Contemporary motor vehicles have increasing numbers of automated functions to augment the safety and comfort of a car. The automotive industry has to incorporate increasing numbers of processing units in the structure...
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Contemporary motor vehicles have increasing numbers of automated functions to augment the safety and comfort of a car. The automotive industry has to incorporate increasing numbers of processing units in the structure of cars to run the software that provides these functionalities. The software components often need access to sensors or mechanical devices which they are designed to operate. The result is a network of hardware units which can accommodate a limited number of software programs, each of which has to be assigned to a hardware unit. A prime goal of this deployment problem is to find software-to-hardware assignments that maximise the reliability of the system. In doing so, the assignments have to observe a number of constraints to be viable. This includes limited memory of a hardware unit, collocation of software components on the same hardware units, and communication between software components. Since the problem consists of many constraints with a significantly large search space, we investigate an ACO and constraint programming (CP) hybrid for this problem. We find that despite the large number of constraints, ACO on its own is the most effective method providing good solutions by also exploring infeasible regions.
Milton Babbitt (1916-2011) was a composer of twelve-tone serial music noted for creating the all-partition array. One part of the problem in generating an all-partition array requires finding a covering of a pitch-cla...
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ISBN:
(纸本)9783319449531;9783319449524
Milton Babbitt (1916-2011) was a composer of twelve-tone serial music noted for creating the all-partition array. One part of the problem in generating an all-partition array requires finding a covering of a pitch-class matrix by a collection of sets, each forming a region containing 12 distinct elements and corresponding to a distinct integer partition of 12. constraint programming (CP) is a tool for solving such combinatorial and constraint satisfaction problems. In this paper, we use CP for the first time to formalize this problem in generating an all-partition array. Solving the whole of this problem is difficult and few known solutions exist. Therefore, we propose solving two sub-problems and joining these to form a complete solution. We conclude by presenting a solution found using this method. Our solution is the first we are aware of to be discovered automatically using a computer and differs from those found by composers.
A conceptual clustering is a set of formal concepts (i.e., closed itemsets) that defines a partition of a set of transactions. Finding a conceptual clustering is an NP-complete problem for which constraint programming...
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ISBN:
(纸本)9783319661582;9783319661575
A conceptual clustering is a set of formal concepts (i.e., closed itemsets) that defines a partition of a set of transactions. Finding a conceptual clustering is an NP-complete problem for which constraint programming (CP) and Integer Linear programming (ILP) approaches have been recently proposed. We introduce new CP models to solve this problem: a pure CP model that uses set constraints, and an hybrid model that uses a data mining tool to extract formal concepts in a preprocessing step and then uses CP to select a subset of formal concepts that defines a partition. We compare our new models with recent CP and ILP approaches on classical machine learning instances. We also introduce a new set of instances coming from a real application case, which aims at extracting setting concepts from an Enterprise Resource Planning (ERP) software. We consider two classic criteria to optimize, i.e., the frequency and the size. We show that these criteria lead to extreme solutions with either very few small formal concepts or many large formal concepts, and that compromise clusterings may be obtained by computing the Pareto front of non dominated clusterings.
constraint programming (CP) is an emergent software technology for declarative description and effective solving of large combinatorial problems especially in the area of integrated production planning. In that contex...
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ISBN:
(纸本)9783540690450
constraint programming (CP) is an emergent software technology for declarative description and effective solving of large combinatorial problems especially in the area of integrated production planning. In that context, CP can be considered as an appropriate framework for development of decision making software supporting small and medium sized enterprises (SMEs) in the course of projects portfolio prototyping. The paper deals with problem considered multiresource problem in which more than one resource type may be required by every activity in the project and the availability of each type is time-windows limited. The problem belongs to a class of NP-complete ones. The aim of the paper is to present a CP based projects portfolio prototyping framework providing a prompt service to a set of routine queries stated both in straight and reverse way, e.g., concerning the projects portfolio makespan implied by a given resources allocation, and the feasible resources allocation guaranteeing an assumed projects portfolio makespan. The way the responses to the routine queries can be guaranteed while may be available in an on-line mode is illustrated in the example enclosed.
constraint programming (CP) is a powerful paradigm for solving combinatorial problems. In CP, the user creates a model by declaring variables with their domains and expresses the constraints that need to be satisfied ...
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ISBN:
(纸本)9781577358350
constraint programming (CP) is a powerful paradigm for solving combinatorial problems. In CP, the user creates a model by declaring variables with their domains and expresses the constraints that need to be satisfied in any solution. The solver is then in charge of finding feasible solutions-a value in the domain of each variable that satisfies all the constraints. The discovery of solutions is done by exploring a search tree that is pruned by the constraints in charge of removing impossible values. The CP framework has the advantage of exposing a rich high-level declarative constraint language for modeling, as well as efficient purpose-specific filtering algorithms that can be reused in many problems. In this work, we harness this flexibility and efficiency for the Block Modeling problem. It is a variant of the graph clustering problem that has been used extensively in many domains including social science, spatio-temporal data analysis and even medical imaging. We present a new approach based on constraint programming, allowing discrete optimization of block modeling in a manner that is not only scalable, but also allows the easy incorporation of constraints. We introduce a new constraint filtering algorithm that outperforms earlier approaches. We show its use in the analysis of real datasets.
Inspired by the geometric reasoning exploited in discrete ellipsoid-based search (DEBS) from the communications literature, we develop a constraint programming (CP) approach to solve problems with strictly convex quad...
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ISBN:
(纸本)9783319449531;9783319449524
Inspired by the geometric reasoning exploited in discrete ellipsoid-based search (DEBS) from the communications literature, we develop a constraint programming (CP) approach to solve problems with strictly convex quadratic constraints. Such constraints appear in numerous applications such as modelling the ground-to-satellite distance in global positioning systems and evaluating the efficiency of a schedule with respect to quadratic objective functions. We strengthen the key aspects of the DEBS approach and implement them as combination of a global constraint and variable/value ordering heuristics in IBM ILOG CP Optimizer. Experiments on a variety of benchmark instances show significant improvement compared to the default settings and state-of-the-art performance compared to competing technologies of mixed integer programming, semi-definite programming, and mixed integer nonlinear programming.
This paper presents a design approach for multi-agent reactive systems in CIM context. This approach allows the user to clearly define its needs and avoid ambiguities and contradictions. Also, it permits to specify th...
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ISBN:
(纸本)0818681489
This paper presents a design approach for multi-agent reactive systems in CIM context. This approach allows the user to clearly define its needs and avoid ambiguities and contradictions. Also, it permits to specify the multi-agent structure of Real Time Software and to make a rigorous verification of these specifications and especially to validate the behaviour of the system. The validation process by reduction in the rewriting logic theory is implemented with the constraint programming language PrologIII. Valid code is automatically generated by using the vertical refinement technical (transformations). This method constitutes a complete approach for the analysis, the structuring and the design of reactive systems.
The Multi-Skill Project Scheduling Problem is a variant of the well-studied Resource Constrained Project Scheduling Problem, in which the resources are assumed to be multi-skilled. Practical applications of this probl...
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ISBN:
(纸本)9783319661582;9783319661575
The Multi-Skill Project Scheduling Problem is a variant of the well-studied Resource Constrained Project Scheduling Problem, in which the resources are assumed to be multi-skilled. Practical applications of this problem occur when the resources considered are a multi-skilled workforce or multi-purpose machines. This variant introduces a set of assignment decisions between the resources and activities, further to the usual scheduling decisions. This additional layer of complexity results in the problem becoming far more difficult to solve. We investigate different constraint programming models and searches tailored for solvers with nogood learning. These models and searches are then evaluated on instances available from the literature as well as newly generated ones. Using the best performing model and search, we are able to close at least 87 open instances from the literature.
Network Function Virtualization (NFV) and Software Defined Networking (SDN) are technologies that recently acquired a great momentum thanks to their promise of being a flexible and cost-effective solution for replacin...
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ISBN:
(纸本)9780998133126
Network Function Virtualization (NFV) and Software Defined Networking (SDN) are technologies that recently acquired a great momentum thanks to their promise of being a flexible and cost-effective solution for replacing hardware-based, vendor-dependent network middleboxes with software appliances running on general purpose hardware in the cloud. Delivering end-to-end networking services across multiple NFV/SDN network domains by implementing the so-called Service Function Chain (SFC) i.e., a sequence of Virtual Network Functions (VNF) that composes the service, is a challenging task. In this paper we address two crucial sub-problems of this task: i) the language to formalize the request of a given SFC to the network and ii) the solution of the SFC design problem, once the request is received. As for i) in our solution the request is built upon the intent-based approach, with a syntax that focuses on asking the user "what" she needs and not "how" it should be implemented, in a simple and high level language. Concerning ii) we define a formal model describing network architectures and VNF properties that is then used to solve the SFC design problem by means of constraint programming (CP), a programming paradigm which is often used in Artificial Intelligence applications. We argue that CP can be effectively used to address this kind of problems because it provides very expressive and flexible modeling languages which come with powerful solvers, thus providing efficient and scalable performance. We substantiate this claim by validating our tool on some typical and non trivial SFC design problems.
Unexpected disruptions such as aircraft failure and airport closure often make the original flight schedule cannot operate regularly and destroy the crew duties. This paper proposed a constraint programming model to s...
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ISBN:
(纸本)9783037859926
Unexpected disruptions such as aircraft failure and airport closure often make the original flight schedule cannot operate regularly and destroy the crew duties. This paper proposed a constraint programming model to solve the crew recovery problem. The total recovery cost was taken as the objective function, temporal-spacial requirements, deadheading and time legalities were considered as constraints. An algorithm based on sequential, least slack and greedy thoughts was designed to search the solution space. Finally, an example was test to indicated feasibility of the proposed model and algorithm.
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