An intelligent robotic system must be capable of making the best decision at any given moment. The criteria for which task is "best" can be derived by performance metrics as well as the ability for it to sat...
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An intelligent robotic system must be capable of making the best decision at any given moment. The criteria for which task is "best" can be derived by performance metrics as well as the ability for it to satisfy all constraints upon the robot and its mission. constraints may exist based on safety, reliability, accuracy, etc. This paper presents a decision framework capable of assisting a robotic system to select a task that satisfies all constraints as well as is optimized based upon one or more performance criteria. The framework models this decision process as a constraint satisfaction problem using techniques and algorithms from constraint programming and constraint optimization in order to provide a solution in real-time. This paper presents this framework and initial results provided through two demonstrations. The first utilizes simulation to provide an initial proof of concept, and the second, a security robot demonstration, is performed using a physical robot.
Everyday more and more complex and critical processes of organizations' services and operations are automated by using business process management systems. Thereby, there exists a growing interest in improving the...
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Everyday more and more complex and critical processes of organizations' services and operations are automated by using business process management systems. Thereby, there exists a growing interest in improving the quality of these processes (e. g., by avoiding functional faults) to ensure the reachability of business goals and, consequently, for organizations to become more competitive. To this end, four contributions to apply diagnosis techniques for identifying and isolating functional faults at both design-time and run-time are proposed.
This paper presents a methodology based on the Variable Neighbourhood Search metaheuristic, applied to the Capacitated Vehicle Routing Problem. The presented approach uses constraint programming and Lagrangean Relaxat...
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This paper presents a methodology based on the Variable Neighbourhood Search metaheuristic, applied to the Capacitated Vehicle Routing Problem. The presented approach uses constraint programming and Lagrangean Relaxation methods in order to improve algorithm's efficiency. The complete problem is decomposed into two separated sub problems, to which the mentioned techniques are applied to obtain a complete solution. With this decomposition, the methodology provides a quick initial feasible solution which is rapidly improved by metaheuristics' iterative process. constraint programming and Lagrangean Relaxation are also embedded within this structure to ensure constraints satisfaction and to reduce the calculation burden. By means of the proposed methodology, promising results have been obtained. Remarkable results presented in this paper include a new best-known solution for a rarely solved 200-customers test instance, as well as a better alternative solution for another benchmark problem.
Because of the bottlenecking operations in a complex coal rail system, millions of dollars are costed by mining companies. To handle this issue, this paper investigates a real-world coal rail system and aims to optimi...
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Because of the bottlenecking operations in a complex coal rail system, millions of dollars are costed by mining companies. To handle this issue, this paper investigates a real-world coal rail system and aims to optimise the coal railing operations under constraints of limited resources (e.g., limited number of locomotives and wagons). In the literature, most studies considered the train scheduling problem on a single-track railway network to be strongly NP-hard and thus developed metaheuristics as the main solution methods. In this paper, a new mathematical programming model is formulated and coded by optimization programming language based on a constraint programming (CP) approach. A new depth-first-search technique is developed and embedded inside the CP model to obtain the optimised coal railing timetable efficiently. Computational experiments demonstrate that high-quality solutions are obtainable in industry-scale applications. To provide insightful decisions, sensitivity analysis is conducted in terms of different scenarios and specific criteria.
The field of data mining has become accustomed to specifying constraints on patterns of interest. A large number of systems and techniques has been developed for solving such constraint-based mining problems, especial...
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The field of data mining has become accustomed to specifying constraints on patterns of interest. A large number of systems and techniques has been developed for solving such constraint-based mining problems, especially for mining itemsets. The approach taken in the field of data mining contrasts with the constraint programming principles developed within the artificial intelligence community. While most data mining research focuses on algorithmic issues and aims at developing highly optimized and scalable implementations that are tailored towards specific tasks, constraint programming employs a more declarative approach. The emphasis lies on developing high-level modeling languages and general solvers that specify what the problem is, rather than outlining how a solution should be computed, yet are powerful enough to be used across a wide variety of applications and application domains. This paper contributes a declarative constraint programming approach to data mining. More specifically, we show that it is possible to employ off-the-shelf constraint programming techniques for modeling and solving a wide variety of constraint-based itemset mining tasks, such as frequent, closed, discriminative, and cost-based itemset mining. In particular, we develop a basic constraint programming model for specifying frequent itemsets and show that this model can easily be extended to realize the other settings. This contrasts with typical procedural data mining systems where the underlying procedures need to be modified in order to accommodate new types of constraint, or novel combinations thereof. Even though the performance of state-of-the-art data mining systems outperforms that of the constraint programming approach on some standard tasks, we also show that there exist problems where the constraint programming approach leads to significant performance improvements over state-of-the-art methods in data mining and as well as to new insights into the underlying data mining probl
During the past decade, inland vessels have gained importance in container transport because of their reliability, low enviromnental impact, and major capacity for increased exploitation. Although inland vessels are c...
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During the past decade, inland vessels have gained importance in container transport because of their reliability, low enviromnental impact, and major capacity for increased exploitation. Although inland vessels are crucial in container transport between terminals in the port and the hinterland, in a large seaport like the one in Rotterdam, Netherlands, only 62% of the inland vessels leave the port on time. The other vessels have to stay in the port area for a longer time than planned. This situation leads to uncertainty in waiting times of vessels at terminals and low utilization of terminal quay resources. A two-phase approach is proposed that integrates mixed-integer programming (MIP) and constraint programming (CP) to solve the problem by generating optimal rotation plans for inland vessels. In the first phase, the single-vessel optimization problem is formulated on the basis of MIP and solved with state-of-the-art MIP solvers. In the second phase, the multiple-vessel coordination problem is formulated on the basis of CP, and a large neighborhood search based heuristic is proposed to solve the problem. Commercial CP solvers are also used for comparison. Simulation results show that the proposed large neighborhood search based heuristic outperforms the commercial CP solver with regard to both the solution quality and the computation time. Moreover, simulation results with respect to departure time of the last vessel, total sojourn time, and waiting time show significant improvement with earlier departure times and shorter sojourn times and waiting times.
Tolerant algebraic side-channel attack (TASCA) exploits side-channel information with an algebraic formulation of a cipher to exploit its weaknesses and recover a secret key. Its inputs consist of a side-channel trace...
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Tolerant algebraic side-channel attack (TASCA) exploits side-channel information with an algebraic formulation of a cipher to exploit its weaknesses and recover a secret key. Its inputs consist of a side-channel trace of an encryption and the clear and cipher texts. TASCA demonstrated that pseudo-Boolean optimization can successfully recover a key with reasonable computational efforts. Unlike Boolean Satisfiability (SAT), constraint programming (CP) is an optimization technology that favors high-level, rich and expressive models that is ideal to naturally model and solve cryptanalysis challenges. It offers direct encoding of bit-wise operations and avoids costly bit-blasting formulation required by SAT and pseudo-Boolean solvers. TASCA-CP is an embodiment of TASCA and is used to attack AES-128 as well as AES-256 to recover keys when noisy side-channel measurements are available. It achieves this task orders of magnitude faster than the original TASCA approach. TASCA-CP, with its performance, enables cryptanalysts to explore larger key-sizes and probe weaknesses of ciphers. The article demonstrates, with an attack on Keeloq, that a high-level modeling approach is essential to easily adapt to different ciphers. The empirical evaluation establishes the performance of the system when compared to the original TASCA implementation on modern IP solvers and identical hardware.
We address the runway scheduling problem under consideration of winter operations. During snowfall, runways have to be temporarily closed in order to clear them from snow, ice and slush. We propose an integrated optim...
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We address the runway scheduling problem under consideration of winter operations. During snowfall, runways have to be temporarily closed in order to clear them from snow, ice and slush. We propose an integrated optimization model to simultaneously plan snow removal for multiple runways and to assign runways and take-off and landing times to aircraft. For this winter runway scheduling problem, we present a time-discrete binary model formulation using clique inequalities and an equivalent constraint programming model. To solve the winter runway scheduling problem optimally, we propose an exact solution methodology. Our start heuristic based on constraint programming generates a feasible initial start solution. We use a column generation scheme, which we initialize with a heuristic solution, to identify all variables of the binary program which are required to solve it optimally. Finally, we apply a branch-andbound procedure to our resulting binary program. Additionally, we present an enhanced time discretization method to balance model size and solution quality. We apply our algorithm to realistic instances from a large international airport. An analysis of resulting model sizes proves the ability of our approach to significantly reduce the number of required variables and constraints of the time-discrete binary program. We also show that our method computes optimal schedules in a short amount of time and often outperforms a time-continuous formulation as well as a pure constraint programming approach. (c) 2021 Elsevier B.V. All rights reserved.
We study a hybrid MIP/CP solution approach in which CP is used for detecting infeasibilities and generating cuts within a branch-and-cut algorithm for MIP. Our framework applies to MIP problems augmented by monotone c...
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We study a hybrid MIP/CP solution approach in which CP is used for detecting infeasibilities and generating cuts within a branch-and-cut algorithm for MIP. Our framework applies to MIP problems augmented by monotone constraints that can be handled by CP. We illustrate our approach on a generic multiple machine scheduling problem, and present a number of computational experiments. (c) 2005 Elsevier Ltd. All rights reserved.
constraint programming uses enumeration and search tree pruning to solve combinatorial optimization problems. In order to speed up this solution process, we investigate the use of semidefinite relaxations within const...
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constraint programming uses enumeration and search tree pruning to solve combinatorial optimization problems. In order to speed up this solution process, we investigate the use of semidefinite relaxations within constraint programming. In principle, we use the solution of a semidefinite relaxation to guide the traversal of the search tree, using a limited discrepancy search strategy. Furthermore, a semidefinite relaxation produces a bound for the solution value, which is used to prune parts of the search tree. Experimental results on stable set and maximum clique problem instances show that constraint programming can indeed greatly benefit from semidefinite relaxations. (c) 2005 Elsevier Ltd. All rights reserved.
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