退缩 发表于 2025-3-21 16:41:54
书目名称Computational Logistics影响因子(影响力)<br> http://impactfactor.cn/2024/if/?ISSN=BK0232638<br><br> <br><br>书目名称Computational Logistics影响因子(影响力)学科排名<br> http://impactfactor.cn/2024/ifr/?ISSN=BK0232638<br><br> <br><br>书目名称Computational Logistics网络公开度<br> http://impactfactor.cn/2024/at/?ISSN=BK0232638<br><br> <br><br>书目名称Computational Logistics网络公开度学科排名<br> http://impactfactor.cn/2024/atr/?ISSN=BK0232638<br><br> <br><br>书目名称Computational Logistics被引频次<br> http://impactfactor.cn/2024/tc/?ISSN=BK0232638<br><br> <br><br>书目名称Computational Logistics被引频次学科排名<br> http://impactfactor.cn/2024/tcr/?ISSN=BK0232638<br><br> <br><br>书目名称Computational Logistics年度引用<br> http://impactfactor.cn/2024/ii/?ISSN=BK0232638<br><br> <br><br>书目名称Computational Logistics年度引用学科排名<br> http://impactfactor.cn/2024/iir/?ISSN=BK0232638<br><br> <br><br>书目名称Computational Logistics读者反馈<br> http://impactfactor.cn/2024/5y/?ISSN=BK0232638<br><br> <br><br>书目名称Computational Logistics读者反馈学科排名<br> http://impactfactor.cn/2024/5yr/?ISSN=BK0232638<br><br> <br><br>engrave 发表于 2025-3-21 20:42:07
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https://doi.org/10.1007/978-3-031-43612-3computational logistics; maritime shipping; container terminal; vehicle routing; combinatorial optimizatCYT 发表于 2025-3-22 06:14:01
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https://doi.org/10.1007/978-3-658-33729-2drivers). Thus, the efficiency of such operating model lies in the successful matching of demand and supply, i.e., how to match the delivery tasks with suitable drivers that will result in successful assignment and completion of the tasks. We consider a Same-Day Delivery Problem (SDDP) involving a P形上升才刺激 发表于 2025-3-22 17:15:34
Vorbereitung der empirischen Erhebungollecting and delivering these items within a hospital can be modeled as a Pickup and Delivery Problem with Time Windows (PDPTW). This paper proposes a hybrid dynamic optimization to address the IHL problem based on a two-step heuristic. This algorithm combines reactive and periodic optimizations to悦耳 发表于 2025-3-22 21:51:45
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Angelika Schmidt-Koddenberg,Annette Mülleryage. Due to a large variety of combinatorial aspects, a scalable algorithm to solve a representative problem is yet to be found. This paper will show that deep reinforcement learning can optimize a non-trivial master bay planning problem. Our experiments show that proximal policy optimization effic