Managing the container yard operations can be challenging as a result of various uncertainties associated with storing and retrieving containers from the yard. These associated uncertainties occur because the arrival of a truck to pick up the container is random, so the departure time of the container is unknown. The problem investigated in this paper emerges when newly arrived containers of different sizes, types and weights require storage operation in the same yard where other containers have already been stored. This situation becomes more challenging when the time of departure of existing container is not known. This study develops a new Fuzzy Knowledge-Based optimisation system named ‘FKB_GA’ for optimal storage and retrieval of containers in a yard that contains long stay pre-existing containers. The containers’ duration of stay factor is considered along with two other factors such as the similarity (containers with same customer) and the quantity of containers per stack. A new Multi-Layered Genetic Algorithm module is proposed which identifies the optimal fuzzy rules required for each set of fired rules to achieve a minimum number of container re-handlings when selecting a stack. An industrial case study is used to demonstrate the applicability and practicability of the developed system.

中文翻译：

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一种新的基于模糊知识的集装箱堆场作业管理优化系统

由于与从堆场存储和取回集装箱相关的各种不确定性，管理集装箱堆场操作可能具有挑战性。这些相关的不确定性的出现是因为卡车到达以提取集装箱是随机的，因此集装箱的出发时间是未知的。本文研究的问题出现在不同尺寸、类型和重量的新到货集装箱需要在已存储其他集装箱的同一堆场进行存储操作时。当现有集装箱的出发时间未知时，这种情况变得更具挑战性。本研究开发了一种新的基于模糊知识的优化系统，名为“FKB_GA”，用于在包含长期存放的预先存在的容器的院子中优化容器的存储和检索。集装箱的停留时间因素与其他两个因素一起考虑，例如相似性（具有相同客户的集装箱）和每堆集装箱的数量。提出了一种新的多层遗传算法模块，该模块识别每组触发规则所需的最佳模糊规则，以在选择堆垛时实现最少的容器重新处理次数。工业案例研究用于证明所开发系统的适用性和实用性。