Hydrogen production is a vital development trend to improve the economics and penetration rate of offshore wind farms (OWFs), and distributed hydrogen production (DHP) is a preferred solution for deep and far sea OWFs...
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Hydrogen production is a vital development trend to improve the economics and penetration rate of offshore wind farms (OWFs), and distributed hydrogen production (DHP) is a preferred solution for deep and far sea OWFs without expensive submarine cables and offshore substations. The export pathway of OWF-DHP consists of collection pipelines, transmission pipelines, and offshore compressor stations. In this paper, mathematical programming is introduced to explore the export pathway planning problem of OWF-DHP. Firstly, the area of OWF is discretized into multiple grids with the center of the grids being the candidate locations of the offshore compressor station. Then, a binary integer quadratic programming problem is established to optimize both the offshore compressor station location and pipeline construction with different topologies. Further, the mathematical model is solved by the branch and cut algorithm integrated with the proposed dimension reduction methods. Finally, the effectiveness of the proposed export pathway planning approach is verified by the actual data of Baltic Eagle OWF in Germany, which would support the construction of the OWF-DHP project.
The use of a common Q-Féjèr's mapping instead of the projection operator in iterative methods for solving variational inequalities based on the iterative regularization principle that makes easier their ...
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The use of a common Q-Féjèr's mapping instead of the projection operator in iterative methods for solving variational inequalities based on the iterative regularization principle that makes easier their application to mathematical programming problems is substantiated. The possibilities of using such iterative methods for concrete classes of problems (inparticular, of linear programming) are indicated.
Mass integration is an efficient tool to prevent pollution derived from chemical processes. In recent years, several advances have been reported in methodologies for the design of mass exchange networks (MENs), using ...
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Mass integration is an efficient tool to prevent pollution derived from chemical processes. In recent years, several advances have been reported in methodologies for the design of mass exchange networks (MENs), using approaches as the pinch point and mathematical programming. Recently, simultaneous design and optimization methodologies have been proposed. The design methods based on simultaneous optimization offer the possibility of synthetizing MENs in a single step, formulating the synthesis problem as a mixed-integer nonlinear programming problem (MINLP). In this work, a MINLP model for the synthesis of mass exchange networks is presented. The model is based on a superstructure represented with disjunctions. The proposed superstructure is obtained from a state-task-network representation and is applied to a process for copper removal from an etching process. The obtained mass exchange network showed a lower total annual cost than the networks previously reported for the same process. (c) 2018 Elsevier Ltd. All rights reserved.
Large-scale mathematical models are built, managed and applied by people with different cognitive skills. This poses a challenge for the design of a multi-view architecture of a system that accommodates these differen...
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Large-scale mathematical models are built, managed and applied by people with different cognitive skills. This poses a challenge for the design of a multi-view architecture of a system that accommodates these differences. A primary objective of mathematical modeling is providing insights into problem behavior, and there are many constituencies who require different views for different questions. One constituency is composed of modellers who have different views of basic model components. Another constituency is composed of problem owners for whom models are built. These two constituencies, which are not exhaustive, have significantly different needs and skills. This paper addresses this issue of multiview architecture by presenting a formal framework for the design of a view creation and management system. Specific views we consider include algebraic, block schematic, graphic, and textual. Both form and content are relevant to view creation, and the merits of views are determined by their value in aiding comprehension and insight. The need for a central, formal structure to create and manage views is demonstrated by the inadequacy of direct mappings from any of the popular systems that are typically designed to support only one View of linear programming models and their instances.
Background: Applying machine learning methods on microarray gene expression profiles for disease classification problems is a popular method to derive biomarkers, i.e. sets of genes that can predict disease state or o...
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Background: Applying machine learning methods on microarray gene expression profiles for disease classification problems is a popular method to derive biomarkers, i.e. sets of genes that can predict disease state or outcome. Traditional approaches where expression of genes were treated independently suffer from low prediction accuracy and difficulty of biological interpretation. Current research efforts focus on integrating information on protein interactions through biochemical pathway datasets with expression profiles to propose pathway-based classifiers that can enhance disease diagnosis and prognosis. As most of the pathway activity inference methods in literature are either unsupervised or applied on two-class datasets, there is good scope to address such limitations by proposing novel methodologies. Results: A supervised multiclass pathway activity inference method using optimisation techniques is reported. For each pathway expression dataset, patterns of its constituent genes are summarised into one composite feature, termed pathway activity, and a novel mathematical programming model is proposed to infer this feature as a weighted linear summation of expression of its constituent genes. Gene weights are determined by the optimisation model, in a way that the resulting pathway activity has the optimal discriminative power with regards to disease phenotypes. Classification is then performed on the resulting low-dimensional pathway activity profile. Conclusions: The model was evaluated through a variety of published gene expression profiles that cover different types of disease. We show that not only does it improve classification accuracy, but it can also perform well in multiclass disease datasets, a limitation of other approaches from the literature. Desirable features of the model include the ability to control the maximum number of genes that may participate in determining pathway activity, which may be pre-specified by the user. Overall, this work highlig
Machine interference is an important problem frequently encountered in production and manufacturing operations such as semiconductor manufacturing and maintenance operations. Due to uncontrollable factors, parameters ...
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Machine interference is an important problem frequently encountered in production and manufacturing operations such as semiconductor manufacturing and maintenance operations. Due to uncontrollable factors, parameters in the machine interference problem may be fuzzy. This paper proposes a mathematical programming approach to construct the membership function of the performance measure of the machine interference system with the machine breakdown rate and the service rate being fuzzy numbers. On the basis of the extension principle, a pair of mathematical programs is formulated to calculate the lower and upper bounds of the fuzzy performance measure at possibility level alpha. By enumerating different values of alpha(1) the membership function of the system performance, measure is constructed. A numerical example is solved Successfully to demonstrate the validity of the proposed approach. Since the performance measure is completely described by a membership function rather than by a crisp value, the fuzziness of input parameters is conserved completely. Thus more information is provided for designing manufacturing systems. The successful extension of machine interference models to fuzzy environments permits machine interference models to have wider practical applications. (c) 2005 Elsevier Inc. All rights reserved.
Decentralized planning in uncertain environments is a complex task generally dealt with by using a decision-theoretic approach, mainly through the framework of Decentralized Partially Observable Markov Decision Proces...
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Decentralized planning in uncertain environments is a complex task generally dealt with by using a decision-theoretic approach, mainly through the framework of Decentralized Partially Observable Markov Decision Processes (DEC-POMDPs). Although DEC-POMDPS are a general and powerful modeling tool, solving them is a task with an overwhelming complexity that can be doubly exponential. In this paper, we study an alternate formulation of DEC-POMDPs relying on a sequence-form representation of policies. From this formulation, we show how to derive Mixed Integer Linear programming (MILP) problems that, once solved, give exact optimal solutions to the DEC-POMDPs. We show that these MILPs can be derived either by using some combinatorial characteristics of the optimal solutions of the DEC-POMDPs or by using concepts borrowed from game theory. Through an experimental validation on classical test problems from the DEC-POMDP literature, we compare our approach to existing algorithms. Results show that mathematical programming outperforms dynamic programming but is less efficient than forward search, except for some particular problems. The main contributions of this work are the use of mathematical programming for DEC-POMDPs and a better understanding of DEC-POMDPs and of their solutions. Besides, we argue that our alternate representation of DEC-POMDPs could be helpful for designing novel algorithms looking for approximate solutions to DEC-POMDPs.
A new approach for solving nonlinear constrained mathematical programming problems that makes use of second-order derivative is presented. In this method, a modified optimization problem associated with a primal nonli...
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A new approach for solving nonlinear constrained mathematical programming problems that makes use of second-order derivative is presented. In this method, a modified optimization problem associated with a primal nonlinear programming problem is constructed that involves second-order modified objective function constituting the primal problem. The equivalence between the nonlinear original mathematical programming problem and its associated optimization problem with a second-order modified objective function is established under second-order invexity assumption. In this way, the procedure for obtaining the second-order sufficient conditions for the mathematical programming problems of this type is presented.
A new mathematical programming model for the three-group classification problem is presented. This model is shown to be computationally more efficient than the General Single Function Classification (GSFC) model for a...
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A new mathematical programming model for the three-group classification problem is presented. This model is shown to be computationally more efficient than the General Single Function Classification (GSFC) model for a variety of data configurations. Theoretical characteristics of the classificatory performance of the two models are also investigated. Copyright (C) 1997 Elsevier Science Ltd
Steady-state availability has been widely applied as a measure to evaluate the reliability characteristics of a repairable system. However, it is generally not realistic to make assumptions concerning failure time and...
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Steady-state availability has been widely applied as a measure to evaluate the reliability characteristics of a repairable system. However, it is generally not realistic to make assumptions concerning failure time and repair time distributions. Thus, this article has developed a procedure to construct the membership function for fuzzy steady-state availability. Based on Zadeh's extension principle, a pair of mathematical programs is formulated to find alpha- cuts of fuzzy steady-state availability. An explicit closed-form expression for the membership function is derived by taking the inverse function of the alpha-cut. To illustrate the interpretation and practical value of fuzzy availability in real-world applications, several numerical examples are provided and discussed.
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