This research study focuses on the optimization of multi-item multi-period procurement lot sizing problem for inventory management. Mathematical model is developed which considers different practical constraints like storage space and budget. The aim is to find optimum order quantities of the product so that total cost of inventory is minimized. The NP-hard mathematical model is solved by adopting a novel ant colony optimization approach. Due to lack of benchmark method specified in the literature to assess the performance of the above approach, another metaheuristic based program of genetic algorithm is also employed to solve the problem. The parameters of genetic algorithm model are calibrated using Taguchi method of experiments. The performance of both algorithms is compared using ANOVA analysis with the real time data collected from a valve manufacturing company. It is verified that two methods have not shown any significant difference as far as objective function value is considered. But genetic algorithm is far better than the ACO method when compared on the basis of CPU execution time.

中文翻译：

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基于种群的元启发式算法分析多项目多周期采购批量问题

这项研究的重点是针对库存管理的多项目多期间采购批次大小优化问题。建立了考虑不同实际约束条件（如存储空间和预算）的数学模型。目的是找到产品的最佳订购数量，以使库存总成本最小化。通过采用一种新颖的蚁群优化方法来求解NP难数学模型。由于缺乏文献中指定的基准方法来评估上述方法的性能，因此还采用了另一种基于元启发式的遗传算法程序来解决该问题。遗传算法模型的参数使用田口实验方法进行校准。使用ANOVA分析将这两种算法的性能与从阀门制造公司收集的实时数据进行比较。验证了两种方法在考虑目标函数值方面均未显示任何显着差异。但是，基于CPU执行时间进行比较时，遗传算法远优于ACO方法。