The identification of genes and pathways involved in biological processes is a central problem in systems biology. Recent microarray technologies and other high-throughput experiments provide information which sheds l...
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The identification of genes and pathways involved in biological processes is a central problem in systems biology. Recent microarray technologies and other high-throughput experiments provide information which sheds light on this problem. In this article, the authors propose a new computational method to detect active pathways, or identify differentially expressed pathways via integration of gene expression and interactomic data in a sophisticated and efficient manner. Specifically, by using signal-to-noise ratio to measure the differentially expressed level of networks, this problem is formulated as a mixed integer linear programming problem (MILP). The results on yeast and human data demonstrate that the proposed method is more accurate and robust than existing approaches.
This paper investigates the significance of a traffic signal control scheme that simultaneously adjusts all signal parameters, i.e., cycle time, split time and offset, in a road network. A novel framework of model pre...
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This paper investigates the significance of a traffic signal control scheme that simultaneously adjusts all signal parameters, i.e., cycle time, split time and offset, in a road network. A novel framework of model predictive control (MPC) is designed that overcomes the limitations of other MPC based traffic signal control strategies, which are mostly restricted to control only split or green time in a fixed cycle ignoring signal offset. A simple macroscopic model of traffic tailored to MPC is formulated that describes traffic dynamics in the network at a short sampling interval. The proposed framework is demonstrated using a small road network with dynamically changing traffic flows. The parameters of the proposed model are calibrated by using data obtained from detailed microscopic simulation that yields realistic statistics. The model is transformed into a mixed logical dynamical system that is suitable to a finite horizon, and traffic signals are optimized using mixed integer linear programming (MILP) for a given performance index. The framework makes the signals flexibly turn to red and green by adapting quickly to any changes in traffic conditions. Results are also verified by microscopic traffic simulation and compared with other signal control schemes.
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.
This paper proposes a method for short term security-constrained unit commitment (SCUC) for hydro and thermal generation units. The SCUC problem is modeled as a multi-objective problem to concurrently minimize the ISO...
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This paper proposes a method for short term security-constrained unit commitment (SCUC) for hydro and thermal generation units. The SCUC problem is modeled as a multi-objective problem to concurrently minimize the ISO's cost as well as minimizing the emissions caused by thermal units. The non-linearity of valve loading effects is linearized in the presented problem. In order to model the SCUC problem more realistically, this paper considers the dynamic ramp rate of thermal units instead of the fixed rate. Moreover, multi-performance curves pertaining to hydro units are developed and the proposed SCUC problem includes the prohibited operating zones (POZs). Besides, the model of SCUC is transformed into mixed integer linear programming (MILP) instead of using mixedinteger non-linearprogramming (MINLP) which has the capability to be solved efficiently using optimization software even for real size power systems. Pareto optimal solutions are generated by employing lexicographic optimization as well as hybrid augmented-weighted epsilon-constraint technique. Furthermore, a Fuzzy decision maker is utilized in this paper to determine the most preferred solution among Pareto optimal solutions derived through solving the proposed multi-objective SCUC problem. Eventually, the proposed model is implemented on modified IEEE 118-bus system comprising 54 thermal units and 8 hydro units. The simulation results reveal that the solutions obtained from the proposed technique in comparison with other methods established recently are superior in terms of total cost and emission output. (C) 2013 Elsevier Ltd. All rights reserved.
A mixedintegerlinear problem is called symmetric if the variables can be permuted without changing the structure of the problem. Generally, these problems are difficult to solve due to the redundant solutions which ...
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A mixedintegerlinear problem is called symmetric if the variables can be permuted without changing the structure of the problem. Generally, these problems are difficult to solve due to the redundant solutions which populate the enumeration tree. In Unit Commitment problems the symmetry is present when identical generators have to be scheduled. This article presents a way to reduce the computational burden of the Branch and Cut algorithm by adding appropriate inequalities into the mixed-linear formulation of the Unit Commitment problem. In the examples considered, this approach leads to a substantial reduction in computational effort, without affecting the objective value. (C) 2013 Elsevier Ltd. All rights reserved.
The frontier of the Production Possibility Set (PPS) consists of two types of full dimensional facets, efficient and weak facets. Identification of all facets of the PPS can be used in sensitivity and stability analys...
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The frontier of the Production Possibility Set (PPS) consists of two types of full dimensional facets, efficient and weak facets. Identification of all facets of the PPS can be used in sensitivity and stability analysis, to find the closet target for inefficient Decision-Making Units (DMUs), and to determine the status of returns to scale of a DMU, among others. There has been a surge of articles on determining efficient facets in recent years. There are, however, many cases where knowledge of weak facets is required for a thorough analysis. This is the case, in particular, when the frontier of the PPS is constructed only of weak facets. The existing algorithms for finding weak facets either require knowledge of all extreme directions of the PPS or applicable only under some restrictions on the position of weak efficient DMUs. We provide a complete characterization of weak facets. Using this characterization, we then devise a different algorithm to find weak facets. We illustrate our algorithm using a numerical example.
Motivation: Synthetic lethal sets are sets of reactions/genes where only the simultaneous removal of all reactions/genes in the set abolishes growth of an organism. Previous approaches to identify synthetic lethal gen...
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Motivation: Synthetic lethal sets are sets of reactions/genes where only the simultaneous removal of all reactions/genes in the set abolishes growth of an organism. Previous approaches to identify synthetic lethal genes in genome-scale metabolic networks have built on the framework of flux balance analysis (FBA), extending it either to exhaustively analyze all possible combinations of genes or formulate the problem as a bi-level mixed integer linear programming (MILP) problem. We here propose an algorithm, Fast-SL, which surmounts the computational complexity of previous approaches by iteratively reducing the search space for synthetic lethals, resulting in a substantial reduction in running time, even for higher order synthetic lethals. Results: We performed synthetic reaction and gene lethality analysis, using Fast-SL, for genomescale metabolic networks of Escherichia coli, Salmonella enterica Typhimurium and Mycobacterium tuberculosis. Fast-SL also rigorously identifies synthetic lethal gene deletions, uncovering synthetic lethal triplets that were not reported previously. We confirm that the triple lethal gene sets obtained for the three organisms have a precise match with the results obtained through exhaustive enumeration of lethals performed on a computer cluster. We also parallelized our algorithm, enabling the identification of synthetic lethal gene quadruplets for all three organisms in under 6 h. Overall, Fast-SL enables an efficient enumeration of higher order synthetic lethals in metabolic networks, which may help uncover previously unknown genetic interactions and combinatorial drug targets.
We present cutting plane algorithms for the inverse mixed integer linear programming problem (InvMILP), which is to minimally perturb the objective function of a mixedintegerlinear program in order to make a given f...
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We present cutting plane algorithms for the inverse mixed integer linear programming problem (InvMILP), which is to minimally perturb the objective function of a mixedintegerlinear program in order to make a given feasible solution optimal. (C) 2008 Elsevier B.V. All rights reserved.
Biofouling in heat exchangers can be managed by regular cleaning. A mathematical framework for the optimization problem involved in selecting the best cleaning schedules for such units is presented that considers (i) ...
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Biofouling in heat exchangers can be managed by regular cleaning. A mathematical framework for the optimization problem involved in selecting the best cleaning schedules for such units is presented that considers (i) an induction period associated with conditioning and colonization, which introduces complexity to the fouling kinetics, and (ii) the existence of several outcomes from cleaning, depending on the choice of cleaning method. The problem is to decide how, when, and which exchanger to clean. A mixedinteger nonlinearprogramming approach, based on the use of a logistic function to model fouling resistance-time dynamics, is shown to give tractable results. The methodology is illustrated with a case study involving a small network of three heat exchangers. An optimized solution based on a cost/performance analysis shows that the cleaning intervals and cleaning methods differ for each exchanger.
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