A micro-genetic algorithm for multi-objective scheduling of a real world pipeline network

The scheduling of activities to transport oil derivative products through a pipe network is a complex combinatorial problem that presents a hard computational solution. During the scheduling horizon, many batches are pumped from (or pass through) different areas. Pipes are shared resources. The balance between the demand requirements and the production campaigns, while satisfying inventory management issues and pipeline pumping procedures, is a difficult task. In order to reduce the complexity, this problem could be decomposed on three sub-problems according to the key elements of scheduling: assignment of resources, sequencing of activities, and determination of resource timing utilization by these activities. This work proposes a model to solve the sequencing and timing subproblems, and its main objective is to develop a hybrid solution based on genetic algorithm (GA) and mixed integer linear programming (MILP) to drive batches of oil derivative products through the network. As both techniques (GA and MILP) can require significant computational efforts, we propose the use of micro-genetic algorithms ðmGAÞ that generally guarantee good solutions with acceptable levels of computational effort. The mGA-MILP hybrid model has been implemented and tested on several practical cases of a Brazilian oil company. As a result, the model can provide a set of solutions that means different options of pipeline operations. This work contributes to the development of a tool to help the specialist solve the batch scheduling problem, which results in a more efficient use of the