A hybrid neural network- meta heuristics approach for permutation flow shop scheduling problems
23 Dec 2010-pp 286-290
TL;DR: The objective of this study is to find a sequence of jobs for the permutation flow shop to minimize makespan and the sequence obtained using neural network is used to generate initial population for genetic algorithm (ANN-GA), genetic algorithm using Random Insertion Perturbation Scheme and Simulated Annealing.
Abstract: The objective of this study is to find a sequence of jobs for the permutation flow shop to minimize makespan. A feed forward back propagation neural network is used to solve the 10 machine problem taken from the literature. The network is trained with the optimal sequences for five, six and seven jobs problem. This trained network is then used to solve the problem with greater number of jobs. The sequence obtained using neural network is used to generate initial population for genetic algorithm (ANN-GA), genetic algorithm using Random Insertion Perturbation Scheme (ANN-GA-RIPS) and Simulated Annealing (ANN-SA). Makespans obtained through these approaches are compared with the Taillard's benchmark problems.
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TL;DR: In this article, a fully verifiable and deployable framework for optimizing schedules in a batch-based production system is proposed, which is designed to control and optimize the flow of batches of material into a network of identical and non-identical parallel and series machines that produce a high variation of complex hard metal products.
Abstract: A fully verifiable and deployable framework for optimizing schedules in a batch-based production system is proposed. The scheduler is designed to control and optimize the flow of batches of material into a network of identical and non-identical parallel and series machines that produce a high variation of complex hard metal products. The proposed multi-objective batch-based flowshop scheduling optimization (MOBS-NET) deploys a fully connected deep neural network (FCDNN) with respect to three performance criteria of energy, cost and makespan. The problem is NP-hard and considers minimizing the energy consumed per unit of product, operations cost, and the makespan. The output of the method has been validated and verified as optimal operational planning and scheduling meeting the business operational objectives. Real-time and look ahead discrete event simulation of the production process provides the feedback and assurance of the robustness and practicality of the optimum schedules prior to implementation.
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References
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TL;DR: The methodology and its implementation is described for several of the more common sequencing objectives, as well as for a hypothetical objective that minimizes a cost function exhibiting a limited exponential behavior.
44 citations
"A hybrid neural network- meta heuri..." refers background in this paper
...[7], Akyol [8], Lee and Shaw [3], Haq and Ramanan [9] and Haq et al....
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TL;DR: The experimental results support the efficacy of using the neural-net approach for real time flow-shop sequencing and show that the neural network can be used to develop hybrid genetic algorithms.
43 citations
"A hybrid neural network- meta heuri..." refers background in this paper
...[7], Akyol [8], Lee and Shaw [3], Haq and Ramanan [9] and Haq et al....
[...]
...In view of the combinatorial complexity and time constraints, most of the large problems can be solved only by heuristic methods [3]....
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...El-bouri et al. [5] Sabuncuoglu and Gurgun [6], El-Bouri et al. [7], Akyol [8], Lee and Shaw [3], Haq and Ramanan [9] and Haq et al. [10]....
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TL;DR: It was found that the ANN-GA-RIPS approach performs better thanANN-GA starting with a random population and the results are found to be within 5% of the upper bounds of Taillard's benchmark problems.
Abstract: The objective of this paper is to find a sequence of jobs for the permutation flow shop to minimise the makespan. The shop consists of 10 machines. A feed-forward back-propagation artificial neural network (ANN) is used to solve the problem. The network is trained with the optimal sequences for five-, six- and seven-job problems. This trained network is then used to solve a problem with a greater number of jobs. The sequence obtained using the neural network is used to generate the initial population for the genetic algorithm (GA) using the random insertion perturbation scheme (RIPS). The makespan of the sequence obtained by this approach (ANN-GA-RIPS) is compared with that obtained using GA starting with a random population (ANN-GA). It was found that the ANN-GA-RIPS approach performs better than ANN-GA starting with a random population. The results obtained are compared with those obtained using the Nawaz, Enscore and Ham (NEH) heuristic and upper bounds of Taillard's benchmark problems. ANN-GA-RIPS per...
35 citations
TL;DR: An implementation alternative to the existing heuristic algorithms is provided and can be extended to solve the scheduling problems in the area of manufacturing.
32 citations
"A hybrid neural network- meta heuri..." refers background in this paper
...[7], Akyol [8], Lee and Shaw [3], Haq and Ramanan [9] and Haq et al....
[...]
...El-bouri et al. [5] Sabuncuoglu and Gurgun [6], El-Bouri et al. [7], Akyol [8], Lee and Shaw [3], Haq and Ramanan [9] and Haq et al. [10]....
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TL;DR: Initial sequences that are structured to enhance the performance of local search techniques are constructed from job rankings delivered by a trained neural network, showing that the sequences suggested by the neural networks are more effective in directing neighborhood search methods to lower local optima.
19 citations
"A hybrid neural network- meta heuri..." refers background in this paper
...[5] Sabuncuoglu and Gurgun [6], El-Bouri et al....
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...[5] show that neural sequences exhibit the potential to lead neighborhood search methods to lower local optima....
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