stochastic constraint programming (SCP) is an extension of constraintprogramming for modelling and solving combinatorial problems involving uncertainty. This paper proposes a metaheuristic approach to SCP that can sc...
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stochastic constraint programming (SCP) is an extension of constraintprogramming for modelling and solving combinatorial problems involving uncertainty. This paper proposes a metaheuristic approach to SCP that can scale up to large problems better than state-of-the-art complete methods, and exploits standard filtering algorithms to handle hard constraints more efficiently. For problems with many scenarios it can be combined with scenario reduction and sampling methods.
In this work we introduce a novel approach, based on sampling, for finding assignments that are likely to be solutions to stochasticconstraint satisfaction problems and constraint optimisation problems. Our approach ...
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In this work we introduce a novel approach, based on sampling, for finding assignments that are likely to be solutions to stochasticconstraint satisfaction problems and constraint optimisation problems. Our approach reduces the size of the original problem being analysed;by solving this reduced problem, with a given confidence probability, we obtain assignments that satisfy the chance constraints in the original model within prescribed error tolerance thresholds. To achieve this, we blend concepts from stochastic constraint programming and statistics. We discuss both exact and approximate variants of our method. The framework we introduce can be immediately employed in concert with existing approaches for solving stochasticconstraint programs. A thorough computational study on a number of stochastic combinatorial optimisation problems demonstrates the effectiveness of our approach. (C) 2015 Elsevier B.V. All rights reserved.
stochasticconstraint Satisfaction Problems (SCSPs) are a powerful modeling framework for problems under uncertainty. To solve them is a PSPACE task. The only complete solution approach to date - scenario-based stocha...
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stochasticconstraint Satisfaction Problems (SCSPs) are a powerful modeling framework for problems under uncertainty. To solve them is a PSPACE task. The only complete solution approach to date - scenario-based stochastic constraint programming - compiles SCSPs clown into classical CSPs. This allows the reuse of classical constraint solvers to solve SCSPs, but at the cost of increased space requirements and weak constraint propagation. This paper tries to overcome these drawbacks by automatically synthesizing filtering algorithms for global chance constraints. These filtering algorithms are parameterized by propagators for the deterministic version of the chance constraints. This approach allows the reuse of existing propagators in current constraint solvers and it has the potential to enhance constraint propagation. Our results show that, for the test bed considered in this work, our approach is superior to scenario-based stochastic constraint programming. For these instances, our approach is more scalable, it produces more compact formulations, it is more efficient in terms of run time and more effective in terms of pruning for both stochasticconstraint satisfaction and optimization problems. (C) 2012 Elsevier B.V. All rights reserved.
In this paper we address the general multi-period production/inventory problem with non-stationary stochastic demand and supplier lead-time under service level constraints. A replenishment cycle policy (R"S"...
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In this paper we address the general multi-period production/inventory problem with non-stationary stochastic demand and supplier lead-time under service level constraints. A replenishment cycle policy (R"S") is modeled, where R" is the nth replenishment cycle length and S" is the respective order-up-to-level. We propose a stochastic constraint programming approach for computing the optimal policy parameters. In order to do so, a dedicated global chance-constraint and the respective filtering algorithm that enforce the required service level are presented. Our numerical examples show that a stochastic supplier lead-time significantly affects policy parameters with respect to the case in which the lead-time is assumed to be deterministic or absent. (C) 2010 Elsevier B.V. All rights reserved.
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