storage capacity. To solve this complex problem, we consider its decomposition into four subproblems, which are solved sequentially. To evaluate the performance of the model, two analysis scenarios based on different storage strategies are proposed to evaluate both the entry and exit distance of pallets, as well as the cost associated with the movements.

The analysis of a production line requires the consideration of its reliability and productive capacity since they have a direct influence on the expected levels of performance. These factors have an intrinsic stochastic behaviour which must be accounted for in the analysis. This paper proposes an integrated approach based on Monte Carlo simulation which is tested on a case study concerning the production line of a private mining company located in northern Chile. Specifically, the primary, secondary, and tertiary crushing processes are analysed in detail. Actual maintenance and operation data for 16 months of operation are considered in the case study. A tailored simulation model is developed that is able to jointly estimate the reliability and productivity of the process. The integration of these two aspects allows situations in which equipment becomes unavailable to be identified and this can improve business continuity and process productivity.