ALTERNATE MODELS FOR NATURAL GAS TRANSPORTATION SYSTEM PERFORMANCE OPTIMIZATION

The Natural Gas market in the U.S is growing rapidly with evidence that the nation has enough shale reserves to power the country for the next century. To ensure continued economic benefits through the use of this environmentally desired energy source, it becomes important to optimize the transportation network system design. Transportation through pipelines is one of the most common methods used to distribute Natural Gas from source to destination. This transportation system, consisting of pipelines, compressors and other supporting equipment, must be optimized, considering all relevant parameters to minimize cost and increase profit. The research presented here improves on the fuel cost minimization models in literature to incorporate pipeline elevation and safety requirements. A new model is proposed to consider the entire transportation network as a single system and optimize it considering all relevant parameters. The optimization model is setup as a mixed integer nonlinear program. The proposed model is used to optimize the pipeline network for a case study, evaluate the model as well as investigate design capacity and installed capacity of pipeline network

A MULTI-COMMODITY NETWORK FLOW APPROACH FOR SEQUENCING REFINED PRODUCTS IN PIPELINE SYSTEMS

In the oil industry, there is a special class of pipelines used for the transportation of refined products. The problem of sequencing the inputs to be pumped through this type of pipeline seeks to generate the optimal sequence of batches of products and their destination as well as the amount of product to be pumped such that the total operational cost of the system, or another operational objective, is optimized while satisfying the product demands according to the requirements set by the customers. This dissertation introduces a new modeling approach and proposes a solution methodology for this problem capable of dealing with the topology of all the scenarios reported in the literature so far. The system representation is based on a 1-0 multi commodity network flow formulation that models the dynamics of the system, including aspects such as conservation of product flow constraints at the depots, travel time of products from the refinery to their depot destination and what happens upstream and downstream the line whenever a product is being received at a given depot while another one is being injected into the line at the refinery. It is assumed that the products are already available at the refinery and their demand at each depot is deterministic and known beforehand. The model provides the sequence, the amounts, the destination and the trazability of the shipped batches of different products from their sources to their destinations during the entire horizon planning period while seeking the optimization of pumping and inventory holding costs satisfying the time window constraints. A survey for the available literature is presented. Given the problem structure, a decomposition based solution procedure is explored with the intention of exploiting the network structure using the network simplex method. A branch and bound algorithm that exploits the dynamics of the system assigning priorities for branching to a selected set of variables is proposed and its computational results for the solution, obtained via GAMS/CPLEX, of the formulation for random instances of the problem of different sizes are presented. Future research directions on this field are proposed

Locating a bioenergy facility using a hybrid optimization method

In this paper, the optimum location of a bioenergy generation facility for district energy applications is sought. A bioenergy facility usually belongs to a wider system, therefore a holistic approach is adopted to define the location that optimizes the system-wide operational and investment costs. A hybrid optimization method is employed to overcome the limitations posed by the complexity of the optimization problem. The efficiency of the hybrid method is compared to a stochastic (genetic algorithms) and an exact optimization method (Sequential Quadratic Programming). The results confirm that the hybrid optimization method proposed is the most efficient for the specific problem. (C) 2009 Elsevier B.V. All rights reserved

Cost Factor Focused Scheduling and Sequencing: A Neoteric Literature Review

The hastily emergent concern from researchers in the application of scheduling and sequencing has urged the necessity for analysis of the latest research growth to construct a new outline. This paper focuses on the literature on cost minimization as a primary aim in scheduling problems represented with less significance as a whole in the past literature reviews. The purpose of this paper is to have an intensive study to clarify the development of cost-based scheduling and sequencing (CSS) by reviewing the work published over several parameters for improving the understanding in this field. Various parameters, such as scheduling models, algorithms, industries, journals, publishers, publication year, authors, countries, constraints, objectives, uncertainties, computational time, and programming languages and optimization software packages are considered. In this research, the literature review of CSS is done for thirteen years (2010-2022). Although CSS research originated in manufacturing, it has been observed that CSS research publications also addressed case studies based on health, transportation, railway, airport, steel, textile, education, ship, petrochemical, inspection, and construction projects. A detailed evaluation of the literature is followed by significant information found in the study, literature analysis, gaps identification, constraints of work done, and opportunities in future research for the researchers and experts from the industries in CSS

Design optimization of oilfield subsea infrastructures with manifold placement and pipeline layout

This work presents a practical and effective optimization method to design subsea production networks, which accounts for the number of manifolds and platforms, their location, well assignment to these gathering systems, and pipeline diameter. It brings a fast solution that can be easily implemented as a tool for layout design optimization and simulation-based analysis. The proposed model comprises reservoir dynamics and multiphase flow, relying on multidimensional piecewise linearization to formulate the layout design problem as a MILP. Besides design validation, reservoir simulation serves the purpose of defining boundaries for optimization variables and parameters that characterize pressure decrease, reservoir dynamics and well production over time. Pressure drop in pipelines are modeled by piecewise-linear functions that approximate multiphase flow simulators. The resulting optimization model and approximation methodology were applied to a real oilfield with the aim of assessing their effectiveness.Este trabalho apresenta um método de otimização prático e eficaz para o projeto de redes de produção submarinas em campos de petróleo offshore, o que compreende o número de coletores, sejam manifolds ou plataformas, sua localização, atribuição de poços a esses coletores e diâmetro de dutos que interligam todos os elementos da rede. Ele traz uma solução rápida que pode ser facilmente implementada como uma ferramenta para otimização de layout e de estudos baseados em simulação. O modelo proposto compreende a dinâmica do reservatório e fluxo multifásico em dutos, baseando-se na linearização multidimensional por partes para formular o problema de otimização de layout como programação inteira linear mista. Além da validação da solução ótima obtida pelo método, a simulação de reservatórios define limites para as variáveis e parâmetros do modelo que caracterizam a perda de carga, a dinâmica do reservatório e a produção de óleo dos poços ao longo do tempo. A perda de carga nas tubulações é modelada por funções lineares por partes que aproximam resultados obtidos pelos simuladores de fluxo multifásicos. O modelo de otimização foi aplicado a um verdadeiro campo de petróleo offshore com o objetivo de avaliar sua efetividade

Desarrollo de una herramienta de apoyo a la toma de decisiones basado en simulación para la ejecución de la programación detallada de transporte sobre un poliducto en Ecopetrol S.A.

Ingeniero (a) IndustrialPregrad

Operational Research IO2017, Valença, Portugal, June 28-30

This proceedings book presents selected contributions from the XVIII Congress of APDIO (the Portuguese Association of Operational Research) held in Valença on June 28–30, 2017. Prepared by leading Portuguese and international researchers in the field of operations research, it covers a wide range of complex real-world applications of operations research methods using recent theoretical techniques, in order to narrow the gap between academic research and practical applications. Of particular interest are the applications of, nonlinear and mixed-integer programming, data envelopment analysis, clustering techniques, hybrid heuristics, supply chain management, and lot sizing and job scheduling problems. In most chapters, the problems, methods and methodologies described are complemented by supporting figures, tables and algorithms. The XVIII Congress of APDIO marked the 18th installment of the regular biannual meetings of APDIO – the Portuguese Association of Operational Research. The meetings bring together researchers, scholars and practitioners, as well as MSc and PhD students, working in the field of operations research to present and discuss their latest works. The main theme of the latest meeting was Operational Research Pro Bono. Given the breadth of topics covered, the book offers a valuable resource for all researchers, students and practitioners interested in the latest trends in this field.info:eu-repo/semantics/publishedVersio

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing