This thesis investigates the optimization algorithm used for steel temperature control on the run-out table at Tata Steel IJmuiden. The system currently implemented in the finishing mill is called STORM (Smart Temperature Optimization on the Run out table for Mechanical property control) and was created and fully implemented this year. This control system computes one setup based on the estimated maximum speed and properties of the head of the slab at the time of entering the finishing mill and is based on a Genetic Algorithm (GA). To accommodate for variations over the length of a steel slab, manual action is required. In order to automate this process, the aim is to further develop the system to enable computation of setups for multiple samples over the length of the slab within the limited time available between a slab entering the finishing mill and the start of the cooling process. For this purpose, this thesis introduces three precomputation extensions that incorporate the concept of a warm start with different methods to incorporate knowledge of the problem: STORM-Trained on a Single Model, STORM-Trained on Multiple Models and STORM-Trained on Multiple Objectives. The baseline is taken to be STORM-Uninformed, that uses no prior knowledge to compute multiple setups. The new methods are compared based on four different product recipes with respect to the fitness of the optimal setup found by the solver and the number of iterations required to obtain a useful setup. A setup is assumed to be useful when it is at most 1.2 times the known optimum of a problem instance. The experiments conducted to compare the three mentioned extensions show that when optimizing for samples over the length of the run-out table STORM-TMM outperforms both other methods, though for problem instances with two domains a population size of 200 is required for precomputation and even then not always a useful setup is found within the iteration limit. For optimization for different sets of objectives there is an even greater distinction in the results for problem instances with one and two domains. For the former, the multi-objective algorithm STORM-TMO most definitely performs best, where for the latter STORM-TSM performs best. Again when computing a setup for two domains, a useful setup is not always found. In conclusion, the multi-objective methods STORM-TMM and STORM-TMO can be used for sample setup computation for problems with one domain after some final checks. For problems with multiple domains, additional research is required.