Nowadays, with the help of the IoT sensor network, we can adopt optimization techniques to support real-time engineering operations over a time horizon. In this optimization problem, the main challenges are twofold. First, we have to consider engineering constraints and capacity into the problem formulation, as well as multiple and often conflicting objectives need to be taken into consideration. Second, we need to solve the optimization within minutes to match the needs of the operation. The solution should contain recommendations of multiple operation steps of the plant-wide transformation processes. In our work we apply the Analytic Hierarchy Process (AHP) as an add-on method of traditional management of plant optimization objectives. This approach allows dealing with both subjective and objective aspects of a decision-making process. As a result, it allows creating an objective function in a more configurable way that can be optimized within a reasonable time. To validate this approach, we compare our results from the AHP versus multiple steps optimization as well as the traditional weighted approach. We demonstrate advantages of the AHP approach in terms of its simplicity, performance, and flexibility.