An improved constraint satisfaction adaptive neural network for job-shop scheduling

Copyright @ Springer Science + Business Media, LLC 2009This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and in part by the National Nature Science Fundation of China under Grant 60821063 and National Basic Research Program of China under Grant 2009CB320601

Merging gradual neural networks and Genetic algorithm for Dynamic Channel Assignment Problem

Under this article, we offer a novel neural-network approach called gradual neural network (GNN) hybridized with a genetic algorithm for a class of combinatorial optimization problems of requiring the constraint satisfaction and the goal function optimization simultaneously. The hard problem of frequency assignment problem in the mobile communication system is efficiently solved by GNN as the typical problem of this class.The goal of this problem is to minimize the electromagnetic compatibility constraints between transceivers by first, rearranging the frequency assignment so that they can accommodate the increasing demands and second, using a minimum number of frequencies. An optimal solution is sought to facilitate the subsequent addition of new links. The binary neural network achieves the constraint satisfaction with the help of genetic algorithm, in order to seek the cost optimization and the network topology. The capability of the GNN algorithm is demonstrated through solving real instances in practical problem, showing that it can find far equivalent solutions than the existing algorithms, has good performance, and suggests a new interesting direction for research

Finding robust solutions for constraint satisfaction problems with discrete and ordered domains by coverings

Constraint programming is a paradigm wherein relations between variables are stated in the form of constraints. Many real life problems come from uncertain and dynamic environments, where the initial constraints and domains may change during its execution. Thus, the solution found for the problem may become invalid. The search forrobustsolutions for constraint satisfaction problems (CSPs) has become an important issue in the ¿eld of constraint programming. In some cases, there exists knowledge about the uncertain and dynamic environment. In other cases, this information is unknown or hard to obtain. In this paper, we consider CSPs with discrete and ordered domains where changes only involve restrictions or expansions of domains or constraints. To this end, we model CSPs as weighted CSPs (WCSPs) by assigning weights to each valid tuple of the problem constraints and domains. The weight of each valid tuple is based on its distance from the borders of the space of valid tuples in the corresponding constraint/domain. This distance is estimated by a new concept introduced in this paper: coverings. Thus, the best solution for the modeled WCSP can be considered as a most robust solution for the original CSP according to these assumptionsThis work has been partially supported by the research projects TIN2010-20976-C02-01 (Min. de Ciencia e Innovacion, Spain) and P19/08 (Min. de Fomento, Spain-FEDER), and the fellowship program FPU.Climent Aunés, LI.; Wallace, RJ.; Salido Gregorio, MA.; Barber Sanchís, F. (2013). Finding robust solutions for constraint satisfaction problems with discrete and ordered domains by coverings. Artificial Intelligence Review. 1-26. https://doi.org/10.1007/s10462-013-9420-0S126Climent L, Salido M, Barber F (2011) Reformulating dynamic linear constraint satisfaction problems as weighted csps for searching robust solutions. 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In: Proceedings of the international conference on principles and practice of constraint programming (CP-2000), pp 500–504Goles E, Martínez S (1990) Neural and automata networks: dynamical behavior and applications. Kluwer Academic Publishers, DordrechtHays W (1973) Statistics for the social sciences, vol 410, 2nd edn. Holt, Rinehart and Winston, New YorkHebrard E (2006) Robust solutions for constraint satisfaction and optimisation under uncertainty. PhD thesis, University of New South WalesHerrmann H, Schneider C, Moreira A, Andrade Jr J, Havlin S (2011) Onion-like network topology enhances robustness against malicious attacks. J Stat Mech Theory Exp 2011(1):P01,027Larrosa J, Schiex T (2004) Solving weighted CSP by maintaining arc consistency. Artif Intell 159:1–26Larrosa J, Meseguer P, Schiex T (1999) Maintaining reversible DAC for Max-CSP. J Artif Intell 107(1):149–163Mackworth A (1977) On reading sketch maps. 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Constraint satisfaction adaptive neural network and heuristics combined approaches for generalized job-shop scheduling

Copyright @ 2000 IEEEThis paper presents a constraint satisfaction adaptive neural network, together with several heuristics, to solve the generalized job-shop scheduling problem, one of NP-complete constraint satisfaction problems. The proposed neural network can be easily constructed and can adaptively adjust its weights of connections and biases of units based on the sequence and resource constraints of the job-shop scheduling problem during its processing. Several heuristics that can be combined with the neural network are also presented. In the combined approaches, the neural network is used to obtain feasible solutions, the heuristic algorithms are used to improve the performance of the neural network and the quality of the obtained solutions. Simulations have shown that the proposed neural network and its combined approaches are efficient with respect to the quality of solutions and the solving speed.This work was supported by the Chinese National Natural Science Foundation under Grant 69684005 and the Chinese National High-Tech Program under Grant 863-511-9609-003, the EPSRC under Grant GR/L81468

A new adaptive neural network and heuristics hybrid approach for job-shop scheduling

Copyright @ 2001 Elsevier Science LtdA new adaptive neural network and heuristics hybrid approach for job-shop scheduling is presented. The neural network has the property of adapting its connection weights and biases of neural units while solving the feasible solution. Two heuristics are presented, which can be combined with the neural network. One heuristic is used to accelerate the solving process of the neural network and guarantee its convergence, the other heuristic is used to obtain non-delay schedules from the feasible solutions gained by the neural network. Computer simulations have shown that the proposed hybrid approach is of high speed and efficiency. The strategy for solving practical job-shop scheduling problems is provided.This work is supported by the National Nature Science Foundation (No. 69684005) and National High -Tech Program of P. R. China (No. 863-511-9609-003)

Diversity Communication in Teams: Improving Problem Solving or Creating Confusion?

Despite the rich and interdisciplinary debate on the role of diversity and communication in group problem solving, as well as the recognition of the interactions between the two topics, they have been rarely treated as a joint research issue. In this paper we develop a computational approach aimed at modeling problem solving agents and we assess the impact of various levels of diversity and communication in teams on agents' performance at solving problems. By communication we intend a conversation on the persuasiveness of the features characterizing the problem setting. By diversity we mean differences in how agents build problem representations that allow them to access various solutions. We deploy the concept of diversity along two dimensions: knowledge amplitude, that is, the amount of available knowledge (compared to the complete representation of a problem), and knowledge variety, which pertains to the differences in agents' knowledge endowments.x10Our results show the different impact of these two sources of variety on problem solving performance in teams, as well as their interplay. Regarding knowledge amplitude, when agents' representation of the problem is considerably incomplete, communication provides confusion as it is difficult to find a common language for sharing thoughts, and agents perform better alone. Adding knowledge variety to this scenario, the effects of communication are even more negative. Conversely, as the representation of the problem gets more and more complete, communication becomes more and more effective. Albeit displaying a clear non-monotonic effect: increasing the communication strength, performance increases until an optimal point, after which it declines and gets very rapidly worse than individual behavior. In this case, the introduction of knowledge variety further increases performance in teams, since benefits from integrating partial representations of the problem occur more frequently than communication clashes. Finally, highly diverse teams seem to be less sensitive to changes in communication strength, while as diversity declines, even small discrepancies from the optimal communication strength level might account for a strong variability of performance. In particular, overestimation of the required communication effort might cause severe performance breakdowns.x10Our results suggest that organizations and firms should jointly consider communication intensity and different sources of diversity in teams, since interactions among these variables might result in problem solving groups resembling more a Tower of Babel than an effective and helpful workplaceproblem solving; diversity; heterogeneous agents; communication; constraint; satisfaction; neural networks; causality

Solving constraint-satisfaction problems with distributed neocortical-like neuronal networks

Finding actions that satisfy the constraints imposed by both external inputs and internal representations is central to decision making. We demonstrate that some important classes of constraint satisfaction problems (CSPs) can be solved by networks composed of homogeneous cooperative-competitive modules that have connectivity similar to motifs observed in the superficial layers of neocortex. The winner-take-all modules are sparsely coupled by programming neurons that embed the constraints onto the otherwise homogeneous modular computational substrate. We show rules that embed any instance of the CSPs planar four-color graph coloring, maximum independent set, and Sudoku on this substrate, and provide mathematical proofs that guarantee these graph coloring problems will convergence to a solution. The network is composed of non-saturating linear threshold neurons. Their lack of right saturation allows the overall network to explore the problem space driven through the unstable dynamics generated by recurrent excitation. The direction of exploration is steered by the constraint neurons. While many problems can be solved using only linear inhibitory constraints, network performance on hard problems benefits significantly when these negative constraints are implemented by non-linear multiplicative inhibition. Overall, our results demonstrate the importance of instability rather than stability in network computation, and also offer insight into the computational role of dual inhibitory mechanisms in neural circuits.Comment: Accepted manuscript, in press, Neural Computation (2018

An event-based architecture for solving constraint satisfaction problems

Constraint satisfaction problems (CSPs) are typically solved using conventional von Neumann computing architectures. However, these architectures do not reflect the distributed nature of many of these problems and are thus ill-suited to solving them. In this paper we present a hybrid analog/digital hardware architecture specifically designed to solve such problems. We cast CSPs as networks of stereotyped multi-stable oscillatory elements that communicate using digital pulses, or events. The oscillatory elements are implemented using analog non-stochastic circuits. The non-repeating phase relations among the oscillatory elements drive the exploration of the solution space. We show that this hardware architecture can yield state-of-the-art performance on a number of CSPs under reasonable assumptions on the implementation. We present measurements from a prototype electronic chip to demonstrate that a physical implementation of the proposed architecture is robust to practical non-idealities and to validate the theory proposed.Comment: First two authors contributed equally to this wor