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

Lima, Camilo; Relvas, Susana; Barbosa‐Póvoa, Ana Paula

doi:10.1016/j.compchemeng.2016.05.002

Cited by 71 publications

(37 citation statements)

References 56 publications

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“…Accurate reliability evaluation and analysis of the RPSC system are of great practical significance for reducing system risks. There are mainly two aspects that will influence the operation of RPSCs, one is the fluctuation of demand and production, and the other is the disruption of refineries, interruption of pipelines, and other huge accidents caused by natural disasters or human‐induced incidences . Considering these factors comprehensively, the evaluation of the reliability of the supply chain can be realized, which will help to understand the operational level of the supply chain and enhance emergency support.…”

Section: Introductionmentioning

confidence: 99%

Sustainable refined products supply chain: A reliability assessment for demand‐side management in primary distribution processes

Wang

Qiu

Yuan

et al. 2019

Energy Science & Engineering

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The reliable operation of the primary distribution of refined products supply chains is crucial in guaranteeing the demand for retail markets. The uncertainties such as demand, production, and inventory will affect the supply chain operation. However, there lacks literature that studies the reliability of demand‐side management of refined products supply chains. This reliability assessment can help define the weak points and boost the performance of the supply chain. And it can help decision‐makers to improve the satisfaction of retail markets. In this paper, a systematic method for comprehensively assessing the reliability of refined products supply chains is proposed. At first, a mathematical programming model is developed to get the distribution plan of refined products. Then, several scenarios such as increasing demand, decreasing production, refineries disruption, and pipeline interruption relevant to the real situations of refined products supply chains are defined. Besides, indices including satisfaction rate, number of dissatisfied retail markets, refined products shortage, shortage of a single retail market, and average shortage duration are proposed to evaluate the reliability of the demand‐side management in primary distribution processes. At last, the Monte Carlo simulation is adopted for a comprehensive analysis combining all the scenarios. This analysis shows a clear picture of the influence of uncertainties. This method is applied to assess the reliability of demand‐side management of a real refined products supply chain. After analyzing different scenarios, the average probability of dissatisfaction under the reference scenario came as 1.93 × 10−4/day that means one day in around 14.2 years could have refined products shortage. This method can also help decision‐makers to assess other primary distribution of refined products supply chains.

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Section: Introductionmentioning

confidence: 99%

Sustainable refined products supply chain: A reliability assessment for demand‐side management in primary distribution processes

Wang

Qiu

Yuan

et al. 2019

Energy Science & Engineering

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“…It encompasses two major distribution paths in the chain: shipping of light products, such as gasoline and diesel, from refineries to distribution terminals; and shipping of heavy products to industrial customers and lube plants [1,2]. In distribution terminals, tank trucks are loaded with specific fuel types in order to meet customer's demand.…”

Section: Introductionmentioning

confidence: 99%

Simulation of Tank Truck Loading Operations in a Fuel Distribution Terminal

Reis¹,

Pitombeira-Neto²,

Rolim³

2017

Int. j. simul. model.

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Tank truck loading operations in fuel distribution terminals have particular characteristics which are challenging to simulate. Demands for different fuel types are correlated rather than independent. In addition, queuing policies which do not address the current state of the terminal may increase equipment idleness and trucks' lead time. We propose a decomposition of the joint probability distribution of fuel types and a procedure to simulate it. We also propose a new queuing policy aimed at reducing equipment idleness and trucks' lead time. The procedures are tested and validated through a discrete-event simulation model of a real fuel distribution terminal. We simulate alternative scenarios in which we increase fuel flow rate as well as the number of loading arms and test a new queuing policy. Results show the effectiveness of the simulation procedure in replicating real performance indicators. Moreover, the proposed idleness-based queuing policy resulted in lower idleness and lead time compared with a first-come-first-served policy.

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“…These include stochastic programming [1,2,4,8,[12][13][14][15][16][17][18][19][20][21]; fuzzy programming [22,23]; and robust optimization [16,24]. In these optimization models, demand, supply, price, cost, process capacity, product yield, conversion rate and technology development are regarded as uncertain parameters [3].Nowadays, RO has become attractive to solve optimization problems under uncertainty as it only requires the mean and range of the uncertain data, and other probabilistic information can be gradually incorporated when it becomes available [25]. In addition, it balances adequately solution quality, feasibility and computational performance [26], being formulated to hedge the optimization problem against any disturbances in the input data series [27].…”

mentioning

confidence: 99%

Adjustable Robust Optimization for Planning Logistics Operations in Downstream Oil Networks

et al. 2019

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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.

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

Cited by 71 publications

References 56 publications

Sustainable refined products supply chain: A reliability assessment for demand‐side management in primary distribution processes

Sustainable refined products supply chain: A reliability assessment for demand‐side management in primary distribution processes

Simulation of Tank Truck Loading Operations in a Fuel Distribution Terminal

Adjustable Robust Optimization for Planning Logistics Operations in Downstream Oil Networks

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