Titlebook: Computational Logistics; 14th International C Joachim R. Daduna,Gernot Liedtke,Stefan Vo? Conference proceedings 2023 The Editor(s) (if app

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書目名稱Computational Logistics影響因子(影響力)




書目名稱Computational Logistics影響因子(影響力)學(xué)科排名




書目名稱Computational Logistics網(wǎng)絡(luò)公開(kāi)度




書目名稱Computational Logistics網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書目名稱Computational Logistics被引頻次




書目名稱Computational Logistics被引頻次學(xué)科排名




書目名稱Computational Logistics年度引用




書目名稱Computational Logistics年度引用學(xué)科排名




書目名稱Computational Logistics讀者反饋




書目名稱Computational Logistics讀者反饋學(xué)科排名

engrave

ACE-inhibitor

https://doi.org/10.1007/978-3-031-43612-3computational logistics; maritime shipping; container terminal; vehicle routing; combinatorial optimizat

CYT

Immunoglobulin

形上升才刺激

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

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

悅耳

Isometric

愚笨

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