Downstream Oil Supply Research Articles

Two-stage optimization model for scheduling multiproduct pipeline network with multi-source and multi-terminal

The multiproduct pipeline network with multi-source and multi-terminal encompasses multi-point injection and multi-point distribution. Upon reorganization of batches at transfer depots, the intricacies of precise batch tracking become increasingly complex, thereby rendering the optimization of multiproduct pipeline network scheduling particularly arduous. In this paper, we introduce a dynamic index batch numbering method. By delineating the boundaries of intermediate injection points, the issue is bifurcated into two sub-problems: the optimization of batch planning for multiproduct pipeline with single-source and multi-terminal and the optimization of resource allocation at intermediate oil depots. The proposed algorithm is validated on two genuine multiproduct pipeline networks in China, demonstrating its capability to generate feasible solutions within a computational constraint of 18,000 CPU seconds. The findings reveal that this methodology holistically considers the entire pipeline network system, encompassing upstream oil intake, downstream oil supply, and the tank capacities of transfer depots, thereby establishing a coherent link between pipeline scheduling and depot planning. This paper may serve as a reference for the automated formulation of multiproduct pipeline network scheduling strategies.

A dynamic two-phased approach for planning downstream oil supply chain network under uncertainty

This paper introduces an innovative approach for designing a downstream oil supply chain network under uncertain conditions, aiming to bridge existing gaps in tactical decision-making and day-to-day operational management. In the initial phase, the focus lies on tactical and operational decisions related to network planning, while the second phase emphasizes real-time transshipment decisions for daily operations. This two-phased strategy proves to be adaptable and proficient in effectively handling uncertainties. Applying the mixed possibilistic-flexible programming and a rolling horizon approach, we enhance the overall performance of the oil supply chain network. To validate the efficacy of our proposed method, we conduct a comprehensive case study utilizing a database sourced from governmental agencies, specifically tailored to the Iranian oil supply chain. Our results demonstrate the efficiency of the proposed approach in addressing uncertainties, including demand fluctuations and network costs. Furthermore, the methodology exhibits potential for broader applications in other supply chain systems and industries grappling with similar challenges.

An integrated MINLP model for multi-party coordination in downstream oil supply chain

Cooperation among enterprises can bring overall and individual performance improvement, and a smooth coordination method is indispensable. However, due to the lack of customized coordination methods, cooperation in the downstream oil supply chain cannot be carried out smoothly. This paper intends to propose a multi-party coordination method to promote cooperation between oil shippers and pipeline operator by optimizing oil transportation, oil substitution and pipeline pricing schemes. An integrated game-theoretic modeling and analysis approach is developed to characterize the operation behaviors of all stakeholders in the downstream oil supply chain. The proposed mixed integer nonlinear programming model constrains supply and demand capacity, transportation routes, oil substitution rules and pipeline freight levels. Logarithm transformation and price discretization are introduced for model linear approximation. Simulation experiments are carried out in the oil distribution system in South China. The results show that compared to the business-as-usual scheme, the new scheme saves transportation cost by 3.48%, increases pipeline turnover by 5.7%, and reduces energy consumption and emissions by 7.66% and 6.77%. It is proved that the proposed method improves the revenue of the whole system, achieves fair revenue distribution, and also improves the energy and environmental benefits of the oil supply chain.

A trio of resiliency, reliability, and uncertainty to design and plan the downstream oil supply chain

The main goal of this research is to design and plan a downstream oil supply chain network under mixed uncertainty. To reach this goal, a bi-objective mixed-integer linear model is proposed to make decisions at tactical and strategic levels. Notably, to deal with mixed uncertainty, a hybrid uncertain programming approach including adjustable possibilistic programming, chance-constrained programming, scenario-based programming, and p-robust optimization is developed and applied. Finally, the proposed model is implemented in a real-life case from the National Iranian Oil Products Distribution Company to illustrate the applicability and efficacy of the proposed approach. The analysis of the results indicates that the benefit of the used methods leads to the improvement of network performance against operational and disruption risks. Moreover, the analysis of the results indicates that the tailored approaches improve the total costs, the resiliency of the network, and the reliability of the chain in comparison to the deterministic situation.

Resilience assessment of the downstream oil supply chain considering the inventory strategy in extreme weather events

As climate change intensifies and extreme weather events occur more frequently, the uncertainties of demand, production and inventory will affect the operation and management of supply chains. In this paper, a systematic method for evaluating the resilience of the supply chain is proposed, which is applied to the demand side management of an actual downstream oil supply chain. According to the practical operation of the downstream oil supply chain, several scenarios such as disruption of refineries, shutdown of pipelines and increasing demand are defined. Then, two indices are introduced to make a quantitative resilience assessment for the studied downstream oil supply chain. The inventory strategy is also proposed and applied to different scenarios. Finally, Monte Carlo simulation is used to make a comprehensive analysis with multiple scenarios. The results show that increasing the inventory of storage depots can improve the resilience of the supply chain by 2.67% to 14.83%. According to the sensitivity analysis for the failure rate and repair rate of each link, it is indicated that the resilience can be improved by 5.95% when the repair rate is doubled and be reduced to 16.04% when the repair rate is reduced by half.

The determinant of a five-stage downstream oil supply chain: An empirical study of Ghana

Background: The benefits derived from a downstream oil supply chain is very explicit and tangible. Various studies have looked at numerous determinants that drive the downstream oil supply chain, mainly among a specific player. However, the metamorphic nature of the industry powered by technological advancement, new policies, new oil well discoveries, and others raise an urgent need to assess the determinants of Ghana’s downstream oil supply chain. Therefore, this article scrutinises the five-stage downstream oil supply chain from the Ghanaian perspective.Objective: The key objectives of this study were to determine the determinant of a five-stage downstream oil supply chain and examine the impact of some of the identified determinants on the five-stage downstream oil performance.Method: The study adopted a quantitative research approach, and a closed-ended questionnaire was used to collect primary data from five-stage downstream oil supply chain players. The data collected was screened, coded and complied in Microsoft excel and analysed statistically using Statistical Package for the Social Sciences (SPSS) 20. A preliminary test was conducted to ascertain the data distribution (mean and standard deviation [SD}), reliability (Cronbach’s alpha) and validity (ANOVA [analysis of variance]). The logistics regression was applied to examine the impact of the determinant on the five-stage downstream oil performance.Results: The results suggest that government interference, human capital, collaborations, transportation issues amongst others are the determinants that affects the five-stage downstream oil supply chain. The logistics regression results revealed that each sector’s performance contributes significantly to the sustenance of the five-stage supply chain. Besides, government interference (political stability and expand industry growth) and human capital (educational level and training programmes) are the factors that affect the five-stage downstream supply chain. But as for collaboration, none of the factors adopted affect the five-stage downstream oil supply chain.Conclusion: In summary, the downstream oil supply chain player agrees that the sector’s performance is steady within the entire chain. However, government interference significantly affects downstream oil supply chain performance negatively. Besides, players within the five-stage downstream supply chain neglect collaboration (trust, information sharing, enabling technology and incentive alignment) and pay less or no attention to cooperation which is the cornerstone of daily operational improvement of the five-stage downstream supply chain.

Designing and planning the downstream oil supply chain under uncertainty using a fuzzy programming approach

This paper addresses the strategic and tactical planning of a downstream oil supply chain (DOSC) subject to different sources of uncertainty. This problem is formulated as a mixed-integer linear programming (MILP) model, whereas uncertainty is tackled using chance constrained programming with fuzzy parameters. The MILP model aims at determining the network design and the products distribution plan in a cost-effective way. A real case study on the Brazilian oil industry is used to validate the model. The proposed model shows to be a valuable decision-support tool in order to aid the decision-making process in the strategic and tactical planning of real-life problems.

A graph modeling framework to design and plan the downstream oil supply chain

AbstractIn this paper, a deterministic mixed‐integer linear programming (MILP) model is proposed aiming at designing and planning the downstream oil supply chain. The optimization model defines the strategic network design, and then determines its tactical distribution plan. A multimodal transportation network is considered in which road, maritime, rail, and pipeline resources may be chosen. The network structure is represented through a graph modeling approach, so the diverse network operations, for example, the exchange of products between warehouses, may be readily incorporated into the optimization model. A case study based on the Brazilian oil industry is explored and analyzed to check and demonstrate the adequacy of the MILP model to solve a real‐world problem.

Emission-cost nexus optimisation and performance analysis of downstream oil supply chains

The aim of this study is to develop a novel framework for the optimisation of downstream oil supply chains with emission-cost nexus and performance analysis. A P-graph based model has been developed to support the decisions of the distribution plan in the supply chain. This created the possibility to consider constraints, including the material balance, the sending capacity of refineries and storage depots, and the transport capacity of railways, barges and pipelines. Three scenarios, covering standard conditions, pipeline disruption, and demand increase, are analysed. The proposed novel method supports the decision-making of downstream oil supply chains and provides insight into the reduction of environmental influences. The results reveal that priority should be given to measures for reducing NOx emission (both Greenhouse and dangerous pollution gas) and the emission factors of barge transport, as this can bring great environmental benefit. When NOx emission for railways and barges is reduced by 5%, 1,435 kCNY/month could be saved. If the emission of the barges is reduced by 5%, this can save 1,289 kCNY/month of environmental cost. The proposed method is handy for analysis under non-standard conditions, such as transport facility disruption and demand increase. It would help in the design a distribution plan from a more comprehensive view, and to provide further direction for reducing the environmental footprint.

Future scenario of China's downstream oil reform: Improving the energy-environmental efficiency of the pipeline networks through interconnectivity

Improving energy supply efficiency and quality is regarded as a key pathway to shifting towards a fully sustainable energy system. To address the low efficiency and high emissions in the downstream oil industry caused by the problem of vertically integrated monopoly, the Chinese government is making an effort to promote a multiproduct pipeline network reform. The fundamental and indispensable step for this goal is the pipeline network interconnectivity. This paper quantifies the energy-environmental impacts of the pipeline network interconnectivity reform on China's downstream oil supply chain to 2030. An integrated framework is developed to obtain the detailed design-scale information required for assessment, introducing demand forecasting and demand reallocation into a pipeline network optimal planning model. The model is formulated as a fuzzy mixed integer linear programming that optimizes the infrastructure development scheme and supply chain operation plan simultaneously while taking into account demand uncertainty. The results show that, compared with the baseline, the pipeline interconnectivity reform could reduce yearly energy consumption and CO2 emissions by 9.7–19.8% and 12.5–17.9%, respectively. It is shown that this reform policy could overcome infrastructure constraints, increase pipeline utilization, and improve both energy and environmental performance. The proposed framework can be a theoretical guideline for policymakers within and beyond China.

Downstream oil supply security in China: Policy implications from quantifying the impact of oil import disruption

Unavoidable disruptions in the international oil supply create heavy pressures on the downstream oil market for oil-importing countries, especially China, whose high dependency on imported oil makes its downstream oil supply security an urgent issue. However, little attention has been paid to the supply security of the downstream oil industry with respect to dealing with import disruptions. Here, an integrated quantitative assessment framework is established to identify the impact of crude oil import disruptions on downstream oil supply security from the supply chain perspective. The dynamic scheduling process of the downstream oil supply chain under 72 import disruption scenarios is simulated by coupling a mathematical programming model with Latin hypercube sampling. The improved grey incidence evaluation method is applied for the comprehensive assessment of province-level and region-level oil product supply security based on the simulation results. The case of 31 provincial-level administrative regions in China in 2017 and 2020 is analysed as an example to demonstrate the framework. We find significant differences among provinces. The southwest and eastern coastal areas of China are vulnerable to potential oil supply disruptions, while the northwest China enjoys high supply security. Based on the results of this paper, policy implications are provided for the Chinese government. The design framework is not case-specific and can be applied for the quantitative assessment of other countries or regions.

Future scenario of China’s downstream oil supply chain: Low carbon-oriented optimization for the design of planned multi-product pipelines

Abstract With the increasing demand of refined products and growing concern about carbon emissions, the planned multi-product pipelines, serving as the primary way of refined products transportation, should be designed reasonably, economically, and low carbon-oriented so that the sustainable and environmentally friendly production and the rational construction of downstream oil supply chain can be achieved. Aiming at this issue, a multi-objective mixed-integer linear programming (MILP) model is proposed to minimize the total economic costs and CO2 emission simultaneously. Various actual process and technical constraints including pipeline construction, pump stations layout, pipeline hydraulic and pumps configurations are considered in the model. Based on the augmented e-constraint method, the conflicting objectives are dealt with and the Pareto front can be get. Finally, based on a predicted future scenario of regional refined products demand, the proposed model was successfully applied to a real-word planned multi-product pipeline in China. Two cases are given to demonstrate the model’s applicability and the influence of CO2 emission objective on the pipeline design scheme determination. Meanwhile, the trade-off between the two objectives was analyzed in detail.

Future scenario of China's downstream oil supply chain: An energy, economy and environment analysis for impacts of pipeline network reform

In China, state-owned monopoly on multiproduct pipeline network leads to low energy efficiency, expensive logistics costs and high greenhouse gas emissions in the downstream oil supply chain. In response to increasing pressures applied by the conflict between supply and demand and the need for emission mitigation, the government has promoted pipeline network reform for years. However, the selection of an appropriate reform scheme is controversial, and no researcher has conducted quantitative analysis on this issue. This paper quantifies the impacts of pipeline network reform on the downstream oil supply chain of China so as to provide policy implications. An integrated evaluation framework is established by coupling a mathematical programming technique with the energy-economy-environment (3E) assessment method. Five future scenarios are designed including the baseline (BAU) and four types of reform policies: third-party access (TPA), pipeline network integration (PNI), business cooperation (BC) and third-party access combined with business cooperation (TPA-BC). The results indicate that (i) all reform schemes have significant benefits for aspects of 3E; (ii) the scenarios can be ranked by 3E performance as follows: TPA-BC > BC > PNI > TPA > BAU; and (iii) only when the pipeline networks need to reserve a high capacity margin can the advantages of PNI compared to TPA be revealed; otherwise, the gap between them is small.

Optimisation of a downstream oil supply chain with new pipeline route planning

The planning of downstream oil supply chains (DOSCs) is important to guarantee that the demand of retail markets (RMs) can be satisfied. As an economic method for refined oil transport, pipelines are widely used in many DOSCs. The distribution cost can be reduced, and the efficiency can be improved by constructing new pipelines in a DOSC. In this paper, an MILP model that optimises a DOSC with new pipeline route planning is developed. Constraints for refined oil distribution and pipeline route planning are proposed. The model also considers the transport between storage depots (SDs) and makes it practical for the real-world application of a DOSC in China. Based on the current DOSC, the routes of new pipelines and supply chain planning after new pipeline construction can be obtained. This method is applied to a real DOSC case in China, and two situations are analysed. This method can also help decision makers conduct optimal pipeline route planning and create distribution plans for other DOSCs.

Stochastic programming approach for the optimal tactical planning of the downstream oil supply chain

This paper develops a multistage stochastic programming to optimally solve the distribution problem of refined products. The stochastic model relies on a time series analysis, as well as on a scenario tree analysis, in order to effectively deal and represent uncertainty in oil price and demand. The ARIMA methodology is explored to study the time series of the random parameters aiming to provide their future outcomes, which are then used in the scenario-based approach. As the designed methodology leads to a large scale optimization problem, a scenario reduction approach is employed to compress the problem size and improve its computational performance. A real-world example motivates the case study, based on the downstream oil supply chain of mainland Portugal, which is used to validate the applicability of the stochastic model. The results explicitly indicate the performance of the designed approach in tackling large and complex problems, where uncertainty is present.

Multi-objective optimization model for a downstream oil and gas supply chain

• Tactical decisions in downstream oil and gas supply chains have a major impact world energy supply. • A new multi-objective optimization model to address the above important decisions is developed. • A real case from the largest oil producing country is used to demonstrate the viability of the model. • The model has the capability of providing interesting managerial insights. 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.

Creating sustainable supply chain for palm oil

Palm oil is a key commodity in the oil and fat sector and sustainability of the supply chain can only be achieved with buyers and sellers engaging in appropriate practices. Sustainability centric sellers allocate resources and change processes for sustainable productions whilst buyers arrest opportunistic behaviours and look beyond profit. Private and public bodies render governance of the chain by developing generally accepted sustainable standards, encourage compliance reporting and provide alternative dispute resolution platform. Hence, the conceptual model for creation of sustainable supply chain for palm oil is made of sellers, buyers and governance anchored by the underpinning mitigation of risk theory. As the downstream palm oil supply chain is extensive, used for food and non-food, the sustainable practices need to be focused at the plantations and procurement of crude oil. The findings reveal that sustainable supply chain can only be achieved with the integrated effort of sellers, buyers and the private-public governance as dictated by market dynamism.

Downstream oil supply chain management: A critical review and future directions

Abstract The oil industry has been playing a particular role in the modern economy, acting globally in different countries within competitive business environments. Due to the complexity of the oil supply chain, the associated decision making process is a difficult task, which involves numerous elements from oil supply, going through oil refining, up to oil product distribution. Thus, decision-support tools are often required to assist the decision making in the context of the oil supply chain. The improvement of such decision-support tools is a continuous goal for corporations. From this background, this work aims to review the scientific production about the application of mathematical programming techniques to the distribution problems, faced by diverse entities in the downstream oil supply chain. The main objectives are to point out main contributions, besides identifying the major voids and new trends in order to establish an agenda for future research directions.