Literature review of oil refineries planning under uncertainty

Leiras, Adriana; Ribas, Gabriela; Hamacher, Śılvio; Elkamel, Ali

doi:10.1504/ijogct.2011.038927

Cited by 22 publications

(20 citation statements)

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“…Two main levels of classification for the uncertainty factors affecting the manufacturing process scheduling can be considered [5]. The first level of classification consists on distinguishing those who are external to the process from those who are internal to the process.…”

Section: General Manufacturing Uncertainty Factorsmentioning

confidence: 99%

“…continuously modify the initial plans and schedules, forcing the operators to change and readapt to these new situations [3]. New ways of incorporating these elements have motivated researchers to propose new approaches to tackle with the uncertainty problem in industrial sectors, such as multistage stochastic programming, robust optimization, or fuzzy programming [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Planning and Scheduling with Uncertainty in the Steel Sector: A Review

et al. 2019

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The following paper proposes a study about the existing solutions for dealing with uncertainty while solving the planning and scheduling problem at steel industry manufacturing processes. The different techniques designed to cope with uncertainty in manufacturing scheduling are discussed, along with the main uncertainty factors affecting the scheduling. The paper proposes a classification for the main uncertainties affecting the steelmaking process and analyzes the existing literature about solutions for the scheduling with uncertainty in the steel sector in terms of approaches followed and uncertainty types considered. Finally, the main remarks and future challenges within this field are presented.

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Section: General Manufacturing Uncertainty Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Planning and Scheduling with Uncertainty in the Steel Sector: A Review

et al. 2019

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“…Leiras et al [1] made a review for oil refineries planning under uncertainties papers from the 90s to 2010 and only 5 out of 22 papers studied the two-stage stochastic programming. Khor et al [2] studied hybrid two-stage stochastic programming and employed sampling method to construct scenarios for the price, demand of finish products and yield uncertainty.…”

Section: Literature Reviewmentioning

confidence: 99%

Scenario Based Two-Stage Stochastic Programming Approach for the Midterm Production Planning of Oil Refinery

2018

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Recently, oil refining industry is facing with lower profit margin due to uncertainty. This causes oil refinery to include stochastic optimization in making a decision to maximize the profit. In the past, deterministic linear programming approach is widely used in oil refinery optimization problems. However, due to volatility and unpredictability of oil prices in the past ten years, deterministic model might not be able to predict the reality of the situation as it does not take into account the uncertainties thus, leads to non-optimal solution. Therefore, this study will develop two-stage stochastic linear programming for the midterm production planning of oil refinery to handle oil price volatility. Geometric Brownian motion (GBM) is used to describe uncertainties in crude oil price, petroleum product prices, and demand for petroleum products. This model generates the future realization of the price and demands with scenario tree based on the statistical specification of GBM using method of moment as input to the stochastic programming. The model developed in this paper was tested for Malaysia oil refinery data. The result of stochastic approach indicates that the model gives better prediction of profit margin.

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“…Uncertainty is usually represented by the variation in the prices inherent in the problem. Leiras et al (2011) summarise the numerous categories of refinery planning tackled in the literature. They show that fewer authors formulate a model with stochastic variables to capture uncertainty.…”

Section: Refinery Planningmentioning

confidence: 99%

A modern two-stage stochastic programming portfolio model for an oil refinery with financial risk management

O'Driscoll

2017

IJOR

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Abstract:The proposal which we wish to make is a two-stage stochastic programming model for a competitive oil refinery with stochastic crude and fuel prices. Most models for refineries are deterministic, and those considering the stochastic problem do so by utilising a Gaussian assumption on profitsimplementing variance as the risk measure. Our model falls into the category of optimisation with coherent risk measures where robustness, rather than ambiguity, is the focus. The objective is to maximise the refiner's profit under raw material, product inventory constraints and a financial risk constraint. The two-stage model leverages off a unique discrete scenario generation technique alongside an admissible and computational tractable drawdown risk measure. The expected value of perfect information calculation of each model gives a value for the additional benefit, which the decision-maker receives in considering the uncertainty inherent in the problem.

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Literature review of oil refineries planning under uncertainty

Cited by 22 publications

References 0 publications

Planning and Scheduling with Uncertainty in the Steel Sector: A Review

Planning and Scheduling with Uncertainty in the Steel Sector: A Review

Scenario Based Two-Stage Stochastic Programming Approach for the Midterm Production Planning of Oil Refinery

A modern two-stage stochastic programming portfolio model for an oil refinery with financial risk management

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