Evolutionary multi-objective optimization of environmental indicators of integrated crude oil supply chain under uncertainty

This study presents a multi-objective mathematical model for integrating upstream and midstream segments of crude oil supply chain in the context of environmental indicators. An actual case study in the Persian Gulf is considered. Upstream and midstream segments are integrated into the presented model due to their significant interaction. Also, oilfield development and transformation planning are considered simultaneously along with green aspects. The bi-objective optimization considers net present value (NPV) and environmental issues. A unique multi-objective evolutionary algorithm based on decomposition (MOEA-D) approach is employed to solve the proposed mixed integer nonlinear programming model. The results of MOEA-D are compared with the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO). The results indicate the superiority of the MOEA-D approach for large size problems.