The demand for water often necessitates desalination, particularly in arid coastal environments. Desalination is often integrated with electrical cogeneration. The demands for water and electricity change over time an...
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The demand for water often necessitates desalination, particularly in arid coastal environments. Desalination is often integrated with electrical cogeneration. The demands for water and electricity change over time and are subject to uncertainty. A country-wide large-scale energy and water cogeneration planning model for Kuwait is formulated as a multi-period mixed integer linear programming problem and solved to minimize the net present value over the time period of 2013-2050. Five different plant technology options were considered for desalination and cogeneration including Oil & Multi Stage Flash, Natural Gas & Multi-Effect Distillation, Natural Gas & Reverse Osmosis, Solar Energy & Multi-Effect Distillation, and Solar Energy & Reverse Osmosis. Both water and energy usage in Kuwait and data from existing plants were utilized in providing the parameters and forecasts necessary for solution of the mathematical programming model. The model provides technology choice and associated capacity decisions for existing plants, new plants at green sites, and existing plant capacity expansions as well as their timing to meet the demands.
One of the key problems in hardware/software codesign is hardware/software partitioning. This paper describes a new approach to hardware/software partitioning using integerprogramming (IP). The advantage of using IP ...
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One of the key problems in hardware/software codesign is hardware/software partitioning. This paper describes a new approach to hardware/software partitioning using integerprogramming (IP). The advantage of using IP is that optimal results are calculated for a chosen objective function, The partitioning approach works fully automatic and supports multi-processor systems, interfacing and hardware sharing. In contrast to other approaches where special estimators are used, we use compilation and synthesis tools for cost estimation. The increased time for calculating values for the cost metrics is compensated by an improved quality of the values. Therefore, fewer iteration steps for partitioning are needed. The paper presents an algorithm using integerprogramming for solving the hardware/software partitioning problem leading to promising results.
Air traffic flow becomes denser and more complex within terminal manoeuvering areas (TMAs) due to rapid growth rates in demand. Effective TMA arrival management plays a key role in the improvement of airspace capacity...
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Air traffic flow becomes denser and more complex within terminal manoeuvering areas (TMAs) due to rapid growth rates in demand. Effective TMA arrival management plays a key role in the improvement of airspace capacity, flight efficiency and air traffic controller performance. This study proposes a mixed integer linear programming model for aircraft landing problems with area navigation (RNAV) route structure using three conflict resolution and sequencing techniques together: flexible route allocation, airspeed reduction and vector manoeuver. A two-step mixed integer linear programming model was developed that minimises total conflict resolution time and then total airborne delay using lexicographic goal programming. Experimental results demonstrate that the model can obtain conflict-free and time optimal aircraft trajectories for RNAV route structures.
Microgrid with distributed energy resources and energy storage system provides sustainability and resiliency. In this research, residential community microgrid is examined with responsive loads that create flexible ge...
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Microgrid with distributed energy resources and energy storage system provides sustainability and resiliency. In this research, residential community microgrid is examined with responsive loads that create flexible generation-demand model. An optimization algorithm using mixed integer linear programming (MILP) has been formulated to minimize the operating cost and emission of dispatchable power generation, with the help of demand response. Usually, in renewable energy-based grid-connected microgrid, the batteries are managed under partial state of charge (SoC) conditions due to the limit of power imported from grid. The proposed MILP model ensures full SoC operation and safe charging or discharging dynamics of the battery in order to enhance its lifespan. Moreover, the day-ahead scheduling of household appliances is carried out using a novel hybrid knapsack method, which combines binary and fractional knapsack algorithms. An electric vehicle battery is considered as a flexible power load, which offers an unique way of approach in scheduling of appliances. The results confirm that the power demanded by the appliances is fulfilled at the user-specified hour for maximum comfort along with minimum operating cost of microgrid. Generic algebraic modeling system (GAMS) tool is used to run the proposed algorithms.
The growing complexity of power system operation demands a greater performance of the modeling approaches, even for generation technologies already consolidated as hydroelectricity. Five mixedintegerlinear programmi...
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The growing complexity of power system operation demands a greater performance of the modeling approaches, even for generation technologies already consolidated as hydroelectricity. Five mixed integer linear programming formulations for modeling the hydro production function (HPF) are compared: four based on previous references (the traditional method based on a single concave piecewise linear flow-power function, the rectangle method, the logarithmic independent branching 6-stencil method, and the quadrilateral method), and one firstly presented in the paper (the parallelogram method). The comparison is made in the context of daily and weekly hydro scheduling problems of a hypothetical three-plant system on different national day-ahead markets and using different levels of detail in the HPF discretization and time limits. The discussion of results is focused around the relative accuracy, effectiveness, and speed of the analyzed methods to solve the scheduling problems in order to aid in making the most appropriate choice depending on the time horizon. This discussion shows the logarithmic independent branching 6-stencil method as one of the most accurate, the parallelogram method as one of the most effective, and the traditional method based on a single concave piecewise linear flow-power function as the fastest one.
The effective application of continuous gas lift entails solving the combinatorial optimization problem of optimally allocating limited resources. This work proposes a mixedintegerlinear formulation for the problem ...
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The effective application of continuous gas lift entails solving the combinatorial optimization problem of optimally allocating limited resources. This work proposes a mixedintegerlinear formulation for the problem of maximizing oil field profit under multiple facility constraints such as limited lift gas, fluid handling, and storage capacities. Families of valid inequalities are identified and introduced into the basic model to render a stronger formulation. Numerical experiments using commercial and non-commercial software, and comparisons with published results, show that the proposed method yields fast solution with significant average increase in oil production rate and profit.
The performance of classification methods, such as Support Vector Machines, depends heavily on the proper choice of the feature set used to construct the classifier. Feature selection is an NP-hard problem that has be...
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The performance of classification methods, such as Support Vector Machines, depends heavily on the proper choice of the feature set used to construct the classifier. Feature selection is an NP-hard problem that has been studied extensively in the literature. Most strategies propose the elimination of features independently of classifier construction by exploiting statistical properties of each of the variables, or via greedy search. All such strategies are heuristic by nature. In this work we propose two different mixed integer linear programming formulations based on extensions of Support Vector Machines to overcome these shortcomings. The proposed approaches perform variable selection simultaneously with classifier construction using optimization models. We ran experiments on real-world benchmark datasets, comparing our approaches with well-known feature selection techniques and obtained better predictions with consistently fewer relevant features. (C) 2014 Elsevier Inc. All rights reserved.
Data classification is one of the fundamental issues in data mining and machine learning. A great deal of effort has been done for reducing the time required to learn a classification model. In this research, a new mo...
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Data classification is one of the fundamental issues in data mining and machine learning. A great deal of effort has been done for reducing the time required to learn a classification model. In this research, a new model and algorithm is proposed to improve the work of Xu and Papageorgiou (2009). Computational comparisons on real and simulated patterns with different characteristics (including dimension, high overlap or heterogeneity in the attributes) confirm that, the improved method considerably reduces the training time in comparison to the primary model, whereas it generally maintains the accuracy. Particularly, this speed-increase is significant in the case of high overlap. In addition, the rate of increase in training time of the proposed model is much less than that of the primary model, as the set-size or the number of overlapping samples is increased. (C) 2013 Elsevier Ltd. All rights reserved.
A mixedintegerlinear model for selecting the best decision making unit (DMU) in data envelopment analysis (DEA) has recently been proposed by Foroughi [Foroughi, A. A. (2011a). A new mixedintegerlinear model for s...
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A mixedintegerlinear model for selecting the best decision making unit (DMU) in data envelopment analysis (DEA) has recently been proposed by Foroughi [Foroughi, A. A. (2011a). A new mixedintegerlinear model for selecting the best decision making units in data envelopment analysis. Computers and Industrial Engineering, 60(4), 550-554], which involves many unnecessary constraints and requires specifying an assurance region (AR) for input weights and output weights, respectively. Its selection of the best DMU is easy to be affected by outliers and may sometimes be incorrect. To avoid these drawbacks, this paper proposes three alternative mixed integer linear programming (MILP) models for identifying the most efficient DMU under different returns to scales, which contain only essential constraints and decision variables and are much simpler and more succinct than Foroughi's. The proposed alternative MILP models can make full use of input and output information without the need of specifying any assurance regions for input and output weights to avoid zero weights, can make correct selections without being affected by outliers, and are of significant importance to the decision makers whose concerns are not DMU ranking, but the correct selection of the most efficient DMU. The potential applications of the proposed alternative MILP models and their effectiveness are illustrated with four numerical examples. (C) 2011 Elsevier Ltd. All rights reserved.
Quality function deployment (QFD) is a product development process performed to maximize customer satisfaction. In the QFD, the design requirements (DRs) affecting the product performance are primarily identified, and...
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Quality function deployment (QFD) is a product development process performed to maximize customer satisfaction. In the QFD, the design requirements (DRs) affecting the product performance are primarily identified, and product performance is improved to optimize customer needs (CNs). For product development, determining the fulfillment levels of design requirements (DRs) is crucial during QFD optimization. However, in real world applications, the values of DRs are often discrete instead of continuous. To the best of our knowledge, there is no mixed integer linear programming (MILP) model in which the discrete DRs values are considered. Therefore, in this paper, a new QFD optimization approach combining MILP model and Kano model is suggested to acquire the optimized solution from a limited number of alternative DRs, the values of which can be discrete. The proposed model can be used not only to optimize the product development but also in other applications of QFD such as quality management, planning, design, engineering and decision-making, on the condition that DR values are discrete. Additionally, the problem of lack of solutions in integer and linearprogramming in the QFD optimization is overcome. Finally, the model is illustrated through an example. (C) 2009 Elsevier Ltd. All rights reserved.
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