Cite Article
Cite Article
MLA
Yu, Fahong, et al. "Logistics Distribution Route Optimization With Time Windows Based on Multi-Agent Deep Reinforcement Learning." IJITSA vol.17, no.1 2024: pp.1-23. http://doi.org/10.4018/IJITSA.342084
APA
Yu, F., Chen, M., Xia, X., Zhu, D., Peng, Q., & Deng, K. (2024). Logistics Distribution Route Optimization With Time Windows Based on Multi-Agent Deep Reinforcement Learning. International Journal of Information Technologies and Systems Approach (IJITSA), 17 (1), 1-23. http://doi.org/10.4018/IJITSA.342084
Chicago
Yu, Fahong, et al. "Logistics Distribution Route Optimization With Time Windows Based on Multi-Agent Deep Reinforcement Learning," International Journal of Information Technologies and Systems Approach (IJITSA) 17, no.1: 1-23. http://doi.org/10.4018/IJITSA.342084
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International Journal of Information Technologies and Systems Approach (IJITSA) The
International Journal of Information Technologies and Systems Approach (IJITSA) is a refereed global journal where a systemic interdisciplinary and/or a multi-methodology perspective of information systems, software engineering, and systems engineering are encouraged. The articles accepted in IJITSA address relevant and contemporaneous or recurrent and unsolved classic problems, opportunities, challenges, and solutions found in IT systems. The journal pursues a better understanding and development of the three addressed disciplines from an interdisciplinary and/or multi-methodology perspective focused on the construct IT system.
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