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