Salhi Abdellah, Alsoufi Ghazwan, Yang Xinan
1Department of Mathematical Sciences, University of Essex, Colchester, UK.
2College of Computer Sciences and Mathematics, University of Mosul, Mosul, Iraq.
Ann Oper Res. 2019;272(1):69-98. doi: 10.1007/s10479-017-2539-7. Epub 2017 May 22.
This paper puts forward an integrated optimisation model that combines three distinct problems, namely berth allocation, quay crane assignment, and quay crane scheduling that arise in container ports. Each one of these problems is difficult to solve in its own right. However, solving them individually leads almost surely to sub-optimal solutions. Hence, it is desirable to solve them in a combined form. The model is of the mixed-integer programming type with the objective being to minimize the tardiness of vessels and reduce the cost of berthing. Experimental results show that relatively small instances of the proposed model can be solved exactly using CPLEX. Large scale instances, however, can only be solved in reasonable times using heuristics. Here, an implementation of the genetic algorithm is considered. The effectiveness of this implementation is tested against CPLEX on small to medium size instances of the combined model. Larger size instances were also solved with the genetic algorithm, showing that this approach is capable of finding the optimal or near optimal solutions in realistic times.
本文提出了一个综合优化模型,该模型结合了集装箱港口中出现的三个不同问题,即泊位分配、岸桥分配和岸桥调度。这些问题中的每一个本身都很难解决。然而,单独解决它们几乎肯定会导致次优解决方案。因此,以组合形式解决它们是可取的。该模型属于混合整数规划类型,目标是最小化船舶的延迟并降低靠泊成本。实验结果表明,所提出模型的相对较小实例可以使用CPLEX精确求解。然而,大规模实例只能使用启发式方法在合理时间内求解。在此,考虑了遗传算法的一种实现。针对组合模型的中小规模实例,将这种实现的有效性与CPLEX进行了测试。较大规模的实例也用遗传算法求解,表明这种方法能够在实际时间内找到最优或接近最优的解决方案。