Solving job-shop scheduling problem with priority using neural network

Zhang, Changshui; Yan, Pingfan; Chang, Taihua

doi:10.1109/ijcnn.1991.170586

Cited by 15 publications

(2 citation statements)

References 2 publications

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“…The BP network prior to the calculation method for the principle, output input particles calculated value and error, and so on, to determine the errors of all samples. When the algorithm is the maximum number of iterations is less than the standard, when the conditions are met to (7), or (4).…”

Section: Pso-bp Modelmentioning

confidence: 99%

Combined Forecasting Model Based on BP Improved by Particle Swarm Optimization and its Application

Liu

2014

AMM

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The BP neural network as the traditional prediction method has certain advantages, but it has some drawbacks, Such as slow convergence and sensitive to the initial weights, etc. The PSO algorithm is introduced into the neural network training, using the particle swarm algorithm to optimize the neural network weights and threshold. Through the establishment of the particle swarm - BP neural network model for power load budget, it improves the accuracy and stability of the forecast.

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Section: Pso-bp Modelmentioning

confidence: 99%

Combined Forecasting Model Based on BP Improved by Particle Swarm Optimization and its Application

Liu

2014

AMM

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“…The quest to find an efficient solution for N + 1 QP is very valuable as it is analogous to many scheduling problems. Some of the scheduling problems have been proposed in [6,9,11,12], where the resources were optimized.…”

Section: Introductionmentioning

confidence: 99%

OPTIMIZATION OF <em>N</em> + 1 QUEENS PROBLEM USING DISCRETE NEURAL NETWORK

Waqas¹,

Bhatti²

2017

NNW

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Combinatorial optimization problems are extensively solved by using neural networks. Hopfield-Tank model is used to solve Traveling Salesman problem and many NP-Hard problems. This paper describes a neural network optimizer/scheduler that optimizes a solution for a highly complicated version of N Queens problem (NQP), i.e. N + 1 non-threatening Queens on a N × N chessboard with an intermediate pawn on it. Both synchronous and asynchronous methods of updating of the neurons have been applied for optimization of N + 1 Queens problem. Computer simulations are used to confirm the results. The proposed neural network is attracted to optimized solution or finds the global minima in 90% of the trials. A new rule of initialization, i.e. the proximity rule of initialization has been proposed. Using the proximity rule of initialization the performance of the system is enhanced and the system converges to an optimal solution in much less time. Many novel applications like multiprocessor job scheduling, resource optimization, of the above mentioned algorithm have been proposed. N Queens problem has been solved by many techniques but no other algorithm exists to solve N + 1 QP in the literature. Consequently, the performance of the network is compared with full space search algorithm.

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