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Biobjective optimization for general oil field development

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2014年 119卷 123-138页

作者： Isebor, Obiajulu J. Durlofsky, Louis J. Stanford Univ Dept Energy Resources Engn Stanford CA 94305 USA

The optimization of oil field development and production planning typically requires the consideration of multiple, possibly conflicting, objectives. For example, in a waterflooding project, we might seek to maximize oil recovery and minimize water injection. It is therefore important to devise and test optimization procedures that consider two or more objectives in the determination of optimal development and production plans. In this work we present an approach for field development optimization with two objectives. A single-objective product formulation, which systematically combines the two objectives in a sequence of single-objective optimization problems, is applied. The method, called BiPSOMADS, utilizes at its core our recently developed PSO-MADS (Particle Swarm Optimization-Mesh Adaptive Direct Search) hybrid optimization algorithm. This derivative-free procedure has been shown to be effective for the solution of generalized field development and well control problems that include categorical, discrete and continuous variables along with general (nonlinear) constraints. Four biobjective field development and well control examples are solved using BiPSOMADS. These examples include problems that consider the maximization of both net present value and cumulative oil production, and the maximization of both long-term and short-term reservoir performance. An example that highlights the applicability of biobjective optimization for field development under geological uncertainty is also presented. This usage of BiPSOMADS enables us to maximize expected reservoir performance while reducing the risk associated with the worst-case scenario. (C) 2014 Elsevier B.V. All rights reserved.

关键词： biobjective optimization derivative-free optimization petroleum field development mixed-integer nonlinear programming multiobjective programming optimization under uncertainty

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Liner ship route schedule design with port time windows

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2014年 41卷 1-17页

作者： Wang, Shuaian Alharbi, Abdurahim Davy, Pam Univ Wollongong Sch Math & Appl Stat Wollongong NSW 2522 Australia

This paper examines a practical tactical liner ship route schedule design problem, which is the determination of the arrival and departure time at each port of call on the ship route. When designing the schedule, the availability of each port in a week, i.e., port time window, is incorporated. As a result, the designed schedule can be applied in practice without or with only minimum revisions. This problem is formulated as a mixed-integer nonlinear nonconvex optimization model. In view of the problem structure, an efficient holistic solution approach is proposed to obtain global optimal solution. The proposed solution method is applied to a trans-Atlantic ship route. The results demonstrate that the port time windows, port handling efficiency, bunker price and unit inventory cost all affect the total cost of a ship route, the optimal number of ships to deploy, and the optimal schedule. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Container liner shipping Schedule design Containership scheduling Port time windows mixed-integer nonlinear programming

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Strong-branching inequalities for convex mixed integer nonlinear programs

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COMPUTATIONAL OPTIMIZATION AND APPLICATIONS 2014年第3期59卷 639-665页

作者： Kilinc, Mustafa Linderoth, Jeff Luedtke, James Miller, Andrew Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA Univ Wisconsin Dept Ind & Syst Engn Madison WI 53706 USA United Parcel Serv Atlanta GA 30328 USA

Strong branching is an effective branching technique that can significantly reduce the size of the branch-and-bound tree for solving mixed integer nonlinear programming (MINLP) problems. The focus of this paper is to demonstrate how to effectively use "discarded" information from strong branching to strengthen relaxations of MINLP problems. Valid inequalities such as branching-based linearizations, various forms of disjunctive inequalities, and mixing-type inequalities are all discussed. The inequalities span a spectrum from those that require almost no extra effort to compute to those that require the solution of an additional linear program. In the end, we perform an extensive computational study to measure the impact of each of our proposed techniques. Computational results reveal that existing algorithms can be significantly improved by leveraging the information generated as a byproduct of strong branching in the form of valid inequalities.

关键词： mixed-integer nonlinear programming Strong-branching Disjunctive inequalities Mixing inequalities

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Global optimization of general non-convex problems with intermediate bilinear substructures

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OPTIMIZATION METHODS & SOFTWARE 2014年第3期29卷 442-462页

作者： Zorn, Keith Sahinidis, Nikolaos V. Carnegie Mellon Univ Dept Chem Engn Pittsburgh PA 15213 USA

This work considers the global optimization of general non-convex nonlinear and mixed-integer nonlinear programming (MINLP) problems with underlying bilinear substructures. We combine reformulation-linearization techniques and advanced convex envelope construction techniques to produce tight subproblem formulations for these underlying structures. When incorporated as linear cutting planes, these relaxation strengthening strategies are highly effective at tightening standard linear programming relaxations generated by factorable programming techniques. Because the size of these augmented linear relaxations increases exponentially with the number of variables, we employ cut filtering and selection strategies to ensure that the tightened subproblems are solved efficiently. We introduce algorithms for bilinear substructure detection, cutting plane identification, cut filtering, and cut selection and embed the proposed implementation in Branch-and-Reduce Optimization Navigator at every node in the branch-and-bound tree. A computational study including problem instances from standard literature test libraries is included to assess the performance of the proposed implementation. Results show that underlying bilinear substructures are identified in 30% of the problems in GLOBALLib and MINLPLib and that the exploitation of these structures significantly reduces computational time, branch-and-bound tree size, and required memory.

关键词： branch-and-bound global optimization factorable polyhedral relaxation reformulation-linearization techniques mixed-integer nonlinear programming

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A mixed-integer nonlinear program for the optimal design and dispatch of distributed generation systems

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OPTIMIZATION AND ENGINEERING 2014年第1期15卷 167-197页

作者： Pruitt, Kristopher A. Leyffer, Sven Newman, Alexandra M. Braun, Robert J. Colorado Sch Mines Div Econ & Business Golden CO 80401 USA Argonne Natl Lab Math & Comp Sci Div Argonne IL 60439 USA Colorado Sch Mines Dept Mech Engn Golden CO 80401 USA

Maturing distributed generation (DG) technologies have promoted interest in alternative sources of energy for commercial building applications due to their potential to supply on-site heat and power at a lower cost and emissions rate compared to centralized generation. Accordingly, we present an optimization model that determines the mix, capacity, and operational schedule of DG technologies that minimize economic and environmental costs subject to the heat and power demands of a building and to the performance characteristics of the technologies. The technologies available to design the system include lead-acid batteries, photovoltaic cells, solid oxide fuel cells, heat exchangers, and a hot water storage tank. Modeling the acquisition and operation of discrete technologies requires integer restrictions, and modeling the variable electric efficiency of the fuel cells and the variable temperature of the tank water introduces nonlinear equality constraints. Thus, our optimization model is a nonconvex, mixed-integer nonlinear programming (MINLP) problem. Given the difficulties associated with solving large, nonconvex MINLPs to global optimality, we present convex underestimation and linearization techniques to bound and solve the problem. The solutions provided by our techniques are close to those provided by existing MINLP solvers for small problem instances. However, our methodology offers the possibility to solve large problem instances that exceed the capacity of existing solvers and that are critical to the real-world application of the model.

关键词： Global optimization mixed-integer nonlinear programming Distributed generation Combined heat and power

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Selecting different raw materials for methanol production using an MINLP model

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JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2014年 19卷 221-227页

作者： Kralj, Anita Kovac Univ Maribor Fac Chem & Chem Engn SLO-2000 Maribor Slovenia

This paper aims at selecting different raw materials, using an MINLP (mixed-integer nonlinear programming) model, when considering the usage of more favourable raw materials for methanol production. The best selection of raw material alternatives was sought for methanol production. Methanol is produced from synthesis gas that is produced from different raw materials - natural gas or biogas. The basic starting point when comparing them is the same mass inlet flow rate for both raw materials. Methanol production was simulated for both natural gas or biogas as the raw material, using an Aspen Plus simulator with a real chemical thermodynamic. Methanol production can be enlarged by simultaneous structuring such as selecting the usage of more favourable raw materials, and parameter optimisation using the MINLP. The selection of raw methanol with optimal parameters has the greatest impact on higher methanol production. Optimal methanol conversion can take place during this operation, by applying optimal parametric data within a reformer unit (temperature = 840 degrees C and pressure = 8 bar), using natural gas. The optimal production of methanol from natural gas was 17 510 kg/h under optimal parameters for 8.4% higher production than under existing parameters. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Methanol production Natural gas Biogas Mathematical model mixed-integer nonlinear programming

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Symmetry-exploiting cuts for a class of mixed-0/1 second-order cone programs

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DISCRETE OPTIMIZATION 2014年 13卷 23-35页

作者： Drewes, Sarah Pokutta, Sebastian Georgia Inst Technol ISyE Atlanta GA 30332 USA

We will analyze mixed-0/1 second-order cone programs where the continuous and binary variables are solely coupled via the conic constraints. We devise a cutting-plane framework based on an implicit Sherali-Adams reformulation. The resulting cuts are very effective as symmetric solutions are automatically cut off and each equivalence class of 0/1 solutions is visited at most once. Further, we present computational results showing the effectiveness of our method and briefly sketch an application in optimal pooling of securities. (C) 2014 Elsevier B.V. All rights reserved.

关键词： mixed-integer nonlinear programming Cutting planes Second order cone programming Outer approximation

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An Outer-Inner Approximation for Separable mixed-integer nonlinear Programs

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INFORMS JOURNAL ON COMPUTING 2014年第1期26卷 31-44页

作者： Hijazi, Hassan Bonami, Pierre Ouorou, Adam Natl ICT Australia Canberra ACT 2601 Australia Univ Marseille Fac Sci Luminy Lab Informat Fondamentale F-13009 Marseille France Orange Labs R&D CORE TPN F-92794 Issy Les Moulineaux 9 France

A common structure in convex mixed-integer nonlinear programs (MINLPs) is separable nonlinear functions. In the presence of such structures, we propose three improvements to the outer approximation algorithms. The first improvement is a simple extended formulation, the second is a refined outer approximation, and the third is a heuristic inner approximation of the feasible region. As a side result, we exhibit a simple example where a classical implementation of the outer approximation would take an exponential number of iterations, whereas it is easily solved with our modifications. These methods have been implemented in the open source solver BONMIN and are available for download from the Computational Infrastructure for Operations Research project website. We test the effectiveness of the approach on three real-world applications and on a larger set of models from an MINLP benchmark library. Finally, we show how the techniques can be extended to perspective formulations of several problems. The proposed tools lead to an important reduction in computing time on most tested instances.

关键词： mixed-integer nonlinear programming outer approximation

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Energy-Optimal Multi-Goal Motion Planning for Planar Robot Manipulators

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2014年第1期163卷 80-104页

作者： Bonami, Pierre Olivares, Alberto Staffetti, Ernesto Ctr Natl Rech Sci Paris France Univ Rey Juan Carlos Madrid Spain

In this work, the energy-optimal motion planning problem for planar robot manipulators with two revolute joints is studied, in which the end-effector of the robot manipulator is constrained to pass through a set of waypoints, whose sequence is not predefined. This multi-goal motion planning problem has been solved as a mixed-integer optimal control problem in which, given the dynamic model of the robot manipulator, the initial and final configurations of the robot, and a set of waypoints inside the workspace of the manipulator, one has to find the control inputs, the sequence of waypoints with the corresponding passage times, and the resulting trajectory of the robot that minimizes the energy consumption during the motion. The presence of the waypoint constraints makes this optimal control problem particularly difficult to solve. The mixed-integer optimal control problem has been converted into a mixed-integer nonlinear programming problem first making the unknown passage times through the waypoints part of the state, then introducing binary variables to enforce the constraint of passing once through each waypoint, and finally applying a fifth-degree Gauss-Lobatto direct collocation method to tackle the dynamic constraints. High-degree interpolation polynomials allow the number of variables of the problem to be reduced for a given numerical precision. The resulting mixed-integer nonlinear programming problem has been solved using a nonlinear programming-based branch-and-bound algorithm specifically tailored to the problem. The results of the numerical experiments have shown the effectiveness of the approach.

关键词： Multi-goal motion planning Robot manipulators mixed-integer optimal control problem mixed-integer nonlinear programming

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SOLVING THE nonlinear DISCRETE TRANSPORTATION PROBLEM BY MINLP OPTIMIZATION

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TRANSPORT 2014年第1期29卷 1-11页

作者： Klansek, Uros Univ Maribor Fac Civil Engn Maribor Slovenia

The nonlinear Discrete Transportation Problem (NDTP) belongs to the class of the optimization problems that are generally difficult to solve. The selection of a suitable optimization method by which a specific NDTP can be appropriately solved is frequently a critical issue in obtaining valuable results. The aim of this paper is to present the suitability of five different mixed-integer nonlinear programming (MINLP) methods, specifically for the exact optimum solution of the NDTP. The evaluated MINLP methods include the extended cutting plane method, the branch and reduce method, the augmented penalty/outer-approximation/equality-relaxation method, the branch and cut method, and the simple branch and bound method. The MINLP methods were tested on a set of NDTPs from the literature. The gained solutions were compared and a correlative evaluation of the considered MINLP methods is shown to demonstrate their suitability for solving the NDTPs.

关键词： transportation problems discrete transporting flows nonlinear cost functions optimization methods mixed-integer nonlinear programming

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