Integrated planning of downstream petroleum supply chain: a multi-objective stochastic approach

Abstract

In studying petroleum supply chain networks, past studies have largely segregated three critical decision-making aspects: integrated planning, uncertainties, and multi-objective setting. This study focuses on consolidating these aspects and proposes a stochastic, multi-objective, mixed-integer linear programming model for strategic and tactical planning of downstream petroleum supply chain (DPSC) networks. Demand, considered the uncertain parameter, is modeled using a two-stage stochastic approach based on scenarios. The model—designed for multiple supply centers, distribution centers, products, and transportation modes—also considers transshipment between the centers. The objective functions consider simultaneous minimization of transportation cost and product loss cost that is incurred during transportation between the centers. The application of the proposed model is demonstrated with a case study of a real-world DPSC network undergoing construction of new pipelines and expansion of storage facilities. The augmented ε-constraint method is used to solve the model. Interesting trade-offs in the case study are analyzed, aiding the decision-makers in exploiting the model as a decision-support tool to better understand the complexity, flexibility, and risk of integrated decision-making under uncertainty.