International Journal of Mechanical and Production Engineering (IJMPE)
Follow Us On :
current issues
Volume-10,Issue-11  ( Nov, 2022 )
Past issues
  1. Volume-10,Issue-10  ( Oct, 2022 )
  2. Volume-10,Issue-9  ( Sep, 2022 )
  3. Volume-10,Issue-8  ( Aug, 2022 )
  4. Volume-10,Issue-7  ( Jul, 2022 )
  5. Volume-10,Issue-6  ( Jun, 2022 )
  6. Volume-10,Issue-5  ( May, 2022 )
  7. Volume-10,Issue-4  ( Apr, 2022 )
  8. Volume-10,Issue-3  ( Mar, 2022 )
  9. Volume-10,Issue-2  ( Feb, 2022 )
  10. Volume-10,Issue-1  ( Jan, 2022 )

Statistics report
Feb. 2023
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 116
Paper Published : 2252
No. of Authors : 6414
  Journal Paper

Paper Title :
Machine Learning in Hybrid Flow Shop Scheduling with Unrelated Machines

Author :Miriam Zacharias, Annika Tonnius, Johannes Gottschling

Article Citation :Miriam Zacharias ,Annika Tonnius ,Johannes Gottschling , (2019 ) " Machine Learning in Hybrid Flow Shop Scheduling with Unrelated Machines " , International Journal of Mechanical and Production Engineering (IJMPE) , pp. 50-56, Volume-7,Issue-6

Abstract : Hybrid flow shop (HFS) problems are often encountered in real world production systems. Despite their practical relevance, very few generic methods exist to solve HFS problems. Instead, many approaches either focus on two-stage problems or the application of simple dispatching rules. Moreover, the majority of authors assume identical parallel machines, which reduces the complexity of machine assignment to a large extent. If a real world case is studied, solution methods are often customized and not adaptable to other settings. Most common decomposition approaches are critical pathrelated or only focus on possible permutations of job indices while utilizing simple machine assignment rules. [1] Lately, machine learning (ML) has been applied to different scheduling problems. A common application is the selection of best dispatching rules based on the state of systems parameters. We propose an alternative ML-based approach to makespan or flow time minimization that is suitable for different configurations regarding the number of stages and number of unrelated machines per stage. To speed up the scheduling process, we apply ML in one step of our solution method: We train Neural Networks (NN) and Support Vector Machines (SVM) with optimal machine assignments and makespan or flowtime values for fixed batch sizes and randomly generated processing time matrices. Afterwards, we use the trained NN and SVM to predict optimal makespan or flowtime values and machine assignments for all partial job sequences (batches) based on a given processing time matrix. Only sequences with close-to-optimal makespan values are evaluated further to determine a final machine assignment and overall sequence. Keywords - Hybrid Flow Shop, Unrelated Machines, Deterministic Scheduling, Divide Et Impera, Machine Learning.

Type : Research paper

Published : Volume-7,Issue-6


Copyright: © Institute of Research and Journals

| PDF |
Viewed - 62
| Published on 2019-08-20
IRAJ Other Journals
IJMPE updates
Volume-10,Issue-11 (Nov, 2022 )
The Conference World