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Static and Discrete Berth Allocation for Large-Scale Marine-Loading Problem by Using Iterative Variable Grouping Genetic Algorithm

In this paper, we study the static discrete berth allocation problems (BAPs) for large-scale time-critical marine-loading scenarios. The objective is to allocate the vessels to different types of berths so that all the vessels can be loaded within the minimum time under the tidal condition. The BAP is formalized as a min–max problem. This problem is rather complex as the vessels and berths are quite numerous in the large-scale marine-loading problem. We analyze this problem from a novel perspective, and find out that this problem has the characteristic of partially separable. Therefore, the iterative variable grouping genetic algorithm (IVGGA) is designed to search the near-optimal berth allocation plans. The vessels and berths are divided into subgroups, and the genetic algorithm (GA) is applied to generate the near-optimal berth allocation plans in each subgroup. To achieve the balance of loading tasks among subgroups, we propose reallocating some vessels among subgroups according to the berth allocation plans in subgroups. To guarantee the convergency of the algorithm, an iterative vessel reallocation policy is devised considering the loading tasks of different types of berths. We demonstrate the proposed algorithm in dealing with large-scale BAPs through numerical experiments. According to the results, we find that the proposed algorithm would have good performance when the number of vessels in each subgroup are kept in medium scale. Compared with the original GA, our algorithm shows the effectiveness of the iterative variable grouping strategy. The performance of our algorithm is almost not changed as the number of vessels and berths increases. The proposed algorithm could obtain efficient berth allocation plans for the large-scale marine-loading problem.