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Title:Secure multi-party co-planning of barge departures
Authors:R.B. Larsen, R. Baksteen, B. Atasoy, R.R. Negenborn

Conference:16th IFAC International Conference on Control in Transportation (IFAC CTS'21)
Address:Lille, France
Date:June 2021

Abstract:Container transport requires a high degree of cooperation to be performed smoothly without unnecessary impact on the environment and monetary losses. The involved operators are, however, often reluctant to cooperate as they fear loosing valuable information and autonomy, eventually leading their clients to choose another (cooperating) operator. We have developed a real-time co-planning method, called Secure Departure Learning that lets several truck operators indicate to a barge operator what departure schedules they prefer without revealing any sensitive information. The method uses Paillier encryption and a learning method inspired by Bayesian Optimization. Simulated experiments on a realistic, Dutch transport network show that a centralized method with full information sharing outperforms Secure Departure Learning on cost and vehicle occupancy. They furthermore show that since using such a centralized method often is impossible, Secure Departure Learning is a good alternative for replacing the current method in practice, where barge departures are scheduled ahead of time and only mode-decisions can be updated in real time. Secure Departure Learning shows a new perspective on cooperation at the operational level in freight transport where co-planning and information protection can go hand in hand.

Reference:R.B. Larsen, R. Baksteen, B. Atasoy, R.R. Negenborn. Secure multi-party co-planning of barge departures. Accepted for the 16th IFAC International Conference on Control in Transportation (IFAC CTS'21), Lille, France, June 2021.
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