The problem of gene regulatory network inference is a major concern of systems biology. In recent years, a novel methodology has gained momentum, called community network approach. Community networks integrate predict...
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The problem of gene regulatory network inference is a major concern of systems biology. In recent years, a novel methodology has gained momentum, called community network approach. Community networks integrate predictions from individual methods in a "metapredictor," in order to compose the advantages of different methods and soften individual limitations. This article proposes a novel methodology to integrate prediction ensembles using constraint programming, a declarative modeling and problem solving paradigm. constraint programming naturally allows the modeling of dependencies among components of the problem as constraints, facilitating the integration and use of different forms of knowledge. The new paradigm, referred to as constrained community network, uses constraints to capture properties of the regulatory networks (e.g., topological properties) and to guide the integration of knowledge derived from different families of network predictions. The article experimentally shows the potential of this approach: The addition of biological constraints can offer significant improvements in prediction accuracy.
This paper aims at providing a fast near-optimum solution to the multimode resource-constrained project scheduling problem (MRCPSP) in large-scale projects, with and without resource-leveling constraints. The MRCPSP p...
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This paper aims at providing a fast near-optimum solution to the multimode resource-constrained project scheduling problem (MRCPSP) in large-scale projects, with and without resource-leveling constraints. The MRCPSP problem is known to be nondeterministic polynomial-time hard (NP-hard) and has been solved using various exact, heuristic, and metaheuristic procedures. In this paper, constraint programming (CP) is used as an advanced mathematical optimization technique that suits scheduling problems. The IBM ILOG modeling software and its CPLEX-CP solver engine have been used to develop a CP optimization model for the MRCPSP problem. Unlike many metaheuristic methods in literature, the CP model is fast and provides a near-optimum solution to the MRCPSP for projects with hundreds of activities within minutes. The paper compares the CP results with two case studies from the literature to prove the practicality and usefulness of the CP approach to both researchers and practitioners. One case study was used as the basis for creating larger projects with up to 2,000 activities. The results reported in this paper can be used as a benchmark for other researchers to compare and improve. This research contributes to developing a practical decision support system for resolving real-life constraints in projects. (C) 2014 American Society of Civil Engineers.
This paper surveys recent applications and advances of the constraint programming-based column generation framework, where the master subproblem is solved by traditional OR techniques, while the pricing subproblem is ...
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This paper surveys recent applications and advances of the constraint programming-based column generation framework, where the master subproblem is solved by traditional OR techniques, while the pricing subproblem is solved by constraint programming (CP). This framework has been introduced to solve crew assignment problems, where complex regulations make the pricing subproblem demanding for traditional techniques, and then it has been applied to other contexts. The main benefits of using CP are the expressiveness of its modeling language and the flexibility of its solvers. Recently, the CP-based column generation framework has been applied to many other problems, ranging from classical combinatorial problems such as graph coloring and two dimensional bin packing, to application oriented problems, such as airline planning and resource allocation in wireless ad hoc networks.
This paper describes a development of a schedule guidance system for a slab extraction sequence from the furnace. To improve productivity, it is necessary to optimize not only the extraction sequence but also a select...
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ISBN:
(纸本)9781479978632
This paper describes a development of a schedule guidance system for a slab extraction sequence from the furnace. To improve productivity, it is necessary to optimize not only the extraction sequence but also a selection of the cooling facility. This scheduling problem is a multi-mode resource constraint project scheduling problem (multi-mode RCPSP) since it includes resource constraints and alternative processes that differ in a process time and occupied resources. The system is required to solve this problem in a short time in order to quickly redisplay a modified schedule, in which the difference between the actual and predicted process time due to an operational disturbance has been resolved. Moreover, the derived schedule from the system is expected to outperform the human operator's result in the point of productivity and have high maintainability to respond flexibly to changes in operational constraints. We investigated some methods to solve this problem. As a result, we applied constraint programming (CP) which can solve this problem in realistic time and find semi-optimal solution.
Genome Rearrangements addresses the problem of finding the minimum number of global operations, such as transpositions, reversals, fusions and fissions that transform a given genome into another. In this paper we deal...
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ISBN:
(纸本)9783642032226
Genome Rearrangements addresses the problem of finding the minimum number of global operations, such as transpositions, reversals, fusions and fissions that transform a given genome into another. In this paper we deal with transposition events, which are events that change the position of two contiguous block of genes in the same chromosome. The transposition event generates the transposition distance problem, that is to find the minimum number of transposition that transform one genome (or chromosome) into another. Although some tractables instances were found [20, 14], it is not known if an exact polynomial time algorithm exists. Recently, Dias and Souza [9] proposed polynomial-sized Integer Linear programming (ILP) models for rearrangement distance problems where events are restricted to reversals, transpositions or a combination of both. In this work we devise a slight different approach. We present some constraint Logic programming (CLP) models for transposition distance based on known bounds to the problem.
constraint programming (CP) allows to solve constraint satisfaction and optimization problems by building and then exploring a search tree of potential solutions. Potential solutions are generated by firstly selecting...
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ISBN:
(纸本)9781467398183
constraint programming (CP) allows to solve constraint satisfaction and optimization problems by building and then exploring a search tree of potential solutions. Potential solutions are generated by firstly selecting a variable and then a value from the given problem, phase known as enumeration. In this context, Autonomous Search (AS) that is a particular case of adaptive systems, enables the problem solver to control and adapt its internal configuration during solving time, based on performance metrics in order to be more efficient. The goal is to provide a mechanism for CP solvers, integrating a component able to evaluate the solving performance process. In particular, we employ a classic decision making method called Choice Function (CF). In this paper, we present an evaluation of different choice functions, based on performance exhibited in a indicators set. The results are promising and show that it is feasible to solve constraint satisfaction problems with this new technique.
Trees are a useful framework for classifying entities whose attributes are, at least partially, related through a common ancestry, such as species of organisms, family members or languages. In some common applications...
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Trees are a useful framework for classifying entities whose attributes are, at least partially, related through a common ancestry, such as species of organisms, family members or languages. In some common applications, such as phylogenetic trees based on DNA sequences, relatedness can be inferred from the statistical analysis of unweighted attributes. The vast majority of mutations that survive across generations are evolutionarily neutral, which means that most genetic differences between species will have accumulated independently and randomly. In these cases, it is possible to calculate the tree from a precomputed matrix of distances. In other cases, such as with anatomical traits or languages, the assumption of random and independent differences does not hold, making it necessary to consider some traits to be more relevant than others for determining how related two entities are. In this paper, we present a constraint programming approach that can enforce consistency between bounds on the relative weight of each trait and tree topologies, so that the user can best determine which sets of traits to use and how the entities are likely to be related.
In recent times, linear project resource leveling based on the linear scheduling method (LSM) has attracted considerable interest owing to the unique advantages of applying the LSM to linear projects. In the research ...
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In recent times, linear project resource leveling based on the linear scheduling method (LSM) has attracted considerable interest owing to the unique advantages of applying the LSM to linear projects. In the research reported in this paper, the linear project resource leveling problem was described as a constraint satisfaction problem based on analyses conducted in previous studies and a two-stage scheduling model for resource leveling of linear projects based on the LSM was proposed. The optimization process was reasonably set so as to fully utilize the rate float of the activity to obtain a more optimal schedule. The constraint programming (CP) technique was used for solving this problem. Based on the proposed scheduling model and algorithm, a two-stage scheduling system for resource leveling of linear projects was developed for automatically establishing a linear schedule for resource leveling. The effectiveness of the proposed model and algorithm was verified for a highway construction project reported previously. (C) 2014 American Society of Civil Engineers.
Cyclic scheduling problems consist in ordering a set of activities executed indefinitely over time in a periodic fashion, subject to precedence and resource constraints. This class of problems has many applications in...
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Cyclic scheduling problems consist in ordering a set of activities executed indefinitely over time in a periodic fashion, subject to precedence and resource constraints. This class of problems has many applications in manufacturing, embedded systems and compiler design, production and chemical systems. This paper proposes a constraint programming approach for cyclic scheduling problems, based on modular arithmetic: in particular, we introduce a modular precedence constraint and a global cumulative constraint along with their filtering algorithms. We discuss two possible formulations. The first one (referred to as CROSS) models a pure cyclic scheduling problem and makes use of both our novel constraints. The second formulation (referred to as CROSS*) introduces a restrictive assumption to enable the use of classical resources constraints, but may incur a loss of solution quality. Many traditional approaches to cyclic scheduling operate by fixing the period value and then solving a linear problem in a generate-and-test fashion. Conversely, our technique is based on a non-linear model and tackles the problem as a whole: the period value is inferred from the scheduling decisions. Our approach has been tested on a number of non-trivial synthetic instances and on a set of realistic industrial instances. The method proved to effective in finding high quality solutions in a very short amount of time. (C) 2013 Elsevier B.V. All rights reserved.
This paper provides a novel perspective in the protein structure prediction (PSP) problem. The PSP problem focuses on determining putative 3D structures of a protein starting from its primary sequence. The proposed ap...
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This paper provides a novel perspective in the protein structure prediction (PSP) problem. The PSP problem focuses on determining putative 3D structures of a protein starting from its primary sequence. The proposed approach relies on a multi-agent system (MAS) perspective, where concurrent agents explore the folding of different parts of a protein. The strength of the approach lies in the agents' ability to apply different types of knowledge, expressed in the form of declarative constraints, to prune the search space of folding alternatives. The paper makes also an important contribution in demonstrating the suitability of a general-purpose graphical processing unit approach to implement such MAS infrastructure, with significant performance improvements over the sequential implementation and other methods.
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