TY - GEN
T1 - Neural Network Assisted Branch-and-Bound Method for Dynamic Berth Allocation Problems
AU - Korekane, Shinya
AU - Nishi, Tatsushi
N1 - Funding Information:
*This work was supported by JSPS KAKENHI 17K18951 1S. Korekane and T. Nishi are with Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-Naka, Kita-ku, Okayama City, Okayama 700-8530, JAPAN (phone: +81 86 251 8058: e-mail: nishi.tatsushi@okayama-u.ac.jp)
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - The berth allocation problem is one of the important problems in the maritime ship operations. Efficient sea transportation can be realized by optimizing the berth operation schedule. The dynamic berth allocation problem asks to determine the allocation and berthing time of each vessel to the berth that minimizes the total service time given a set of vessels and a set of berths. The branch and bound algorithms have been used to solve the problem as an exact solution algorithm. However, it becomes intractable to solve the problem by the conventional branch and bound algorithms when the number of ships and berths is increased. In this paper, we propose a new branch-and-bound algorithm called Neural Network Assisted Branch-and-Bound (NN-BB) to determine the search priority of each node to reduce the total computation time of the branch-and-bound method. By determining the search priority of the node from the result of the neural network, the optimal solution is quickly searched for the learned problem. We compare the performance of the proposed method with the conventional branch and bound method from computational experiments.
AB - The berth allocation problem is one of the important problems in the maritime ship operations. Efficient sea transportation can be realized by optimizing the berth operation schedule. The dynamic berth allocation problem asks to determine the allocation and berthing time of each vessel to the berth that minimizes the total service time given a set of vessels and a set of berths. The branch and bound algorithms have been used to solve the problem as an exact solution algorithm. However, it becomes intractable to solve the problem by the conventional branch and bound algorithms when the number of ships and berths is increased. In this paper, we propose a new branch-and-bound algorithm called Neural Network Assisted Branch-and-Bound (NN-BB) to determine the search priority of each node to reduce the total computation time of the branch-and-bound method. By determining the search priority of the node from the result of the neural network, the optimal solution is quickly searched for the learned problem. We compare the performance of the proposed method with the conventional branch and bound method from computational experiments.
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U2 - 10.1109/SMC52423.2021.9658903
DO - 10.1109/SMC52423.2021.9658903
M3 - Conference contribution
AN - SCOPUS:85124326285
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 208
EP - 213
BT - 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2021
Y2 - 17 October 2021 through 20 October 2021
ER -