A pareto optimal study for the multi-objective oil platform location problem with NSGA-II

Offshore production has a high level of complexity inherent to its development and exploration. This context leads to the need for the Multi-objective and Multi-level Capacitated Oil Platform Location Problem (MMCOPLP), which is used for the definition of places for platforms and drilling of wells, given a set of possible candidate points. The conflict between objectives is commonly present in the multi-objective problems and, consequently, there is a set of optimal solutions called the Pareto Optimal. The Pareto Optimal is hard to be determined by exact methods, and heuristics and/or metaheuristics are implemented as alternative methods. Considering the above, the main objective of this study is to propose a Non-dominated Sorting Genetic Algorithm (NSGA-II) to solve the MMCOPLP. Instances are also proposed and the ε-Constraint exact method is used to find the Pareto Optimal. Based on the error ratio, on generational distance and on hypervolume metrics, computational experiments show that the NSGA-II provides good solutions, close to Pareto Optimal and better than a Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic proposed in the literature.