Abstract
Due-date assignment plays an important role in scheduling systems and strongly influences the delivery performance of job shops. Because of the stochastic and dynamic nature of job shops, the development of general due-date assignment models (DDAMs) is complicated. In this study, two genetic programming (GP) methods are proposed to evolve DDAMs for job shop environments. The experimental results show that the evolved DDAMs can make more accurate estimates than other existing dynamic DDAMs with promising reusability. In addition, the evolved operation-based DDAMs show better performance than the evolved DDAMs employing aggregate information of jobs and machines.
| Original language | English |
|---|---|
| Pages (from-to) | 105-138 |
| Journal | Evolutionary Computation |
| Volume | 22 |
| Issue number | 1 |
| Online published | 7 Feb 2014 |
| DOIs | |
| Publication status | Published - 2014 |
| Externally published | Yes |
Research Keywords
- Due-date assignment
- Genetic programming
- Hyper-heuristics
- Job shop
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