give a gap analysis table based on these literature review :
Attia et al. (2019) presented a multi objective optimization model for intermediate term planning of the supply chain in upstream hydrocarbon resources. The proposed model considered the environmental aspects and sustainability in planning chain operations. The model considers sustainability as an objective by minimizing the depletion rate of the reservoir. Beiranvand et al. (2018) developed a mathematical programming model for Iran's crude oil supply chain. The model consists of crude oil, refinery, petro chemistry, and downstream uses. By increasing the revenue from the sale of oil and its derivatives, as well as decreasing the related costs, the model attempts to maximize the amount of profit from the crude oil market and its derivatives. In order to consider real-world conditions with regard to the uncertainties, a robust optimization model was developed to increase the profitability of the whole chain by considering different scenarios. The model considers the possibility of violating the demand limit. In another study, Nicoletti and You (2020) modeled the crude oil supply chain from the oil well to the refinery as a mixed integer bi level linear program, including the conflicting objectives and interactions among different stakeholders. The composition, pricing, transportation distances, and environmental impacts of crude oil were considered in the model. In the bi level problem, crude oil producers aim to maximize their profits from selling crude oil, while crude oil refiner has dual objectives of both maximizing the profit from selling products to the market and minimizing the environmental effect of refinery products during their life cycle, which is determined by the type of crude oil purchased by the refinery. Then, the resulting model was applied in the two case studies. Zarrinpoor and Omidvari (2021) presented a mathematical model for designing the crude oil supply chain through considering related to facility location, demand allocation, transportation, and distribution planning. The proposed model considered the environmental requirements for greenhouse gas emissions, where the amount of greenhouse gas emissions from oil transportation cannot exceed a certain amount. The uncertainty of budget parameters, capacity of transportation units, capacity of exploitation units, quantity of exports, and amount of crude oil extraction and production, as well as the demand for refinery products and their production rate were included in the proposed model. In addition, a robust optimization approach was employed to deal with uncertainty in the model parameters. Numerical results verified the efficiency of the proposed model and indicated that the efficiency of the model decreases by increasing the uncertainty level of profitability. However, the profitability of oil supply chain can be guaranteed by handling the uncertainties of the parameters and appropriate production and distribution management. 
Some papers also included gas-natwork in their models. For example, Mikolajková et al. (2017) designed an optimization model for gas distribution pipeline network by considering supply of gas. Behrooz and Bozarjmehri (2017) studied the daily planning of natural gas transmission systems under demand uncertainty. Bittante et al. (2018) developed a mathematical model to help decide on the tactical aspects of gas supply chain design by focusing maritime transport between a set of supply ports and sparsely distributed receiving ports with given demands. Zarei and Naseri (2019) designed a natural gas supply chain (NGSC composed of oil and gas fields, refineries, distribution centers (DCs), or major consumption centers, import and export terminals, and underground storage reservoirs (USRs) by developing a mixed integer linear programming (MILP) model which optimizes the location of new facilities and pipeline routes together with the capacity and number of pipelines. Additionally, they formulated the expansion of the existing facilities and pipeline routes, extraction and production rates, gas storage, transmission flow rates, and import and export volumes on a limited time horizon.
Azarakhsh et al., (2021) presents a multi-objective, scenario-based mixed-integer linear programming model for the entire oil supply chain, addressing uncertainties and incorporating social, environmental, and risk management aspects, validated using real data.
Redutskiy and Balycheva (2024) address a research gap by proposing a mixed-integer nonlinear programming model for planning capacities and coordinating activities in the integrated petroleum supply chain, focusing on minimizing energy consumption and enhancing operational efficiency.

C.	STRATEGIC AND TACTICAL PLANNING 
According to Chopra and Meindl (2007), the strategic problem in supply chain management involves designing the infrastructure of the supply chain to align it with the strategic objectives of companies in the network. Optimization models aid decision-making processes across the network, improving overall profitability by balancing sourcing, production, inventory, and transportation costs while maintaining required service levels, as highlighted by Barbosa-Póvoa (2014). Facility location, capacity sizing, technology selection, transportation modes, outsourcing, and investment decisions are key strategic decisions (Sahebi et al., 2014) that have a long-term impact on the supply chain due to high setup costs.
In tactical planning, the goal is to optimize the established configuration by making decisions related to production, distribution, resource planning, inventory management, and control policies within a timeframe of a few months to a year. Combining strategic and tactical decisions in network design leads to better outcomes, as noted by Barbosa-Póvoa (2014) and Papageorgiou (2009). 
Rafie and Sahebi (2021) introduces an optimization model for integrating gas-oil and biodiesel supply chains, optimizing their connection points with economic and environmental objectives. Applied to a real case study in Iran, the model addresses location, allocation, production planning, inventory management, and capacity expansion, showing promise for future fuel source management and supply chain integration research. 
Alnaqbi et al., (2023) proposes a stochastic model for tactical planning of the Crude Oil Supply Chain (COSC) to address cost and demand uncertainties. The model integrates multi-echelon supply chains, multiple products, and a multi-period planning horizon, considering inventory and backorder penalties. Using Sample Average Approximation (SAA) with Multiple Replications Procedure (MRP), the model illustrates the impact of cost uncertainty on planning decisions and synergy gains, highlighting the value of modeling uncertainty in supply chain planning. Finally, in this paper, we consider strategic and tactical planning in an integrated model.