TY  - JOUR
PY  - 2023//
TI  - Algorithms for restoring disaster-struck seaport operations considering interdependencies between infrastructure availability and repair team assignments
JO  - Computers and industrial engineering
A1  - Zukhruf, Febri
A1  - Balijepalli, Chandra
A1  - Frazila, Russ Bona
A1  - Nugroho, Taufiq Suryo
A1  - Kurnia, Irma Susan
SP  - e108894
EP  - e108894
VL  - 175
IS  - 
N2  - This paper presents new algorithms for restoring seaport operations after a disaster and develops a model considering interdependencies to select an efficient course of action. The model prioritises the infrastructure to be repaired, identifies the equipment required and the number of repair teams to be deployed. This paper develops a new dynamic programming model to assign multicrew repair teams and shows that the solution is exact. This paper then develops a new variant of the Hungarian Algorithm by embedding an exploitation-exploration strategy to obtain an approximate solution for large-sized assignment problems. Furthermore, this paper solves the restoration problem in totality by accounting for interdependencies between marine/land-side infrastructure/equipment and repair team assignments. This paper also develops a new variant of Genetic Algorithm based on a deletion-mutation technique and explores reducing the computation time involved in solving optimisation problems. This paper applies the principles laid out to restore Pantoloan seaport in Indonesia which was struck by a tsunami. The approximate solution obtained by the extended Hungarian Algorithm for small problems is quicker and matches with the exact solution obtained by the new dynamic programming. In case of large-sized problems, the extended Hungarian Algorithm has been found to arrive at a solution which allows reopening the seaport 48 % sooner than the other algorithms. The new variant of Genetic Algorithm outperforms the Genetic Algorithm with Local Search, needing only 40 % of the computation time and the solution found to be particularly stable too.<p />  <p>Language: en</p>
LA  - en
SN  - 0360-8352
UR  - http://dx.doi.org/10.1016/j.cie.2022.108894
ID  - ref1
ER  -