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

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

doi:10.1016/j.compchemeng.2017.09.012

Cited by 63 publications

(27 citation statements)

References 22 publications

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“…Researchers have done some relevant studies on the design and planning of the oil supply chain . The mathematical models developed in their studies were used as efficient tools to determine the distribution plans.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Researchers have done some relevant studies on the design and planning of the oil supply chain. [28][29][30][31][32] The mathematical models developed in their studies were used as efficient tools to determine the distribution plans. While some studies [33][34][35][36] applied sampling methods such as the Monte Carlo method to sample the uncertain data in the optimization of the oil supply chain, the above studies can be the references for our paper to develop the mathematical model that can optimize the distribution plan of the RPSC.…”

Section: Literature Reviewmentioning

confidence: 99%

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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: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

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 ARO model is applied to a real case study on a refined products network distribution in the Portuguese oil industry, which was originally characterized by Lima et al [1]. Figure 1 represents the network under study.…”

Section: Case Studymentioning

confidence: 99%

“…In the oil industry, companies develop a set of activities and processes so that crude oil can be duly transformed into oil products demanded by final consumers such as petrochemical industries, airports and individual users [1]. The associated decision-making process is framed within a deeply complex context and addresses the oil exploration, production and transportation at upstream, oil refining at midstream, and oil product distribution and marketing at the downstream segments [2].…”

Section: Introductionmentioning

confidence: 99%

“…In general, the choice of the mathematical modeling framework to solve a particular problem under uncertainty relies on the specific characteristics of this problem, as well as on the level of information about uncertainty.The use of decision-support tools specifically for the DOSC problem under uncertainty has been a research challenge [11], and different approaches have been proposed in this direction. 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].…”

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confidence: 99%

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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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Integrated Optimization Model for the Fuel Supply Chain of a State Company (SC) in Brazil

Bottino

Leal

2019

Operations Management for Social Good

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

Cited by 63 publications

References 22 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

Adjustable Robust Optimization for Planning Logistics Operations in Downstream Oil Networks

Integrated Optimization Model for the Fuel Supply Chain of a State Company (SC) in Brazil

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