Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 39 for “"Constraint programming"”.
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A Constraint Programming Pre-processor for Duty Scheduling
… this problem is solved using mathematical programming methods. The University of Leeds has developed a driver scheduling system, TRACS-I I, that solves the bus driver scheduling problem by first generating a large set of potential duties and selecting a subset of these via the associated …
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Solving Single-Track Railway Scheduling Problem Using Constraint Programming
… is found in practice by adding minimum headway constraints between trains into their model. Two strategies for resolving the conflicts in a desired timetable are presented. The two strategies have their applicability in practice. For instance, resolving a conflict in the first strategy stems …
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Constraint programming for optimization under uncertainty in inventory control
Constraint Programming (CP) is a programming paradigm where relations between variables can be stated in the form of constraints. CP features discrete domains and global constraints. Global constraints capture interesting substructures of a problem, encapsulate dedicated inference algorithms based …
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Constraint Programming Approaches to Electric Vehicle and Robot Routing Problems
… The central thesis of this dissertation is that constraint programming (CP) can be an effective and flexible paradigm for modeling and solving routing problems involving electric vehicles. While efforts on the development of mixed-integer linear programming (MILP) approaches to electric vehicle …
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Investigation of matching problems using constraint programming and optimisation methods
… examine the popular matching in the context of constraint programming using global constraints. We discuss the possibility to find a popular matching even for the instances that does not admit one. The second part of the thesis focuses on non-bipartite graphs, i.e. the kidney exchange problem, a …
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Efficient algorithms and representations for chance-constrained mixed constraint programming
… difficult to define, for example when imposing constraints on remote science missions. Without well-defined costs of failure, it is difficult to balance the risks of failure against the rewards of success. This motivates an alternative approach, in which we define what it means to fail, and look …
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Use of isoperformance, constraint programming, and mixed integer linear programing for architecture tradespace exploration of passive Optical Earth Observation Systems
… uses automation techniques, isoperformance, and constraint programming to rapidly construct potential space-based passive optical EO sensor architecture concepts which meet a given set of customer requirements. Cost estimates are also generated for each sensor concept via integration with …
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Extending the relational model with constraint satisfaction
We propose a new approach to data driven constraint programming. By extending the relational model to handle constraints and variables as first class citizens, we are able to express first order logic SAT problems using an extended SQL which we refer to as SAT/SQL. With SAT/SQL, one can efficiently …
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Robust solutions for constraint satisfaction and optimisation under uncertainty.
… a framework for finding robust solutions of constraint programs. Our approach is based on the notion of fault tolerance. We formalise this concept within constraint programming, extend it in several dimensions and introduce some algorithms to find robust solutions efficiently. When applying …
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Constraint satisfaction approaches to bus driver scheduling
… driver scheduling often use mathematical programming combined with heuristics. Purely heuristic approaches have found it very difficult to produce efficient driver schedules for large scheduling problems. Furthermore, some of these approaches may not be easily adaptable to different …
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Computing policy parameters for stochastic inventory control using stochastic dynamic programming approaches
… herein is the usage of stochastic dynamic programming approaches, a mathematical programming technique introduced by Bellman. Stochastic dynamic programming is hybridised with branch-and-bound, binary search, constraint programming and other computational techniques to develop innovative …
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Models and Algorithms for School Timetabling
In constraint programming, combinatorial problems are specified declaratively in terms of constraints. Constraints are relations over problem variables that define the space of solutions by specifying restrictions on the values that variables may take simultaneously. To solve problems stated in …
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Structural analysis of combinatorial optimization problem characteristics and their resolution using hybrid approaches
… structure, typically being dirtied by side constraints, or being composed of two or more sub-problems, usually not disjoint. Such problems are not suitable to be solved with pure approaches based on a single programming paradigm, because a paradigm that can effectively face a problem …
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STRENGTH EVALUATION OF CRYPTOGRAPHIC PRIMITIVES TO LINEAR, DIFFERENTIAL AND ALGEBRAIC ATTACKS.
… differential and linear trails with the constraint programming language Minizinc.
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A specialised constraint approach for stable matching problems
Constraint programming is a generalised framework designed to solve combinatorial problems. This framework is made up of a set of predefined independent components and generalised algorithms. This is a very versatile structure which allows for a variety of rich combinatorial problems to be …
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Heuristically guided constraint satisfaction for AI planning
… using alternative techniques. One such paradigm, Constraint Programming (CP), has successfully been used in various planner architectures in recent years. The efficacy of a given constraint reformulation depends on the encoding method, search technique(s) employed, and the consequent amount of …
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Solver Tuning with Genetic Algorithms
Currently the parameters in a constraint solver are often selected by hand by experts in the field; these parameters might include the level of preprocessing to be used, the variable ordering heuristic or the suitable modelling approach. The efficient and automatic mechanism of parameters tuning …
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Resource-Constrained Project Scheduling with Autonomous Learning Effects
… problems to this model. Four different Constraint Programming model formulations are developed to efficiently solve the model. Bounding techniques are proposed for tightening optimality gaps, including four lower bounding model relaxations, an upper bounding model relaxation, and a …
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