This work studies the problem of lot sizing and scheduling of multiple products on a single machine, with stochastic demand and sequence-dependent setup times, called Stochastic Economic Lot Scheduling Problem (SELSP). The present work differs from others in the literature by considering simple inventory control policies and using the simulation-optimization approach to calibrate their parameters. We consider two inventory control policies: (i) fixed cycling (First in Sequence - FIS) and (ii) dynamic scheduling based on inventory levels (Lowest Days of Supply - LDS), both combined with variable lot sizing. The problem is solved using AnyLogic simulation software and the OptQuest search engine to minimize the total inventory cost (ordering, holding and shortage costs). The experimental design included the following factors: number of items, coefficient of variation of demand, system workload, and degree of setup increment, allowing the comparison of the two control policies in different scenarios. The experiments show that dynamic scheduling (LDS) outperforms fixed cycling (FIS) in all scenarios, ranging from 0.2% to 4.6% reduction. The developed models proved to solve the problem, effectively generating reasonable solutions. Furthermore, as they are user-friendly, we believe they can be adapted, without great difficulties, to real-life scenarios of the process industry.