Improving natural gas supply chain profitability: A multi-methods optimization study

Developing effective optimization models to improve pipeline network profitability in oil and gas supply chains is one of the most promising research areas in this industry. Because of the substantial advantages natural gas networks' operations have realized, this industry has become more competitive and eager to develop robust supply optimization decision models. However, although several models and techniques have been developed to reduce natural gas consumption, only very few studies have focused on comparing the performance of these models and the implications of the distinct optimization performances. Consequently, the generalizability of the research in the area is still problematic, representing a research area not sufficiently explored. Taking this into account, this paper compares the fuel consumption values in a French gas pipeline by analyzing the Genetic algorithms (GA), Generalized reduced gradient (GRG), and Ant colony optimization (ACO) models. Overall, our findings show significant differences in gas consumption when the ACO and GA are compared with the GRG technique. Furthermore, the findings indicate that ACOs are competitive with GA and GRG in computational efficiency in finding near-global optimized solutions. The article can assist decision-makers and policymakers in discovering the most profitable operational parameters to minimize gas consumption and increase the profitability of natural gas networks.