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.
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.
This paper addresses the robust two-machine permutation flow-shop scheduling problem considering non-deterministic operation processing times associated with an uncertainty budget. The objective is to minimize the mak...
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ISBN:
(纸本)9783031332708;9783031332715
This paper addresses the robust two-machine permutation flow-shop scheduling problem considering non-deterministic operation processing times associated with an uncertainty budget. The objective is to minimize the makespan of the schedule. Exact solution methods incorporated within the framework of a two-stage robust optimization are proposed to solve the problem. We first prove that under particular conditions the robust two-machine permutation flow-shop scheduling problem can be solved in polynomial time by the well-known Johnson's algorithm usually dedicated to the deterministic version. Then we tackle the general problem, for which we propose a column and constraint generation algorithm. We compare two versions of the algorithm. In the first version, a mixed-integer linear programming formulation is used for the master problem. In the second version, we use a constraint programming model for the master problem. To the best of our knowledge, the use of constraint programming for a master problem in a two-stage robust optimization problem is innovative. The experimental results show the very good performance of the method based on the constraint programming formulation. We also notice that Johnson's algorithm is surprisingly efficient for the robust version of the general problem.
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.
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.
The Spatial Packaging Problem (SPP) aims to solve a mixture of the 3D Packing Problem (3DPP) and the 3D Pipe-Routing Problem. The main feature that distinguishes the SPP from the traditional 3DPP is the interconnectio...
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ISBN:
(纸本)9783319339542;9783319339535
The Spatial Packaging Problem (SPP) aims to solve a mixture of the 3D Packing Problem (3DPP) and the 3D Pipe-Routing Problem. The main feature that distinguishes the SPP from the traditional 3DPP is the interconnections that exist between its components. The SPP is more challenging because the shape and dimensions of the interconnections are unknown, and must be determined as part of the solution. In this paper, we propose a relaxation, a constraint programming model and a search heuristic to solve the SPP. We relax the SPP by using taxicab geometry and model it as a constraint satisfaction problem, then solve it by using a search heuristic based on interconnection volumes. The proposed approach has been evaluated on a challenging benchmark that reflects a range of aerospace and commercial applications varying in number of components and interconnections. The preliminary results show the effectiveness and efficiency of the proposed approach.
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.
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.
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.
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