Existing training techniques for spiking neuronal networks tend to be monolithic in nature and scale poorly to larger networks. This paper presents a technique for combining multiple functional neural groupings into a...
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Existing training techniques for spiking neuronal networks tend to be monolithic in nature and scale poorly to larger networks. This paper presents a technique for combining multiple functional neural groupings into a more complex composite network. This is accomplished by ensuring that four axioms hold true for the composite network. The axioms were designed to ensure that incoming signals arrive simultaneously to any component groupings. A number of experiments were conducted in which an algorithm implementing the axioms was used to combine component groupings into more complex networks;these experiments show the practical utility of the technique and reinforce by demonstration the correctness of the axioms.
The Chinese postman problem with time windows (CPPTW) is modelled as a constraint programme and results are reported for a set of test problems with up to 69 edges. Two different formulations are proposed. The first f...
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The Chinese postman problem with time windows (CPPTW) is modelled as a constraint programme and results are reported for a set of test problems with up to 69 edges. Two different formulations are proposed. The first formulation approaches the problem directly and the second transforms the problem to an equivalent vehicle routing problem with time windows. The results demonstrate that optimal solutions can be obtained quickly when the time windows are tight. However, the results also show that as the time windows are made wider and the number of feasible solutions increases, these constraint programming formulations take significantly longer to find a provably optimal solution. The results also demonstrate how the size and density of the graph affects the computing time needed to find an optimal solution. (c) 2005 Published by Elsevier Ltd.
constraint Databases represent complex data by means of formulas described by constraints (equations, inequations or Boolean combinations of both). Commercial database management systems allow the storage and efficien...
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constraint Databases represent complex data by means of formulas described by constraints (equations, inequations or Boolean combinations of both). Commercial database management systems allow the storage and efficient retrieval of classic data, but for complex data a made-to-measure solution combined with expert systems for each type of problem are necessary. Therefore, in the same way as commercial solutions of relational databases permit storing and querying classic data, we propose an extension of the Selection Operator for complex data stored, and an extension of SQL language for the case where both classic and constraint data need to be managed. This extension shields the user from unnecessary details on how the information is stored and how the queries are evaluated, thereby enlarging the capacity of expressiveness for any commercial database management system. In order to minimize the selection time, a set of strategies have been proposed, which combine the advantages of relational algebra and constraint data representation. (C) 2014 Elsevier Ltd. All rights reserved.
In this paper two strategies are presented to reduce the combinatorial complexity when solving single stage and multistage optimization scheduling problems that involve cost minimization and due dates. These problems ...
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In this paper two strategies are presented to reduce the combinatorial complexity when solving single stage and multistage optimization scheduling problems that involve cost minimization and due dates. These problems can naturally be decomposed into assignment and sequencing subproblems. The proposed strategies rely on either combining mixed-integer programming (MILP) to model the assignment part and constraint programming (CP) for modeling the sequencing part, or else combining MILP models for both parts. The subproblems are solved sequentially by adding integer cuts to the first MILP to generate new assignments. Results are presented for both single and multistage systems. (C) 2002 Elsevier Science Ltd. All rights reserved.
This paper presents the optimal batch scheduling of a multi-product dairy process using an approach that combines optimization and constraint programming techniques. A suitable model describing the subprocesses and pr...
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This paper presents the optimal batch scheduling of a multi-product dairy process using an approach that combines optimization and constraint programming techniques. A suitable model describing the subprocesses and production rules is developed allowing to obtain scheduling constraints relating the production process and the machines available together with their relative efficiencies. After the scheduling problem has been formulated, the batch scheduling of a real powder milk/yogurt process is obtained in an optimal manner using the proposed approach with the objective of meeting customers' deadlines considering the efficiencies/costs of available alternative machines. Results using real consumer orders on some representative scenarios corresponding to the dairy production plant used as a case study are provided. This application shows a formulation closer to the engineering problem description thanks to the constraint-based language that facilitates the adaptation of the optimization objectives and constraints to real applications. (C) 2018 Elsevier Ltd. All rights reserved.
This paper discusses heterogeneous Network-on-Chip (NoC) design from a constraint programming (CP) perspective and extends the formulation to solving Voltage-Frequency Island (VFI) problem. In general, VFI is a superi...
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This paper discusses heterogeneous Network-on-Chip (NoC) design from a constraint programming (CP) perspective and extends the formulation to solving Voltage-Frequency Island (VFI) problem. In general, VFI is a superior design alternative in terms of thermal constraints, power consumption as well as performance considerations. Given a Communication Task Graph (CTG) and subsequent task assignments for cores, cores are allocated to the best possible places on the chip in the first stage to minimize the overall communication cost among cores. We then solve the application scheduling problem to determine the optimum core types from a list of technological alternatives and to minimize the make-span. Moreover, an elegant CP model is proposed to solve VFI problem by mapping and grouping cores at the same time with scheduling the computation tasks as a limited capacity resource allocation model. The paper reports results based on real benchmark datasets from the literature. (C) 2014 Elsevier Ltd. All rights reserved.
Sudoku is not only a popular puzzle but also an interesting and challenging constraint satisfaction problem. Therefore, automatic solving methods have been the subject of several publications in the past two decades. ...
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Sudoku is not only a popular puzzle but also an interesting and challenging constraint satisfaction problem. Therefore, automatic solving methods have been the subject of several publications in the past two decades. Although current methods provide good solutions for small-sized puzzles, larger instances remain challenging. This article introduces a new local search technique based on the min-conflicts heuristic for Sudoku. Furthermore, the authors propose an innovative hybrid search technique that exploits constraint programming as a perturbation technique within the iterated local search framework. They experimentally evaluate their methods on challenging benchmarks for Sudoku and report improvements over state-of-the-art solutions. To show the generalizability of the proposed approach, they also applied their method on another challenging scheduling problem. The results show that the proposed method is also robust in another problem domain.
This paper is aimed at researchers and practitioners in Operational Research who are interested in the new field of constraint programming/constraint Logic programming. Due to differing terminology and problem represe...
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This paper is aimed at researchers and practitioners in Operational Research who are interested in the new field of constraint programming/constraint Logic programming. Due to differing terminology and problem representation they might have found it difficult to access the field. The paper focuses on discrete optimisation problems. The first part lists frequently used terms in constraint programming (CP), contrasting them with their counterparts in Mathematical programming (MP). The second part explains some of the most important concepts and techniques in more detail by comparing the CP and MP implementations of a small example problem, the 'Change Problem'. It includes an overview of the respective results. In conclusion a more generalised comparison of CP and RIP techniques is given.
Wind farms are frequently located in proximity to human dwellings, natural habitats, and infrastructure making land use constraints and noise matters of increasing concern for all stakeholders. In this study, we perfo...
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Wind farms are frequently located in proximity to human dwellings, natural habitats, and infrastructure making land use constraints and noise matters of increasing concern for all stakeholders. In this study, we perform a constrained multi-objective wind farm layout optimization considering energy and noise as objective functions, and considering land use constraints arising from landowner participation, environmental setbacks and proximity to existing infrastructure. A multi-objective, continuous variable Genetic Algorithm (NSGA-II) is combined with a novel constraint handling approach to solve the optimization problem. This constraint handling approach uses a combination of penalty functions and constraint programming to balance local and global exploration to find feasible solutions. The proposed approach is used to solve the wind farm layout optimization problem with different numbers of turbines and under different levels of land availability (constraint severity). Our results show increasing land availability and/or number of turbines, increases energy generation, noise production, and computational cost. Results also illustrate the potential of the proposed constraint handling approach to outperform existing methods in the context of evolutionary optimization, yielding better solutions at a lower computational cost. (C) 2018 Elsevier Ltd. All rights reserved.
The Nurse Rostering Problem can be defined as assigning a series of shift sequences (schedules) to several nurses over a planning horizon according to some limitations and preferences. The inherent benefits of generat...
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The Nurse Rostering Problem can be defined as assigning a series of shift sequences (schedules) to several nurses over a planning horizon according to some limitations and preferences. The inherent benefits of generating higher-quality schedules are a reduction in outsourcing costs and an increase in job satisfaction of employees. In this paper, we present a hybrid algorithm, which combines Integer programming and constraint programming to efficiently solve the highly-constrained Nurse Rostering Problem. We exploit the strength of IP in obtaining lower-bounds and finding an optimal solution with the capability of CP in finding feasible solutions in a co-operative manner. To improve the performance of the algorithm, and therefore, to obtain high-quality solutions as well as strong lower-bounds for a relatively short time, we apply some innovative ways to extract useful information such as the computational difficulty of instances and constraints to adaptively set the search parameters. We test our algorithm using two different datasets consisting of various problem instances, and report competitive results benchmarked with the state-of-the-art algorithms from the recent literature as well as standard IP and CP solvers, showing that the proposed algorithm is able to solve a wide variety of instances effectively. (C) 2017 Elsevier Ltd. All rights reserved.
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