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The supply chain of many industries, including Oil and Gas, was significantly affected by the disruption caused by the Covid pandemic. This, in turn, had a knock-on effect on other industries around the globe. Sustaining the impact of the disruption posed a major challenge for the industry. This study contributes to the existing literature by identifying and analyzing the most significant drivers that affected the sustainability of the Oil and Gas supply chain during the Covid pandemic. Fifteen drivers were identified based on an extensive literature review and a survey conducted with experts working in the Oil and Gas industry. Multi-criteria decision-making methodologies were used to analyze these drivers. The analysis from the fuzzy analytical hierarchy process found that the most important drivers for the sustainability of the Oil and gas supply chain during the pandemic were "Risk management capacity", "Government regulation" and "Health and safety of employees". On the other hand, the driver "Community Pressure" was found to be of the least importance. Furthermore, the study integrated the results of the fuzzy analytical hierarchy process with the fuzzy technique for order of preference by similarity to ideal solution to calculate the supply chain sustainability index. A case example was demonstrated to rank the industries based on such calculations. This study can support the governmental institutions in benchmarking the Oil and Gas industry based on its sustainability index. Additionally, the outcomes of the study will help industrial decision makers prioritize the drivers the company should focus and devise strategies based on the priority to improve the sustainability of their supply chain during severe disruption. This will be crucial as the World health organization has cautioned that the world may encounter another pandemic in the near future. [ABSTRACT FROM AUTHOR]

In this research, we introduce a significant advancement in decision-making methodologies by proposing the Combine Compromise Solution Approach. This innovative method addresses the complexities inherent in multi-criteria decision-making scenarios, particularly in optimizing logistics within the oil and gas supply chain. Through our exploration of circular spherical fuzzy sets, we investigate various algebraic operations without detailing the research methodology. Our primary contribution lies in the practical application and effectiveness of the Combine Compromise Solution Approach, exemplified through a case study on logistics optimization. By presenting insights gleaned from this approach and conducting a comparative analysis against alternative methods, we demonstrate its utility and potential impact in real-world contexts. This research not only offers immediate practical solutions but also paves the way for future investigations into the broader applications of circular spherical fuzzy sets in decision-making processes, thus advancing the field significantly.

• Oil and Gas Supply Chain is a multifaceted network comprising diverse activities and echelons. • Operations planning and maintenance scheduling optimized in a risk management framework. • Relax-and-Fix (RF) heuristic finds an initial solution. • The model schedules maintenance activities consistently to reduce lost sales. • The model behaves consistently under different decision-maker risk attitudes. The Oil and Gas Supply Chain (OGSC) is a multifaceted network comprising diverse activities and echelons. Instability or interruptions can cause economic fluctuations, impacting industries, markets, and consumers. Maintenance activities, which pause production but extend facilities' life, are recommended during non-peak demand periods to avoid production losses and meet customer demand. To mitigate these effects, decisions on operations planning, maintenance scheduling, and maintenance team assignments should be optimized in a risk management framework. The proposed model adopts a mixed-integer linear programming (MILP) framework and is solved via a sequential approach that incorporates the relax-and-fix (RF) heuristic in order to find a solution that is close to optimal. Subsequently, the solution serves as an initial solution for the CPLEX solver, which employs a branch-and-cut algorithm to attain the exact optimal solution. The practicality of this model has been showcased through its application to the supply chain in Saudi Arabia. The model efficiently schedules maintenance activities evenly and consistently across the OGSC plants over the planning period to reduce lost sales by keeping plants operational during high-demand periods. Furthermore, a sensitivity analysis was conducted to investigate the influence of the decision-maker's risk attitude on the outcomes that were obtained. [ABSTRACT FROM AUTHOR]

The oil and gas networks are overlapped because of the inclusion of associated gas in crude oil. This necessitates the integration and planning of oil and gas supply chain together. In recent years, hydrocarbon market has experienced high fluctuation in demands and prices which leads to considerable economic disruptions. Therefore, planning of oil and gas supply chain, considering market uncertainty is a significant area of research. In this regard, this study develops a multi-objective stochastic optimization model for tactical planning of downstream segment of oil and natural gas supply chain under uncertainty of price and demand of petroleum products. The proposed model was formulated based on a two-stage stochastic programming approach with a finite number of realizations. The proposed model helps to assess various trade-offs among the selected goals and guides decision maker(s) to effectively manage oil and natural gas supply chain. The applicability and the utility of the proposed model has been demonstrated using the case of Saudi Arabia oil and gas supply chain. The model is solved using the improved augmented ε-constraint algorithm. The impact of uncertainty of price and demand of petroleum products on the obtained results was investigated. The Value of Stochastic Solution (VSS) for total cost, total revenue, and service level reached a maximum of 12.6%, 0.4%, and 6.2% of wait-and see solutions, respectively. Therefore, the Value of the Stochastic Solution proved the importance of using stochastic programming approach over deterministic approach. In addition, the obtained results indicate that uncertainty in demand has higher impact on the oil and gas supply chain performance than the price. [ABSTRACT FROM AUTHOR]

The worldwide oil & gas industry is one of the world's most complex business networks, and is connected with almost every supply chain branch. It includes international and domestic transportation, materials handling, ordering and inventory visibility and control, import/export facilitation and social network, etc. Traditionally, it has been influenced by big oilfield companies. However, in recent years the industry has been changing into a more heterogeneous and diverse network of businesses, and the oilfields are getting smaller and more diverse. One of the reason could be dwindling the oil reserves and growing specialized companies which are able to extract hydrocarbons; another reason is the restructuring and globalization of the entire business as well as some new technology implementing. Using agent-based modeling and big data technology integrity, we are able to optimize supply chain in oil and gas industries.

Hydrocarbon supply chain (HCSC) is a complex network that extends from oil and gas fields to demand nodes. Integrating operation and maintenance activities along this complex network is crucial since the hydrocarbon industry is the most influential sector in the world economy, and any disruptions or variations in hydrocarbon product supply will affect the whole world economy. Therefore, effective and thoughtful maintenance extends the life of an asset and enhances its reliability. To prevent huge losses in production and ultimately satisfy customer needs, the maintenance jobs are preferred to be performed during times of low demand. Thus, operation planning and maintenance scheduling decisions are dependent and should be optimized simultaneously. Therefore, the aim of this study is to develop an integrated mathematical model for the operation and maintenance planning of the oil and gas supply chain. The utility of the proposed model has been demonstrated using the Saudi Arabian HCSC. The proposed model effectively produces optimal operation and maintenance schedule decisions. A sensitivity analysis was conducted to study the effect of critical parameters on the obtained results. [ABSTRACT FROM AUTHOR]

The work presents a simulation-based Multi-Objective Optimization (MOO) framework for efficient production planning in Energy Supply Chains (ESCs). An Agent-based Model (ABM) that is more comprehensive than others adopted in the literature is developed to simulate the agent’s uncertain behaviors and the transaction processes stochastically occurring in dynamically changing ESC structures. These are important realistic characteristics that are rarely considered. The simulation is embedded into a Non-dominated Sorting Genetic Algorithm (NSGA-II)-based optimization scheme to identify the Pareto solutions for which the ESC total profit is maximized and the disequilibrium among its agent’s profits is minimized, while uncertainty is accounted for by Monte Carlo (MC) sampling. An oil and gas ESC model with five layers is considered to show the proposed framework and its capability of enabling efficient management of the ESC sustained production while considering the agent’s uncertain interactions and the dynamically changing structure.

Hydrocarbon supply chain (HCSC) is a complex network that extends from oil and gas fields to demand nodes. Integrating operation and maintenance activities along this complex network is crucial since the hydrocarbon industry is the most influential sector in the world economy, and any disruptions or variations in hydrocarbon product supply will affect the whole world economy. Therefore, effective and thoughtful maintenance extends the life of an asset and enhances its reliability. To prevent huge losses in production and ultimately satisfy customer needs, the maintenance jobs are preferred to be performed during times of low demand. Thus, operation planning and maintenance scheduling decisions are dependent and should be optimized simultaneously. Therefore, the aim of this study is to develop an integrated mathematical model for the operation and maintenance planning of the oil and gas supply chain. The utility of the proposed model has been demonstrated using the Saudi Arabian HCSC. The proposed model effectively produces optimal operation and maintenance schedule decisions. A sensitivity analysis was conducted to study the effect of critical parameters on the obtained results.

Oil and gas companies play an important role in the global economy since they supply a large portion of the necessary energy to the world. The optimal production of oil and gas should be performed in an integrated fashion for the whole supply chain. The downstream oil and gas supply chain (OGSC) has attracted the interest of many researchers due to its central role in the world economy. This paper develops an integrated multi-objective OGSC model for medium-term tactical decision making for the OGSC downstream segment. The selected objectives related to downstream activities are the following: minimize the total cost, maximize the total revenue, and maximize the service level. The model includes multi-period and multi-product inputs. The model is verified and solved using an improved augmented ε-constraint algorithm to generate Pareto optimal solutions. The model assists in assessing various trade-offs among different objectives and guides decision makers for the effective management of the downstream OGSC. The utility of the proposed model is demonstrated using a real case from a Saudi Arabian downstream OGSC. Sensitivity analysis is conducted to investigate the effects of input parameters on the set of Pareto optimal solutions. The model is expected to have a positive impact on the future management of this important component of the energy sector. [ABSTRACT FROM AUTHOR]

The work presents a simulation-based Multi-Objective Optimization (MOO) framework for efficient production planning in Energy Supply Chains (ESCs). An Agent-based Model (ABM) that is more comprehensive than others adopted in the literature is developed to simulate the agent's uncertain behaviors and the transaction processes stochastically occurring in dynamically changing ESC structures. These are important realistic characteristics that are rarely considered. The simulation is embedded into a Non-dominated Sorting Genetic Algorithm (NSGA-II)-based optimization scheme to identify the Pareto solutions for which the ESC total profit is maximized and the disequilibrium among its agent's profits is minimized, while uncertainty is accounted for by Monte Carlo (MC) sampling. An oil and gas ESC model with five layers is considered to show the proposed framework and its capability of enabling efficient management of the ESC sustained production while considering the agent's uncertain interactions and the dynamically changing structure. [ABSTRACT FROM AUTHOR]