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Simulation-based optimization for discharge/loading operations at a maritime container terminal

机译:基于仿真的海运集装箱码头卸货/装卸优化

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The discharge/loading process of a single container ship by multiple quay cranes and shuttle vehicles moving back and forth from the quay to the yard and vice versa is focused in this paper. The core problem of this major operational issue reduces to finding the optimal assignment and optimal sequencing (schedule) of bays (jobs) processed by a fixed number of available cranes (machines). Under the classical assumption that machines have no release time and that their processing occurs with continuity, at a constant rate, in literature it has been tackled as a deterministic machine scheduling problem and formulated by integer programming as the quay crane scheduling problem (QCSP). Here, instead, the QCSP is viewed as a decisional step within an uncertain and dynamic logistic process where the quay cranes are the resources to be managed at the best, i.e., by minimizing the time spent waiting for each other due to conflicts, as well as the time wasted for blocking and starvation phenomena due to congestion occurring along the path from the quay area and to the stacking yard and vice versa. We present a simulation-based optimization (SO) model for this wider modeling problem with the objective of finding the schedule which optimizes a classical objective function. The search process for the optimal schedule is accomplished by a simulated annealing (SA) algorithm, while performance estimation of the overall container discharge/loading process is provided by the simulation framework as a whole. Numerical experiments on a real instance are presented for tuning purposes of the SA procedure implemented within the simulator.
机译:本文重点讨论了由多台码头起重机和往返码头往返的穿梭车辆对一艘集装箱船的卸货/装货过程,反之亦然。这个主要操作问题的核心问题减少到找到由固定数量的可用起重机(机器)处理的托架(作业)的最佳分配和最佳排序(计划)。在经典的假设中,机器没有释放时间,并且它们的处理过程以恒定的速率连续发生,在文献中,它已被视为确定性机器调度问题,并通过整数编程表述为码头起重机调度问题(QCSP)。在这里,QCSP被视为不确定和动态物流过程中的决定性步骤,在该过程中,码头起重机是最佳管理资源,即,通过最小化由于冲突而彼此等待的时间因为从码头区到堆场的路径上发生拥堵,浪费了因阻塞和饥饿现象而浪费的时间,反之亦然。我们针对此更广泛的建模问题提出了基于仿真的优化(SO)模型,其目的是找到优化经典目标函数的进度表。最佳计划的搜索过程是通过模拟退火(SA)算法完成的,而整个集装箱卸货/装载过程的性能估算是由整个模拟框架提供的。出于仿真器中实现的SA程序调整目的,提出了在真实实例上的数值实验。

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