A Hybrid Approach Using CLP and MILP Applied to Tank Farm Operation Scheduling

This work develops an optimization model to aid the operational decision-making in a real world tank farm scheduling. The short term scheduling of tank farm is a hard task because the specialist has to take into account issues concerning plant topology, mass balances, transfer policies, resource constraints, demand pattern, and changeovers. So this operational decision-making is still based on experience with the aid of manual computations. The main goal of this work is to reduce the difference between a theoretical model and the practical needs. In order to reduce this difference the formulation addresses a new aspect related to the operator procedure. When the operator executes the programmed activities many tasks are delayed or advanced for personal convenience. This fact can cause bottlenecks in the system operation. In order to avoid them, some considerations about qualitative variables are inserted in the model. So that, the generated scheduling tends to be more practical to represent the qualitative variables by means a fuzzy system. Moreover the scheduling problem is modeled in a unified framework, which uses Constraint Logic Programming (CLP) and Mixed Integer Linear Programming (MILP). This approach had a computational time smaller than only the MILP model, and is able to define a good solution in few seconds. The proposed model can be used to test and correct new operational conditions and scenarios rather than to just determine the scheduling of regular activities.