# 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.

Topics: Job shop scheduling (63%), Flow shop scheduling (63%), Hybrid neural network (57%), Feedforward neural network (55%), Simulated annealing (54%)

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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.

##### References

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RAND Corporation

^{1}TL;DR: A simple decision rule is obtained in this paper for the optimal scheduling of the production so that the total elapsed time is a minimum.

Abstract: Each of a collection of items are to be produced on two machines (or stages). Each machine can handle only one item at a time and each item must be processed through machine one and then through machine two. The setup time plus work time for each item for each machine is known. A simple decision rule is obtained in this paper for the optimal scheduling of the production so that the total elapsed time is a minimum. A three-machine problem is also discussed and solved for a restricted case.

2,932 citations

### "A hybrid neural network- meta heuri..." refers background in this paper

...Johnson’s algorithm [4] is the earliest known optimal solution algorithm for n jobs two machines flow shop scheduling problems....

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TL;DR: The results are strong in that they hold whether the problem size is measured by number of tasks, number of bits required to express the task lengths, or by the sum of thetask lengths.

Abstract: NP-complete problems form an extensive equivalence class of combinatorial problems for which no nonenumerative algorithms are known. Our first result shows that determining a shortest-length schedule in an m-machine flowshop is NP-complete for m ≥ 3. For m = 2, there is an efficient algorithm for finding such schedules. The second result shows that determining a minimum mean-flow-time schedule in an m-machine flowshop is NP-complete for every m ≥ 2. Finally we show that the shortest-length schedule problem for an m-machine jobshop is NP-complete for every m ≥ 2. Our results are strong in that they hold whether the problem size is measured by number of tasks, number of bits required to express the task lengths, or by the sum of the task lengths.

2,158 citations

### "A hybrid neural network- meta heuri..." refers background in this paper

...He stated that application of GAs for solving NP-hard problems have revealed their efficiency to obtain good solutions in relatively shortest times....

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...This problem is found to be NP-hard [2]....

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TL;DR: This paper proposes 260 randomly generated scheduling problems whose size is greater than that of the rare examples published, and the objective is the minimization of the makespan.

Abstract: In this paper, we propose 260 randomly generated scheduling problems whose size is greater than that of the rare examples published. Such sizes correspond to real dimensions of industrial problems. The types of problems that we propose are: the permutation flow shop, the job shop and the open shop scheduling problems. We restrict ourselves to basic problems: the processing times are fixed, there are neither set-up times nor due dates nor release dates, etc. Then, the objective is the minimization of the makespan.

1,945 citations

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### "A hybrid neural network- meta heuri..." refers background in this paper

...Problems are taken from benchmark problems given in literature [19]....

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24 Mar 1982-

Abstract: (1982). Sequencing and Scheduling: An Introduction to the Mathematics of the Job-Shop. Journal of the Operational Research Society: Vol. 33, No. 9, pp. 862-862.

1,012 citations

### "A hybrid neural network- meta heuri..." refers background in this paper

..., J1 must be processed on machine Mk before Mi then the same is true for all jobs [1]....

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TL;DR: A Genetic Algorithm is developed for finding (approximately) the minimum makespan of the n-job, m-machine permutation flowshop sequencing problem and the performance of the algorithm is compared with that of a naive Neighbourhood Search technique and with a proven Simulated Annealing algorithm.

Abstract: The basic concepts of Genetic Algorithms are described, following which a Genetic Algorithm is developed for finding (approximately) the minimum makespan of the n-job, m-machine permutation flowshop sequencing problem. The performance of the algorithm is then compared with that of a naive Neighbourhood Search technique and with a proven Simulated Annealing algorithm on some carefully designed sets of instances of this problem.

813 citations