This paper addresses the operational planning of an ethylene production plant. The problem is formulated as a non-linear programming (NLP) model. The NLP is validated on a real-world petrochemical plant whose main pro...
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This paper addresses the operational planning of an ethylene production plant. The problem is formulated as a non-linear programming (NLP) model. The NLP is validated on a real-world petrochemical plant whose main product is ethylene as well as several other hydrocarbon products that are obtained using naphtha as feedstock. The amount produced of each product can be adjusted, within certain bounds, through the operation of specific units, the production of gasoline blends or the tuning of cracking severity. This operational flexibility requires the use of optimization techniques for the production planning. The objective is to maximize the difference between product revenues subtracted from feedstock and utilities costs. The resulting model is non-convex and involves bilinear terms for the flow rates of individual components and their ratios. The model is applied and analyzed under typical scenarios found in the operation of the plant, such as processing naphtha with different properties and formulating gasoline blends, among others. Finally, the model is coupled with a short cut model to determine more precisely utility consumption. This is illustrated with the simulation of a xylene tower that calculates steam consumption for this unit. (c) 2007 Elsevier B.V. All rights reserved.
This paper develops a non-linear programming model to design optimal corporate contracts for airlines stipulating front-end discounts for all nets, which are defined by combination of routes, cabin types, and fare cla...
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This paper develops a non-linear programming model to design optimal corporate contracts for airlines stipulating front-end discounts for all nets, which are defined by combination of routes, cabin types, and fare classes. The airline's profit is modeled using a multinomial logit function that captures the client's choice behavior in a competitive market. Alternative formulations are employed to investigate the impact of price elasticity, demand, and competition on optimal discounting policies. A case study involving a major carrier is presented to demonstrate the model. The results indicate that airlines can increase revenues significantly by optimizing corporate contracts using the suggested model. (c) 2006 Elsevier Ltd. All rights reserved.
Inverse lithography attempts to synthesize the input mask which leads to the desired output wafer pattern by inverting the forward model from mask to wafer. In this article, we extend our earlier framework for image p...
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Inverse lithography attempts to synthesize the input mask which leads to the desired output wafer pattern by inverting the forward model from mask to wafer. In this article, we extend our earlier framework for image prewarping to solve the mask design problem for coherent, incoherent, and partially coherent imaging systems. We also discuss the synthesis of three variants of phase shift masks (PSM);namely, attenuated (or weak) PSM, 100% transmission PSM, and strong PSM with chrome. A new two-step optimization strategy is introduced to promote the generation and placement of assist bar features. The regularization framework is extended to guarantee that the estimated PSM have only two or three (allowable) transmission values, and the aerial-image penalty term is introduced to boost the aerial image contrast and keep the side-lobes under control. Our approach uses the pixel-based mask representation, a continuous function formulation, and gradient-based iterative optimization techniques to solve the inverse problem. The continuous function formulation allows analytic calculation of the gradient in O(MNlog(MN)) operations for an M x N pattern making it practically feasible. We also present some results for coherent and incoherent imaging systems with very low k(1) values to demonstrate the effectiveness of our approach.(C) 2007 Elsevier B.V. All rights reserved.
This workreports a synthesis model for the optimal design of an emulsion pertraction process for the removal and recovery of pollutant anions from industrial wastewaters. The goal is to define the minimum membrane are...
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This workreports a synthesis model for the optimal design of an emulsion pertraction process for the removal and recovery of pollutant anions from industrial wastewaters. The goal is to define the minimum membrane area needed in order to obtain an environmentally acceptable stream with minimum concentration of the pollutant and at the same time a concentrated solution for further processing. A superstructure is proposed which consists of a prespecified number of modules that are interconnected in all possible ways in order to account for all potential configurations. The selection of the optimal design from this superstructure is formulated as a non-linear programming (NLP) problem that is solved with CONOPT2 from GAMS. In order to reduce the membrane area, alternative configurations that provide flexibility to the emulsion phase inlet and higher residence time values were analyzed obtaining 56% reduction of the membrane area. (c) 2006 Elsevier Ltd. All rights reserved.
Blockmodelling is a method for identifying structural similarities or equivalences between elements which has applications in a variety of contexts, including multiattribute performance assessment. One criterion for f...
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Blockmodelling is a method for identifying structural similarities or equivalences between elements which has applications in a variety of contexts, including multiattribute performance assessment. One criterion for forming blocks results in a difficult non-linear integer programme. We give several integer linearprogramming formulations of this problem and provide comparative computational results. We show that methods of reducing symmetry proposed by Sherali and Smith are not effective in this case and propose an iterative approach in which the size of the problem is reduced. (c) 2006 Elsevier B.V. All rights reserved.
An outer approximation based, time-varying optimization methodology to obtain a least-cost groundwater remediation design based upon a multi-period, pump-and-treat strategy is introduced. In this novel approach, the r...
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An outer approximation based, time-varying optimization methodology to obtain a least-cost groundwater remediation design based upon a multi-period, pump-and-treat strategy is introduced. In this novel approach, the remediation design is modified, or updated, from time to time in order to obtain cost-effective removal of contaminants from the subsurface subject to the requirement of respecting upper-bound contaminant concentration constraints at target locations and times. Concentration constraints are defined at the end of each management period, the duration of which is calculated by the optimization algorithm in such a way as to ensure maximum mass removal and to prevent further off-site migration of the contaminant plume. At each stage, the pumping strategy is modified and constraints are relocated in response to the changes in the plume geometry. A penalty term is added to the objective function to assure the performance of the proposed remediation-pumping scheme at a pre-selected time beyond the end of the design period. The utilization of the suggested approach to a field application illustrates the effectiveness of the proposed strategy. (c) 2006 Elsevier B.V. All rights reserved.
We studied a supplier selection problem, where a buyer, while facing random demand, is to decide ordering quantities from a set of suppliers with different yields and prices. We provided the mathematical formulation f...
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We studied a supplier selection problem, where a buyer, while facing random demand, is to decide ordering quantities from a set of suppliers with different yields and prices. We provided the mathematical formulation for the buyer's profit maximization problem and proposed a solution method based on a combination of the active set method and the Newton search procedure. Our computational study shows that the proposed method can solve the problem efficiently, and is able to generate interesting and insightful results that lead us to various managerial implications.
Energy consumption optimization constitutes one of the main challenges in wireless sensor networks. Most of the power based routing strategies proposed in the literature are based on global power consumption optimizat...
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ISBN:
(纸本)9781424405220
Energy consumption optimization constitutes one of the main challenges in wireless sensor networks. Most of the power based routing strategies proposed in the literature are based on global power consumption optimization rather than maximizing the network lifetime. In this paper, we propose an adaptive routing strategy that maximizes the lifetime of the sensor network, where optimal routes are selected in order to delay, as long as possible, the first energy run out among sensor nodes. The proposed routing strategy also takes into account both the reception and the transmission energy dissipations. Moreover, in order to adapt to the changes that may occur in the network topology or characteristics, we propose a global adaptive framework that dynamically reconfigures routes according to the current network state. The proposed strategy is modelled and simulation results are presented.
Generalized polynomial programming (GPP) is a non-linear programming (NLP) method based on a non-convex objective function, which is subject to nonconvex inequality constraints. Hence, a GPP problem has multiple local...
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Generalized polynomial programming (GPP) is a non-linear programming (NLP) method based on a non-convex objective function, which is subject to nonconvex inequality constraints. Hence, a GPP problem has multiple local optima in its constrained solution space. General NLP techniques use local optimization, and therefore do not easily solve GPP problems. Some deterministic global optimization approaches have been developed to overcome this drawback of NLP methods. Although these approaches yield a global solution to a GPP problem, they can be mathematically tedious. Therefore, this study presents a real-coded genetic algorithm (RGA), which is a stochastic global optimization method, to find a global solution to a GPP problem. The proposed RGA is used to solve a set of GPP problems. The best solution obtained by the RGA is compared with the known global solution to each test problem. Numerical results show that the proposed RGA converges to a global solution to a GPP problem.
This paper presents the utilization of SISCON, a the software package meant to be utilised in the evaluation of optimal decisions for large scale systems. The large scale systems have generally a complex structure and...
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This paper presents the utilization of SISCON, a the software package meant to be utilised in the evaluation of optimal decisions for large scale systems. The large scale systems have generally a complex structure and consequently a global computation approach cannot be effectively carried out if at all. First the decomposition of large–scale problems is carried out. Then the subproblems are solved by using standard optimization techniques. SISCON offers opportunities for solving non-linear mathematical programming problems and for evaluating optimal decisions in large scale systems control. SISCON firstly evaluates mathematical models developed from experimental data using LS least square methods for linear and nonlinear systems and after that then computes the optimal decision solution by solving the mathematical non-linear programming problems.
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