Modeling city logistics using adaptive dynamic programming based multi-agent simulation
Year of publication: |
2019
|
---|---|
Authors: | Firdausiyah, N. ; Taniguchi, Eiichi ; Qureshi, A. G. |
Published in: |
Transportation research / E : an international journal. - Amsterdam : Elsevier, ISSN 1366-5545, ZDB-ID 1380969-6. - Vol. 125.2019, p. 74-96
|
Subject: | Adaptive dynamic programming | Multi-agent simulation | Reinforcement learning | City logistics | Urban consolidation center | Agentenbasierte Modellierung | Agent-based modeling | Simulation | Lernprozess | Learning process | Theorie | Theory | Dynamische Optimierung | Dynamic programming | Logistik | Logistics | Stadtökonomik | Urban economics | Lieferkette | Supply chain |
-
Torbali, Bilge, (2023)
-
Dynamic programming principles for mean-field controls with learning
Gu, Haotian, (2023)
-
Transaction selection policy in tier-to-tier SBSRS by using Deep Q-Learning
Arslan, Bartu, (2023)
- More ...
-
An exact solution approach for vehicle routing and scheduling problems with soft time windows
Qureshi, A. G., (2009)
-
Designing multimodal freight transport networks : a heuristic approach and application
Yamada, Tadashi, (2009)
-
Travel time reliability in vehicle routing and scheduling with time windows
Ando, Naoki, (2006)
- More ...