Multivalued Decision Diagrams (MDDs) are efficient data structures widely used in several fields like verification, optimization and dynamic programming. In this thesis, we first focus on improving the main algorithms...
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Multivalued Decision Diagrams (MDDs) are efficient data structures widely used in several fields like verification, optimization and dynamic programming. In this thesis, we first focus on improving the main algorithms such as the re- duction, allowing MDDs to potentially exponentially compress set of tuples, or the combination of MDDs such as the intersection of the union. We go further by designing parallel algorithms, and algorithms handling non-deterministic MDDs. We then investigate relaxed MDDs, that are more and more used in optimization, and define the notions of relaxed reduction or operation and de- sign efficient algorithms for them. The sampling of solutions stored in a MDD is solved with respect to probability mass functions or Markov chains. In order to combine MDDs with constraint programming, we design the propagators of all the types of MMDD constraints in solvers, and introduce a new one, the channeling constraint. These new propagators outperform the existing ones and allow the reformulation of several other constraints such as the dispersion constraint, and even to define new ones easily. We finally apply our algorithm to several real world industrial problems such as text and music generation and geomodeling of a petroleum reservoir.
The basic goal of data mining is to discover patterns occurring in the databases, such as associations, classification models, sequential patterns, and so on. In this paper we focus on the problem of frequent pattern ...
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ISBN:
(纸本)9781618040114
The basic goal of data mining is to discover patterns occurring in the databases, such as associations, classification models, sequential patterns, and so on. In this paper we focus on the problem of frequent pattern discovery, which is the process of searching for patterns such as sets of features or items that appear in data frequently. Such frequent patterns can reveal associations, correlations, and many other interesting relationships hidden in a database. Most of frequent pattern mining systems in the market are too generic and become inefficient when set of patterns is large and the frequent patterns are very long. A new trend in data mining is a scalable method that uses constraints to guide the system in its search for interesting patterns. Our main research objective is the development of constraint-based mining methodology and this paper presents the preliminary results of our study and prototype development. We present the implementation of frequent pattern mining system based on declarative programming paradigm using logic programming and constraint logic programming. The comparative performance studies on speed and memory usage of logic versus constraint programming are also reported in the paper.
This paper solves the well-known multi-mode resource-constrained project scheduling problem (MRCPSP). The problem type is known to be NP-hard and has been solved using various exact as well as (meta-) heuristic proced...
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Syllogisms and syllogistic reasoning has been the subject of research and scientific discourse for more than two millennia. Syllogisms sum quantified assertions into an overall statement, usually consisting of two pre...
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Syllogisms and syllogistic reasoning has been the subject of research and scientific discourse for more than two millennia. Syllogisms sum quantified assertions into an overall statement, usually consisting of two premises and one conclusion. While syllogistic reasoning can be modeled by classical first-order logic in a straightforward manner, it is an open question which of the possible syllogisms are accepted as valid by human reasoners. In this paper, we present an approach that models the reasoning process with seven spaces of a set diagram. It can easily be implemented by constraint logic programming. We distinguish several assumptions that humans may make during their reasoning process, in particular that all used categories are non-empty. In contrast to pure logic-based approaches, the proposed procedure allows to represent diverse models human reasoners may follow. The results show good correlation and coincidence with psychological investigations.
This thesis mainly concerns the further development of Sequence Planner (SP), a tool used for verification and optimization of operation sequences. The work was conducted on a virtual robotic production cell (at Volvo...
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This thesis mainly concerns the further development of Sequence Planner (SP), a tool used for verification and optimization of operation sequences. The work was conducted on a virtual robotic production cell (at Volvo Cars) modeled in the virtual commissioning software Process Simulate (PS). A way to handle more complex robot sequences and other resources, such as fixtures was also developed in this project. Sequence Planner is now capable of importing and modeling the robots as well as some fixtures from the virtual PS model. The SP model can be manually modified and added to, whereafter it can be verified by means of supervisory control theory, ensuring a non-blocking and deadlock free system. Using constraint programming, a set of feasible operation sequences can be produced and sorted according to their total execution time. The sequences can then be sent back to the virtual commis- sioning tool for further testing and verification.
constraint Answer Set programming (CASP) is a convenient integration of the Answer Set programming (ASP) paradigm with constraint programming (CP), which was exploited for a range of applications. HEX-programs are ano...
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Schedules and physical workspaces are two key elements of linear construction projects that are extremely interdependent. Any negligence in incorporating spatial and temporal constraints in developing and improving sc...
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Schedules and physical workspaces are two key elements of linear construction projects that are extremely interdependent. Any negligence in incorporating spatial and temporal constraints in developing and improving schedules of linear projects results in inevitable delays and workspace congestions and can substantially hinder the performance of the activity resources. This study augments the current linear scheduling methods by presenting an uncertainty-aware optimization framework to optimize the duration of linear projects while minimizing their potential congestions. The methodology is built upon the new concept of space-time float for explicit consideration of spatio-temporal constraints of activities and their inherent uncertainty. A constraint satisfaction approach was used for the two-tier optimization of duration and congestion. A fuzzy inference system was also incorporated to assess the inherent uncertainty in the schedule. Two case examples from literature are analyzed. The results demonstrate the effectiveness of the proposed method in planning and control of the unforeseen variations from planned schedules of linear projects. (C) 2016 American Society of Civil Engineers.
The Dynamic Facility Layout Problem (DFLP) is designing a facility over a multi-period planning horizon where the interdepartmental material flows change from one period to the next one due to changes in product deman...
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The Dynamic Facility Layout Problem (DFLP) is designing a facility over a multi-period planning horizon where the interdepartmental material flows change from one period to the next one due to changes in product demands. The DFLP is used while designing manufacturing and logistics facilities over multiple planning periods; however, it is a very challenging nonlinear optimization problem. In this paper, a zone-based block layout is used to design manufacturing and logistics facilities over multiple planning periods. A zone-based block layout inherently includes possible aisle structures, which can easily be adapted to different material handling systems. The unequal area DFLP is modeled and solved using a zone-based structure where the dimensions of the departments are decision variables, and the departments are assigned to flexible zones with a pre-structured positioning. A matheuristic approach, which combines concepts from Tabu Search (TS) and mathematical programming, is proposed to solve the zone-based DFLP on the continuous plane with unequal area departments. The TS determines the relative locations of departments and their assignments to zones while their exact locations and shapes are calculated by the mathematical programming. Numerical results for a set of test problems from the literature showed that our proposed matheuristic approach is promising.
IBM ILOG CP Optimizer is a constraint solver that implements a model-and-run paradigm. For scheduling problems, CP Optimizer provides a relatively simple but very expressive modeling language based on the notion of in...
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IBM ILOG CP Optimizer is a constraint solver that implements a model-and-run paradigm. For scheduling problems, CP Optimizer provides a relatively simple but very expressive modeling language based on the notion of interval variables. This paper presents the temporal linear relaxation (TLR) used to guide the automatic search when solving scheduling problems that involve temporal and resource allocation costs. We give the rationale of the TLR, describe its integration in the automatic search of CP Optimizer, and present the relaxation of most of the constraints and expressions of the model. An experimental study on a set of classical scheduling benchmarks shows that using the TLR is essential for problems with irregular temporal costs and generally helps for problems with resource allocation costs.
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