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Article Dans Une Revue ACM Transactions on Intelligent Systems and Technology Année : 2022

Two-Level Optimization to Reduce Waiting Time at Locks in Inland Waterway Transportation

Résumé

Inland vessels often have to cross numerous locks before reaching their final destination, which leads to a significant delay and sometimes represents as much as half of the total travel time. The delay affects shipment costs and can affect other parts of the transport chain, adversely impacting this transportation mode’s growth. Therefore, this work presents a two-level solution to ensure a shorter waiting time at locks and improve inland waterway transport. On the one hand, the first level focuses on making infrastructural modifications by proposing an efficient Lock Automation Decision Making (Lock-ADM) method. The problem modeling consists of using a three-stage algorithm. Firstly, we calculate the optimal number of locks while minimizing the investment costs using the exact solver, CPLEX. Secondly, we measure the importance of locks in the network, and finally, we select the best locks to automate using the Genetic Algorithm (GA) metaheuristic. Based on real data, we achieved an average reduction of 33.7% in overall lock waiting time at a low cost. On the other hand, the second level proposes a Dynamic Lock Scheduling (Lock-DS) to efficiently manage vessels scheduling at locks by minimizing their waiting time and optimizing their speed. We achieve an average reduction of 69.9% in vessel waiting time and a reduction of 48.03% in total fuel consumption compared to existing scheduling methods. Automating the most important locks with Lock-ADM and managing their crossing with Lock-DS ensure shorter vessels’ waiting time and represent a significant first step towards the automation of inland navigation.
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Dates et versions

hal-03977501 , version 1 (07-02-2023)

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Wided Hammedi, Sidi Mohammed Senouci, Philippe Brunet, Metzli Ramirez-Martinez. Two-Level Optimization to Reduce Waiting Time at Locks in Inland Waterway Transportation. ACM Transactions on Intelligent Systems and Technology, 2022, 13 (6), pp.1-30. ⟨10.1145/3527822⟩. ⟨hal-03977501⟩
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