Name
Title | ||
|---|---|---|
GRASP for sequencing mixed models in an assembly line with work overload, useless time and production regularity. |
Abstract | ||
|---|---|---|
A GRASP algorithm is presented for solving a sequencing problem in a mixed-model assembly line. The problem is focused on obtaining a manufacturing sequence that completes the greatest possible amount of required work and fulfils the production regularity property. The implemented GRASP algorithm is compared with other resolution procedures by means of instances from a case study linked to the Nissan’s engine plant in Barcelona. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s13748-015-0071-z | Progress in AI |
Keywords | Field | DocType |
GRASP, Sequencing, Mixed-model assembly line, Production mix preservation | GRASP,Computer science,Mixed model,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
5 | 1 | 2192-6360 |
Citations | PageRank | References |
2 | 0.40 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joaquín Bautista | 1 | 345 | 27.50 |
| Rocıo Alfaro | 2 | 36 | 6.68 |
| Cristina Batalla | 3 | 25 | 3.84 |