This paper is on the problem of short-term hydro scheduling, particularly concerning a head-sensitive hydro chain. A novel mixed-integer nonlinear programming approach is proposed for optimizing power generation effic...
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This paper is on the problem of short-term hydro scheduling, particularly concerning a head-sensitive hydro chain. A novel mixed-integer nonlinear programming approach is proposed for optimizing power generation efficiency. The proposed approach considers not only the nonlinear dependence between power generation, water discharge and head, but also start-up costs for the hydro units and discontinuous operating regions, in order to obtain more realistic and feasible results. Numerical results based on one of the main Portuguese cascaded hydro systems illustrate the proficiency of the proposed approach.
This paper presents an overview of mixed-integer nonlinear programming techniques by first providing a unified treatment of the Branch and Bound, Outer-Approximation, Generalized Benders and Extended Cutting Plane met...
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This paper presents an overview of mixed-integer nonlinear programming techniques by first providing a unified treatment of the Branch and Bound, Outer-Approximation, Generalized Benders and Extended Cutting Plane methods as applied to nonlinear discrete optimization problems that are expressed in algebraic form. The extension of these methods is also considered for logic based representations. Finally, an overview of the applications in many areas in process engineering is presented.
Energy consumption is expected to be reduced while maintaining high productivity for container handling. This paper investigates a new energy-efficient scheduling problem of automated container terminals, in which qua...
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Energy consumption is expected to be reduced while maintaining high productivity for container handling. This paper investigates a new energy-efficient scheduling problem of automated container terminals, in which quay cranes (QCs) and lift automated guided vehicles (AGVs) cooperate to handle inbound and outbound containers. In our scheduling problem, operation times and task sequences are both to be determined. The underlying optimization problem is mixed-integer nonlinear programming (MINLP). To deal with its computational intractability, a customized and efficient genetic algorithm (GA) is developed to solve the studied MINLP problem, and lexicographic and weighted-sum strategies are further considered. An epsilon-constraint algorithm is also developed to analyze the Pareto frontiers. Comprehensive experiments are tested on a container handling benchmark system, and the results show the effectiveness of the proposed lexicographic GA, compared to results obtained with two commonly-used metaheuristics, a commercial MINLP solver, and two state-of-the-art methods.
We consider the problem of aircraft conflict avoidance in Air Traffic Management systems. Given an initial configuration of a number of aircraft sharing the same airspace, the main goal of conflict avoidance is to gua...
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We consider the problem of aircraft conflict avoidance in Air Traffic Management systems. Given an initial configuration of a number of aircraft sharing the same airspace, the main goal of conflict avoidance is to guarantee that a minimum safety distance between each pair of aircraft is always respected during their flights. We consider aircraft separation achieved by heading angle deviations, and, propose a mixed 0-1 nonlinear optimization model, that is then combined with another one which is based on aircraft speed regulation. A two-step solution approach is proposed, where the two models are sequentially solved using a state-of-the-art mixed-integer nonlinear programming solver. Numerical results validate the proposed approach and clearly show the benefit of combining the two considered separation maneuvers. (C) 2016 Elsevier B.V. All rights reserved.
We present a mixed-integer nonlinear programming (MINLP) formulation of a UAV path optimization problem, and attempt to find the global optimum solution. As objective functions in UAV path optimization problems tend t...
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ISBN:
(纸本)9781509059928
We present a mixed-integer nonlinear programming (MINLP) formulation of a UAV path optimization problem, and attempt to find the global optimum solution. As objective functions in UAV path optimization problems tend to be non-convex, traditional optimization solvers (typically local solvers) are prone to local optima, which lead to severely sub-optimal controls. For the purpose of this study, we choose a target tracking application, where the goal is to optimize the kinematic controls of UAVs while maximizing the target tracking performance. First, we compare the performance of two traditional solvers numerically - MATLAB's fmincon and knitro. Second, we formulate this UAV path optimization problem as a mixed-integernonlinear program (MINLP). As this MINLP tends to be computationally expensive, we present two pruning methods to make this MINLP tractable. We also present numerical results to demonstrate the performance of these methods.
For large, complex reacting systems, computational efficiency becomes a critical issue in process simulation, optimization, and model-based control. Mechanism simplification is often a necessity to improve computation...
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For large, complex reacting systems, computational efficiency becomes a critical issue in process simulation, optimization, and model-based control. Mechanism simplification is often a necessity to improve computational speed. We present a novel approach to reaction mechanism simplification that formulates the model reduction problem as a mixed-integer nonlinear programming problem and solves it using DICOPT + + (discrete and continuous optimizer). Two formulations of the mechanism simplification problem are considered, one involving the elimination of reactions and the other the elimination of species. Both steady-state and dynamic problem formulations are developed. Solutions for example problems having six reactions and six species are presented. (C) 2000 Elsevier Science Ltd. All rights reserved.
mixed-integer nonlinear programming (MINLP) problems involving general constraints and objective functions with continuous and integer variables occur frequently in engineering design, chemical process industry and ma...
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mixed-integer nonlinear programming (MINLP) problems involving general constraints and objective functions with continuous and integer variables occur frequently in engineering design, chemical process industry and management. Although many optimization approaches have been developed for MINLP problems, these methods can only handle signomial terms with positive variables or find a local solution. Therefore, this study proposes a novel method for solving a signomial MINLP problem with free variables to obtain a global optimal solution. The signomial MINLP problem is first transformed into another one containing only positive variables. Then the transformed problem is reformulated as a convex mixed-integer program by the convexification strategies and piecewise linearization techniques. A global optimum of the signomial MINLP problem can finally be found within the tolerable error. Numerical examples are also presented to demonstrate the effectiveness of the proposed method.
Dimethyl ether (DME) is a clean and efficient synthetic fuel with the potential to substitute liquefied petroleum gas (LPG) and diesel. From both the environmental and economic viewpoints, there is a strong preference...
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Dimethyl ether (DME) is a clean and efficient synthetic fuel with the potential to substitute liquefied petroleum gas (LPG) and diesel. From both the environmental and economic viewpoints, there is a strong preference to use biomass as the feedstock of synthetic fuels. In this study, state-of-the-art technologies were surveyed and a superstructure representation for the DME production process involving the use of biomass as a feedstock was investigated. Moreover, a mixed-integer nonlinear programming (MINLP) model was proposed for developing a DME production process based on biomass gasification. A combination of optimal technologies was adopted;detailed studies are presented to demonstrate the key features of the proposed superstructure.
Aiming at the shortcomings of existing algorithms in solving mixed-integerprogramming problems, such as local convergence and poor solution accuracy, this paper presents a hybrid improved sine and cosine algorithm. T...
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Aiming at the shortcomings of existing algorithms in solving mixed-integerprogramming problems, such as local convergence and poor solution accuracy, this paper presents a hybrid improved sine and cosine algorithm. The improved position update formula is introduced to enhance the global and local search abilities of the algorithm, and combined mutation is further given to avoid local convergence. The performance of proposed algorithm is validated by the high-dimensional modified CEC 2017 COPs and two complex engineering optimization problems. The experimental findings indicate that the hybrid mechanism of improved sine and cosine algorithm and combined mutation is demonstrably effective, and the proposed algorithm provides a satisfactory solution for addressing high-dimensional complicated mixed-integerprogramming problems.
Index funds consist of a subset of stocks, an index tracking portfolio, included in the market index. The index tracking portfolio aims to match the performance of the benchmark index. In this paper, we propose a hybr...
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Index funds consist of a subset of stocks, an index tracking portfolio, included in the market index. The index tracking portfolio aims to match the performance of the benchmark index. In this paper, we propose a hybrid model for solving the multiperiod index tracking problem, which includes rebalancing concerns, transaction costs, limits on the number of stocks, and diversification by sector, market capitalization, and stock weight. Our hybrid model combines the genetic algorithm (GA) to select stocks of the index tracking portfolio and mixed-integer nonlinear programming (MINLP) to estimate its weights. Finally, we apply our proposed hybrid model to the S&P500 to find an index tracking portfolio that includes those constraints. The results show that our hybrid model is able to create an index fund whose return rate is similar to the market index with significantly lower risk.
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