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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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41#
發(fā)表于 2025-3-28 18:25:05 | 只看該作者
The Dynamic RORO Stowage Planning Probleme heuristic can find stowage plans for the dynamic arrival of cargo. A sensitivity analysis is conducted to investigate algorithm sensitivity in relation to revenue, fuel costs, and cargo handling time. The results indicate a high sensitivity in the number of units of cargo stowed when these parameters fluctuate.
42#
發(fā)表于 2025-3-28 22:28:37 | 只看該作者
Digital Twins in?Seaports: Current and?Future Applications twins in seaports. Moreover, we present results and insights from a major project building a digital twin for the EUROGATE container terminal in Hamburg, Germany. As such, the paper provides an overview on the maturity of digital twin applications in the port sector and discusses important aspects to be considered during implementation.
43#
發(fā)表于 2025-3-29 02:52:51 | 只看該作者
44#
發(fā)表于 2025-3-29 05:26:48 | 只看該作者
When Routing Meets Recommendation: Solving Dynamic Order Recommendations Problem in?Peer-to-Peer Logdrivers). 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
45#
發(fā)表于 2025-3-29 09:33:45 | 只看該作者
46#
發(fā)表于 2025-3-29 14:02:29 | 只看該作者
Cybersecurity Considerations for the Design of an AI-Driven Distributed Optimization of Container Cadal freight transportation system involves the use of different modes of transportation, such as trucks, trains, and ships, to move freight containers. However, this system is loaded with inefficiencies due to the poor availability of real-time coordination and disruptions, causing delays, increased
47#
發(fā)表于 2025-3-29 18:59:12 | 只看該作者
48#
發(fā)表于 2025-3-29 23:42:57 | 只看該作者
Towards a?Deep Reinforcement Learning Model of?Master Bay Stowage Planningyage. 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
49#
發(fā)表于 2025-3-30 01:26:05 | 只看該作者
The Dynamic RORO Stowage Planning Problem ports through stowage planning will increase slow-steaming use. Stowage planning assigns cargo to positions on board the vessel. This paper studies how the dynamic arrival of cargo affects stowage planning by considering revenue from shipping cargo vs. fuel costs incurred from time spent waiting an
50#
發(fā)表于 2025-3-30 05:28:22 | 只看該作者
Allocation of?Shore Side Electricity: The Case of?the?Port of?Hamburgironmental benefit is to be maximized. To this end, an optimization model is developed that yields the optimal SSE supply point allocation plan. The proposed model solves the berth allocation problem with port call data. The novelty of this model lies in the distinction of terminals, respecting the
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