Oil-derivatives pipeline logistics using discrete-event simulation

Cafaro, Diego Carlos; Méndez,; Cerdá,,

doi:10.1109/wsc.2010.5678864

Cited by 17 publications

(11 citation statements)

References 15 publications

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“…We then refer to research on related problems, such as truck routing, truck scheduling, and simulation of operations in container terminals and mines. Cafaro et al [6] describe a discrete-event simulation (DES) model in order to schedule the delivery of multiple petroleum products from a refinery to a terminal through a pipeline. Cafaro and Cerdá [7] propose a mixed integer linear programming model to dynamically schedule multiproduct pipelines, while Relvas et al [8] develop an integrated approach to scheduling and managing inventory for the distribution of oil products.…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

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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“…Cafaro et al introduced a discrete-event simulation model of a trunk pipeline transporting refined products from a single origin to multiple distribution terminals. It was developed on Arena to simulate the pipeline operational plan generated by the continuous-time mathematical formulation of Cafaro and Cerdá .…”

Section: Heuristic Rules For Selecting the Receiving Terminalmentioning

confidence: 99%

Detailed Scheduling of Operations in Single-Source Refined Products Pipelines

Cafaro

Méndez

et al. 2011

Ind. Eng. Chem. Res.

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The short-term scheduling of refined products pipelines is a very complex problem with many operational constraints to be considered. Available nondiscrete planning approaches just provide the input schedule and the set of aggregate product deliveries to be performed from in-transit lots to depots at every batch injection. To determine the sequence of individual cuts on each batch to be accomplished by the pipeline operator, it is still necessary to refine such an aggregate plan. To do so, new computational tools for efficiently generating the detailed schedule of single-source pipelines with multiple distribution terminals are presented. Two types of methodologies are proposed. On one hand, it is developed a continuous-time mixed-integer linear programming (MILP) formulation that seeks to minimize both the total flow restart volume and the number of single-delivery pumping runs over the planning horizon. In this way, substantial savings in energy consumption and pump maintenance costs are achieved. Effective solution strategies for the MILP model are also designed to deal with large pipeline scheduling problems. On the other hand, three different heuristic rules for selecting the receiving terminal are introduced. By applying those rules in combination with a discrete-event simulation model, not only alternative detailed schedules can be generated in a very short CPU time but also some of them are near-optimal solutions. Three instances of a case study aimed at finding the detailed schedule of a real-world single-source pipeline system are solved through the proposed optimization and discrete-event simulation methods. Results are analyzed to assess the quality of the generated solutions and the required computational costs.

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“…The work suffers from false detection and localization of oil spills due to inefficient tools for analysis of pressure and temperature profiles. (Cafaro et al, 2010) presented oil-derivatives pipeline logistics system with the aim of evolving a computational tool to manage oil products pipeline in a reliable and cost effective manner. The work was limited by lack of capability for efficient priority scheduling for pipes and products delivery.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Neural Network Models for Petroleum Products Pipeline Monitoring

Inyang¹,

Udoh²,

Akinyokun³

2017

SET

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In recent years, Neural Network (NN) has gained popularity in proffering solution to complex nonlinear problems. Monitoring of variations in Petroleum Products Pipeline (PPP) attributes (flow rate, pressure, temperature, viscosity, density, inlet and outlet volume) which changes with time is complex due to existence of non linear interaction amongst the attributes. The existing works on PPP monitoring are limited by lack of capabilities for pattern recognition and learning from previous data. In this paper, NN models with pattern recognition and learning capabilities are compared with a view of selecting the best model for monitoring PPP. Data was collected from Pipelines and Products Marketing Company (PPMC), Port Harcourt, Nigeria. The data was used for NN training, validation and testing with different NN models such as Multilayer Perceptron (MLP), Radial Basis Function (RBF), Generalized Feed Forward (GFF), Support Vector Machine (SVM), Time Delay Network (TDN) and Recurrent Neural Network (RNN). Neuro Solutions 6.0 was used as the front-end-engine for NN training, validation and testing while My Structured Query Language (MySQL) database served as the back-end-engine. Performance of NN models was measured using Mean Squared Error (MSE), Mean Absolute Error (MAE), Correlation Coefficient (r), Akaike Information Criteria (AIC) and Minimum Descriptive Length (MDL). MLP with one hidden layer and three processing elements performed better than other NN models in terms of MSE, MAE, AIC, MDL and r values between the computed and the desired output.

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Oil-derivatives pipeline logistics using discrete-event simulation

Cited by 17 publications

References 15 publications

Simulation of Tank Truck Loading Operations in a Fuel Distribution Terminal

Simulation of Tank Truck Loading Operations in a Fuel Distribution Terminal

Detailed Scheduling of Operations in Single-Source Refined Products Pipelines

Comparative Analysis of Neural Network Models for Petroleum Products Pipeline Monitoring

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