Strategic and tactical mathematical programming models within the crude oil supply chain context—A review

Carbon intensity of global crude oil trading and market policy implications

The energy mix transition has accelerated the need for more accurate emissions reporting throughout the petroleum supply chain. Despite increasing environmental regulations and pressure for emissions disclosure, the low resolution of existing carbon footprint assessment does not account for the complexity of crude oil trading. The lack of source crude traceability has led to poor visibility into the "well-to-refinery-entrance" carbon intensities at the level of granular pathways between producers and destination markets. Using high-fidelity datasets, optimization algorithms to facilitate supply chain traceability and bottom-up, physics-based emission estimators, we show that the variability in global "well-to-refinery-entrance" carbon intensities at the level of crude trade pathways is significant: 4.2-214.1 kg-CO2-equivalent/barrel with a volume-weighted average of 50.5 kg-CO2-equivalent/barrel. Coupled with oil supply forecasts under 1.5 °C scenarios up to 2050, this variability translates to additional CO2-equivalent savings of 1.5-6.1 Gigatons that could be realized solely by prioritizing low-carbon supply chain pathways without other capital-intensive mitigation measures.

Sustainable Integrated Fuzzy Optimization for Multimodal Petroleum Supply Chain Design with Pipeline System: The Case Study of Vietnam

Over the years, oil-related energy sources have played an irreplaceable role in both developed and developing countries. Therefore, the efficiency of petroleum supply chains is a key factor that significantly affects the economy. This research aimed to optimize the configuration of the uncertainty multimodal petroleum supply chain in terms of economy, energy and environment (3E assessment). This study proposes a novel integration methodology between a heuristic algorithm and exact solution optimization. In the first stage, this study determines the facilities’ potential geographical coordinates using heuristic algorithm. Then, the fuzzy min-max goal programming model (FMMGPM) was developed to find the multi-objective solutions. In particular, this model allows analysis of supply chain uncertainty through simultaneous factors such as demand, resource, cost and price. These uncertainty factors are expressed as triangular fuzzy parameters that can be analyzed in terms of both probability and magnitude. Moreover, the model is applied to the entire petroleum supply chain in Vietnam, including downstream and upstream activities. In addition, another novelty is that for the first time, pipeline systems in logistics activities are considered in Vietnam’s petroleum supply chain optimization study. The results also show the short-term and long-term benefits of developing a pipeline system for oil transportation in Vietnam’s petroleum supply chain. To evaluate the effects of uncertainty on design decisions, this study also performed a sensitivity analysis with scenarios constructed based on different magnitudes and probabilities of uncertainty.

A sustainable mathematical model for design of net zero energy buildings

Energy is vital recourse for economic development of today's business. The services demanded of residential and commercial buildings require substantial energy use. Energy consumption in this sector has been growing in total, gradually. As a result the high emission of greenhouse gases is released and, hence, the saving energy with better building management have made a major priority of the energy and environment sectors throughout the world. In this direction, to reduce energy consumption and mitigate environmental impacts in buildings, net-zero energy buildings (NZEB) is a very effective solution. As a result, a multi-objective model is developed to identify the best combination of materials and construction options considering their related costs, energy efficiency, and environmental impacts of buildings, simultaneously. This sustainable model is presented to construct a building considering the construction costs and energy consumption of the design options. To design the NZEB, while minimizing costs and carbon emissions, use has been made of a combination of different types of active/heating and cooling systems and renewable equipment through such high-efficiency, effective, and updated technologies as the solar panel. Finally, the case study of a residential building with two scenarios is used to demonstrate the proposed framework. The results show that, for scenarios1 and 2 respectively using insulation thickness such as (wall, roof, and windows) and renewable equipment have the highest sustainable impact in NEBZ's performance.

Adjustable Robust Optimization for Planning Logistics Operations in Downstream Oil Networks

The oil industry operates in a very uncertain marketplace, where uncertain conditions can engender oil production fluctuations, order cancellation, transportation delays, etc. Uncertainty may arise from several sources and inexorably affect its management by interfering in the associated decision-making, increasing costs and decreasing margins. In this context, companies often must make fast and precise decisions based on inaccurate information about their operations. The development of mathematical programming techniques in order to manage oil networks under uncertainty is thus a very relevant and timely issue. This paper proposes an adjustable robust optimization approach for the optimization of the refined products distribution in a downstream oil network under uncertainty in market demands. Alternative optimization techniques are studied and employed to tackle this planning problem under uncertainty, which is also cast as a non-adjustable robust optimization problem and a stochastic programing problem. The proposed models are then employed to solve a real case study based on the Portuguese oil industry. The results show minor discrepancies in terms of network profitability and material flows between the three approaches, while the major differences are related to problem sizes and computational effort. Also, the adjustable model shows to be the most adequate one to handle the uncertain distribution problem, because it balances more satisfactorily solution quality, feasibility and computational performance.