1. The use of Neural Networks to schedule flow-shop with dynamic job arrival
‘A Multi-Neural Network Learning for lot Sizing and Sequencing on a Flow-Shop’

2. Flow-shop Scheduling
Research are focused on flow-shop sequencing without lot sizing.
So the solutions obtained may be sub-optimal.

3. Objective of the research:
Determine both sequence and lot-sizes simultaneously
It uses a multi-Neural Networks
The samples to train the Neural Networks are generated by using a genetic algorithm.

4. Neural Networks
Models that simulate the way in which the brain perform a particular task.
Learn through examples as how human does.
The process of learning is called learning algorithm.

5. Neural Networks It resembles the brain in two aspects:
Knowledge is acquired by the network through a learning process.
Inter-neuron connection strengths known as synaptic weights are used to store.

6. Neural Networks The modification of the synaptic weights:
is to perform the desired task.
is to modify its own topology, which is motivated by the fact that neurons in the brain can die and new synaptic connections can grow.

7. There are different network architectures
Single Layered.
Multi-Layered.
Recurrent (with feedback).
Lattice Structures (the dimension refers to the number of the space in which the graph lies).

8. Applications of Neural Networks
Data Classification
Economics
Optimization Problems
Scheduling
Etc.

9. Scheduling The flow shop problem consist of two main elements:
A group of m machines.
A set of n jobs to be processed on this group of machines.

10. Scheduling
Typically, the problem of scheduling and controlling jobs is a flow line is divided into the problems of:
Lot sizing.
Sequencing.

11. Lot sizing problem
Given the demand for an item each time period, the lot sizing problem is to determine the order and inventory quantity in each time period.

12. Sequencing Problem
It consist in find the sequence of jobs that minimizes the tardiness.
It is in NP-complete.
Heuristics are the primary way to tackle this problem.

13. Heuristics for sequencing jobs
Local search – hill climbing and steepest decent methods.
Guided search – Tabu search and Genetic algorithms (model of machine learning that derives its behavior from the metaphor of the process of evolution in nature).

14. Sequencing Problem Few algorithms to solve large problems.
For small problems: enumeration and integer programming.
Where the number of jobs and machines are large, there is usually a tradeoff between the solution quantity and the computational time.

15. Multi-neural architecture

16. Lot-Sizing Neural Network
Four job parameters on each machine were used as inputs to the NN:
Average processing time.
Standard derivation of processing time.
Average setup time.
Standard derivation of the setup time.

17. Lot-Sizing Neural Network

18. Sequencing Neural Network
For the training, each element of the input set is composed of jobs to be processed.
It utilizes the concept of an associative memory.
Output: the desired job sequencing.
Input: sequencing of samples.

19. Sequencing Neural Network
A neural weight matrix is introduced to make real-time sequencing decisions.
It consists of 2 layers with and equivalent number of processing elements.

20. Sequencing Neural Network

21. Information used to the experiment
For data generation: max of 50 jobs were considered.
Buffer size: 20
Processing time and setup times for jobs: randomly in [0.01,1]
Initial lot sizes: randomly in [0.01,1]
Sets generated: 9

22. Results

23. Conclusions Multi-neural networks give a better solution quality.
The user interfaces combines both sequencing and lot-sizing to make a final scheduling on real time.
The solution is consistent across all problem sets