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: In this article, simulated annealing is proposed as a heuristic to obtain approximate solutions to the problem of scheduling jobs in a flow shop, where job processing order must be the same on each machine and the objective is to minimize the maximum completion time.
Abstract: The problem of scheduling jobs in a flow-shop is considered. The job processing order must be the same on each machine and the objective is to minimize the maximum completion time. Simulated annealing is proposed as a heuristic to obtain approximate solutions. Extensive computational tests with problems having up to 20 machines and 100 jobs show simulated annealing to compare favourably with known constructive heuristics and with descent methods.
589 citations
"A hybrid neural network- meta heuri..." refers methods in this paper
...Osman and Potts [11], proposed a simple Simulated Annealing algorithm using a shift neighbourhood and a random neighbourhood search....
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TL;DR: In this paper, a multi-objective genetic algorithm was proposed for flow shop scheduling with a concave Pareto front and the performance of the algorithm was examined by applying it to the flowshop scheduling problem with two objectives: minimizing the makespan and minimizing the total tardiness.
502 citations
"A hybrid neural network- meta heuri..." refers background in this paper
...Murata and Tanaka [ 16 ] have proposed a multi objective random weight based genetic algorithm for flow shop scheduling....
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TL;DR: It is found that the proposed simulated annealing algorithm provides better solutions than repeated iterative improvement algorithm, for a fixed total computational time.
305 citations
TL;DR: The conclusions show that the GA based heuristic can always give the best results in a short time on a SUN workstation.
241 citations
"A hybrid neural network- meta heuri..." refers methods in this paper
...[17] developed a simple genetic algorithm for the PFSP with various...
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TL;DR: By computer simulations on randomly generated test problems, it is shown that the performance of the proposed algorithms is less sensitive to the choice of a cooling schedule than that of the standard simulated annealing algorithm.
170 citations
Additional excerpts
...[13] presented two Simulated Annealing algorithms characterized by having robust performance with respect to the temperature cooling schedule....
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