A fast approach for the integrated berth allocation and quay crane assignment problem. (November 2015)

• Record Type: Journal Article
• Authors: Ma, HL; Chan, Felix TS; Chung, SH
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 229:Number 11(2015:Nov.)
• Page Start: 2076
• Page End: 2087
• Publication Date: 2015-11
• Subjects: Berth allocation -- quay crane assignment -- vessel scheduling -- terminal -- genetic algorithm; Mechanical engineering -- Periodicals; Engineering -- Management -- Periodicals; Manufacturing processes -- Periodicals; 629.8
• DOI: 10.1177/0954405414544555
• Languages: English
• ISSNs: 0954-4054

Abstract

Integrated planning of berth allocation and quay crane assignment in multi-user container terminals has recently attracted much attention by researchers and terminal practitioners. In the literature, many modeling and algorithms in dealing with this integrated problem (e.g. evolution algorithm and greedy search) have been proposed, competing in the solution quality and computational time. However, it is found that holistically solving this integrated problem may not be efficient due to its complexity. Adequately decomposing the problem can increase the solution quality. Meanwhile, it can reduce the computational time required. In this connection, the main contribution of this article is to propose a new two-level genetic algorithm. This algorithm is different from the traditional genetic algorithm in modeling berth allocation and quay crane assignment in the encoding, decoding and evolution mechanisms. The proposed algorithm decomposes the integrated problem into a master problem and a sub-problem, representing the quay crane assignment and the corresponding vessel schedule in each berth. This decomposing approach is designed to enhance the local searching ability, while maintaining the global searching ability of the genetic algorithm. To test the solution quality, existing algorithm found in the literature has been compared. Furthermore, a set of numerical experiment has been carried out to compare the proposed algorithms with the optimal solution obtained by CPLEX. The result demonstrated the proposed algorithm outperforms the existing algorithm and can obtain near-optimal solution. We have also demonstrated the significance of decomposing the model by comparing with traditional single genetic algorithm approach in this field.