Well placement and control optimization in oil field development are commonly performed in a sequential manner. In this work, we propose a joint approach that embeds well control optimization within the search for opt...
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Well placement and control optimization in oil field development are commonly performed in a sequential manner. In this work, we propose a joint approach that embeds well control optimization within the search for optimum well placement configurations. We solve for well placement using derivative-free methods based on pattern search. Control optimization is solved by sequential quadratic programming using gradients efficiently computed through adjoints. Joint optimization yields a significant increase, of up to 20% in net present value, when compared to reasonable sequential approaches. The joint approach does, however, require about an order of magnitude increase in the number of objective function evaluations compared to sequential procedures. This increase is somewhat mitigated by the parallel implementation of some of the pattern-search algorithms used in this work. Two pattern-search algorithms using eight and 20 computing cores yield speedup factors of 4.1 and 6.4, respectively. A third pattern-search procedure based on a serial evaluation of the objective function is less efficient in terms of clock time, but the optimized cost function value obtained with this scheme is marginally better.
We address the discrete network design problem (DNDP) which determines the optimal number of lanes to add to each candidate link in a road network. We formulate the problem as a bi-level programming model, where the u...
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ISBN:
(纸本)9789881581419
We address the discrete network design problem (DNDP) which determines the optimal number of lanes to add to each candidate link in a road network. We formulate the problem as a bi-level programming model, where the upper level aims to minimize the total travel time via adding new lanes to candidate links and the lower level is a traditional Wardrop user equilibrium (UE) problem. We propose a global optimization method that is based on the system-optimum relaxation of the UE model. Numerical examples are given.
Branch-and-Bound (B&B) is perhaps the most fundamental algorithm for the global solution of convex mixed-integer nonlinear programming (MINLP) problems. It is well-known that carrying out branching in a nonsimplis...
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Branch-and-Bound (B&B) is perhaps the most fundamental algorithm for the global solution of convex mixed-integer nonlinear programming (MINLP) problems. It is well-known that carrying out branching in a nonsimplistic manner can greatly enhance the practicality of B&B in the context of mixed-integer Linear programming (MILP). No detailed study of branching has heretofore been carried out for MINLP. In this article, we study and identify useful sophisticated branching methods for MINLP, including novel approaches based on approximations of the nonlinear relaxations by linear and quadratic programs.
This paper proposed a penalty guided artificial bee colony algorithm (ABC) to solve the reliability redundancy allocation problem (RAP). The redundancy allocation problem involves setting reliability objectives for co...
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This paper proposed a penalty guided artificial bee colony algorithm (ABC) to solve the reliability redundancy allocation problem (RAP). The redundancy allocation problem involves setting reliability objectives for components or subsystems in order to meet the resource consumption constraint, e.g. the total cost. RAP has been an active area of research for the past four decades. The difficulty that one is confronted with the RAP is the maintenance of feasibility with respect to three nonlinear constraints, namely, cost, weight and volume related constraints. In this paper nonlinearly mixed-integer reliability design problems are investigated where both the number of redundancy components and the corresponding component reliability in each subsystem are to be decided simultaneously so as to maximize the reliability of the system. The reliability design problems have been studied in the literature for decades, usually using mathematical programming or heuristic optimization approaches. To the best of our knowledge the ABC algorithm can search over promising feasible and infeasible regions to find the feasible optimal/near-optimal solution effectively and efficiently;numerical examples indicate that the proposed approach performs well with the reliability redundant allocation design problems considered in this paper and computational results compare favorably with previously-developed algorithms in the literature. (C) 2010 Elsevier Ltd. All rights reserved.
We introduce in this paper an optimal method for tree network design avoiding congestion. We see this problem arising in telecommunication and transportation networks as a flow extension of the Steiner problem in dire...
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We introduce in this paper an optimal method for tree network design avoiding congestion. We see this problem arising in telecommunication and transportation networks as a flow extension of the Steiner problem in directed graphs, thus including as a particular case any alternative approach based on the minimum spanning tree problem. Our multi-commodity formulation is able to cope with the design of centralized computer networks, modern multi-cast multi-party or hub-based transportation trees. The objective in such cases is the minimization of the sum of the fixed (structural) and variable (operational) costs of all the arcs composing an arborescence that links the origin node (switching center, server, station) to every demand node (multi-cast participants, users in general). The nonlinear multi-commodity flow model is solved by a generalized Benders decomposition approach. (C) 2011 Elsevier Inc. All rights reserved.
In this paper, a novel mixed-integernonlinear approach is proposed to solve the short-term hydro scheduling problem in the day-ahead electricity market, considering not only head-dependency, but also start/stop of un...
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In this paper, a novel mixed-integernonlinear approach is proposed to solve the short-term hydro scheduling problem in the day-ahead electricity market, considering not only head-dependency, but also start/stop of units, discontinuous operating regions and discharge ramping constraints. Results from a case study based on one of the main Portuguese cascaded hydro energy systems are presented, showing that the proposed mixed-integernonlinear approach is proficient. Conclusions are duly drawn. (C) 2010 Elsevier Ltd. All rights reserved.
The aim of this paper is to introduce a methodology to solve a large-scale mixed-integernonlinear program (MINLP) integrating the two main optimization problems appearing in the oil refining industry: refinery planni...
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The aim of this paper is to introduce a methodology to solve a large-scale mixed-integernonlinear program (MINLP) integrating the two main optimization problems appearing in the oil refining industry: refinery planning and crude-oil operations scheduling. The proposed approach consists of using Lagrangian decomposition to efficiently integrate both problems. The main advantage of this technique is to solve each problem separately. A new hybrid dual problem is introduced to update the Lagrange multipliers. It uses the classical concepts of cutting planes, subgradient, and boxstep. The proposed approach is compared to a basic sequential approach and to standard MINLP solvers. The results obtained on a case study and a larger refinery problem show that the new Lagrangian decomposition algorithm is more robust than the other approaches and produces better solutions in reasonable times. (C) 2011 Elsevier Ltd. All rights reserved.
In this paper a discrete location model for non-essential service facilities planning is described, which seeks the number, location, and size of facilities, that maximizes the total expected demand attracted by the f...
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In this paper a discrete location model for non-essential service facilities planning is described, which seeks the number, location, and size of facilities, that maximizes the total expected demand attracted by the facilities. It is assumed that the demand for service is sensitive to the distance from facilities and to their size. It is also assumed that facilities must satisfy a threshold level of demand (facilities are not economically viable below that level). A mixed-integer nonlinear programming (MINLP) model is proposed for this problem. A branch-and-bound algorithm is designed for solving this MINLP and its convergence to a global minimum is established. A finite procedure is also introduced to find a feasible solution for the MINLP that reduces the overall search in the binary tree generated by the branch-and-bound algorithm. Some numerical results using a GAMS/MINOS implementation of the algorithm are reported to illustrate its efficacy and efficiency in practice.
We discuss a tank design problem for a multi product plant, in which the optimal cycle time and the optimal campaign size are unknown. A mixed-integer nonlinear programming (MINLP) formulation is presented, where non-...
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We discuss a tank design problem for a multi product plant, in which the optimal cycle time and the optimal campaign size are unknown. A mixed-integer nonlinear programming (MINLP) formulation is presented, where non-convexities are due to the tank investment cost, storage cost, campaign setup cost and variable production rates. The objective of the optimization model is to minimize the sum of the production cost per ton per product produced. A continuous-time mathematical programming formulation is proposed and several extensions are discussed. The model is implemented in GAMS and computational results are reported for the two global MINLP solver BARON and LINDOGlobal as well as several nonlinear solvers available in GAMS. (c) 2010 Elsevier Ltd. All rights reserved.
Nowadays, there is growing interest in renewable energy (RE) generation projects due to environmental and sustainability concerns. However, initial costs and uncertainties caused by RE source variability, changes in s...
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Nowadays, there is growing interest in renewable energy (RE) generation projects due to environmental and sustainability concerns. However, initial costs and uncertainties caused by RE source variability, changes in support schemes, and other factors can render RE projects unattractive when subject to conventional financial assessment. Initial research suggests that the value of RE projects can be enhanced by the application of real options (RO) theory in the planning and evaluation of such projects. Literature on application of RO planning in RE generation projects is limited, and typically focuses solely on flexible investment decisions and neglects flexible design A more comprehensive approach should address flexibility in designs. This paper proposes an advanced RO methodology for RE generation projects planning, and illustrates the methodology using variations of a hydropower case study. The fundamental differences between advanced RO approaches and other techniques are illustrated on a simple case study. The complete version of the proposed advanced methodology is then compared against other available tools. The results show higher expected profits for projects planned with the advanced RO methodology. (C) 2011 Elsevier Ltd. All rights reserved.
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