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: A new neural network approach is proposed to solve the single machine mean tardiness scheduling problem and the minimum makespan job shop scheduling problem that combines the characteristics of neural networks and algorithmic approaches.
96 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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TL;DR: A new hybrid particle swarm optimization model named HPSO that combines random-key (RK) encoding scheme, individual enhancement (IE) scheme, and particle Swarm optimization (PSO) is presented and used to solve the flow-shop scheduling problem (FSSP).
Abstract: In this paper, a new hybrid particle swarm optimization model named HPSO that combines random-key (RK) encoding scheme, individual enhancement (IE) scheme, and particle swarm optimization (PSO) is presented and used to solve the flow-shop scheduling problem (FSSP). The objective of FSSP is to find an appropriate sequence of jobs in order to minimize makespan. Makespan means the maximum completion time of a sequence of jobs running on the same machines in flow-shops. By the RK encoding scheme, we can exploit the global search ability of PSO thoroughly. By the IE scheme, we can enhance the local search ability of particles. The experimental results show that the solution quality of FSSP based on the proposed HPSO is far better than those based on GA [Lian, Z., Gu, X., & Jiao, B. (2008). A novel particle swarm optimization algorithm for permutation flow-shop scheduling to minimize makespan. Chaos, Solitons and Fractals, 35, 851-861.] and NPSO [Lian, Z., Gu, X., & Jiao, B. (2008). A novel particle swarm optimization algorithm for permutation flow-shop scheduling to minimize makespan. Chaos, Solitons and Fractals, 35, 851-861.], respectively.
95 citations
"A hybrid neural network- meta heuri..." refers methods in this paper
...The upper bounds of the solution (of the Taillards benchmark) are commonly taken for comparison in the literature.[21] It is found that the makespan values obtained using ANN-GARIPS approach are within 5% from the Upper Bounds for the respective problems....
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TL;DR: A heuristic algorithm that makes use of simulated annealing (SA) technique and a perturbation scheme in the SA algorithm for scheduling to minimize the maximum weighted tardiness of a job and total weighted tardsiness of jobs are presented.
53 citations
TL;DR: From the computational results, it is shown that solutions obtained by the proposed method are superior to those of traditional heuristics and that this method can derive optimum or near-optimum solutions at considerably less computational time than that of the other simulated annealing scheme.
51 citations
"A hybrid neural network- meta heuri..." refers methods in this paper
...[14] demonstrated a hybrid technique by introducing problem domain knowledge into a Simulated Annealing algorithm....
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26 Jun 2007
TL;DR: A new hybrid particle swarm optimization model named HPSO that combines random-key (RK) encoding scheme, individual enhancement (IE) scheme, and particle Swarm optimization (PSO) is presented and used to solve the flow-shop scheduling problem (FSSP).
Abstract: In this paper, a new hybrid particle swarm optimization model named HPSO that combines random-key (RK) encoding scheme, individual enhancement (IE) scheme, and particle swarm optimization (PSO) is presented and used to solve the flow-shop scheduling problem (FSSP). The objective of FSSP is to find an appropriate sequence of jobs in order to minimize makespan. Makespan means the maximum completion time of a sequence of jobs running on the same machines in flow-shops. By the RK encoding scheme, we can exploit the global search ability of PSO thoroughly. By the IE scheme, we can enhance the local search ability of particles. The experimental results show that the solution quality of FSSP based on the proposed HPSO is far better than those based on GA [1] and NPSO [1], respectively.
47 citations