The storage of goods plays a crucial role in warehouse management systems. Although this operation is often considered simple, it is in fact complex, due to the volume of products, the uncertainty in demands, and the rapid customer service response that the market requires. Moreover, the optimization of logistical resources to maintain an efficient operational flow, such as the allocation of storage spaces, often requires complex decisions. In this paper, we analyze academic contributions on the storage location assignment problem published between 2005 and 2017. The literature is classified according to the solution methods, objectives, and related considerations. Suggestions are also provided for future research.

This paper addresses the stochastic permutation flow shop problem (SPFSP) in which the stochastic parameters are the processing times. This allows the modeling of setups and machine breakdowns. Likewise, it is proposed a multi-objective greedy randomized adaptive search procedure (GRASP) coupled with Monte-Carlo Simulation to obtain expected makespan and expected tardiness. To manage the bi-objective function, a sequential combined method is considered in the construction phase of the meta-heuristic. Moreover, the local Search combines 2-optimal interchanges with a Pareto Archived Evolution Strategy (PAES) to obtain the Pareto front. Also, some Taillard benchmark instances of deterministic permutation flow shop problem were adapted in order to include the variation in processing times. Accordingly, two coefficients of variation (CVs) were tested: one depending on expected processing times values defined as twice the expected processing time of a job, and a fixed value of 0.25. Thus, the computational results on benchmark instances show that the variable CV provided lower values of the expected makespan and tardiness, while the con-stant CV presented higher expected measures. The computational results present insights for further analysis on the behavior of stochastic scheduling problems for a better approach in real-life scenarios at industrial and service systems.