Name
Title | ||
|---|---|---|
Dynamic scheduling for multi-site companies: a decisional approach based on reinforcement multi-agent learning |
Abstract | ||
|---|---|---|
In recent years, most companies have resorted to multi-site or supply-chain organization in order to improve their competitiveness and adapt to existing real conditions. In this article, a model for adaptive scheduling in multi-site companies is proposed. To do this, a multi-agent approach is adopted in which intelligent agents have reactive learning capabilities based on reinforcement learning. This reactive learning technique allows the agents to make accurate short-term decisions and to adapt these decisions to environmental fluctuations. The proposed model is implemented on a 3-tier architecture that ensures the security of the data exchanged between the various company sites. The proposed approach is compared to a genetic algorithm and a mixed integer linear program algorithm to prove its feasibility and especially, its reactivity. Experimentations on a real case study demonstrate the applicability and the effectiveness of the model in terms of both optimality and reactivity. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/s10845-011-0580-y | J. Intelligent Manufacturing |
Keywords | Field | DocType |
multi agent system,scheduling,reinforcement learning | Intelligent agent,Production control,Scheduling (computing),Multi-agent system,Linear programming,Artificial intelligence,Engineering,Dynamic priority scheduling,Machine learning,Genetic algorithm,Reinforcement learning | Journal |
Volume | Issue | ISSN |
23 | 6 | 0956-5515 |
Citations | PageRank | References |
17 | 0.80 | 31 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| N. Aissani | 1 | 60 | 3.83 |
| Abdelghani Bekrar | 2 | 97 | 14.30 |
| D. Trentesaux | 3 | 95 | 8.29 |
| Bouziane Beldjilali | 4 | 65 | 9.29 |