ABSTRACT This paper presents two inventory models for ameliorating items under crisp and interval environments. In these models, three-parameter Weibull distribution is considered to represent both the amelioration and deterioration rates. In crisp, an inventory model is formulated for ameliorating item with fixed values of different inventory parameters. Due to uncertainty, these parameters may not be fixed. In this context, another inventory model with interval valued parameters is developed. Also, demand is dependent on the selling price and advertisement frequency of the product. The corresponding profit maximization problem has been developed. For solving the problem, different variants of quantum behaved particle swarm optimization technique (QPSO) are applied. To validate the proposed models, two numerical examples are considered and solved. The results are compared for different variants of QPSO techniques. Finally, graphical sensitivity analyses are presented to study the impact of several system parameters on cycle length, initial stock level along with average profit for both the models.

中文翻译：

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通过基于量子行为粒子群优化技术的不同变体，改善具有 Weibull 分布式改善和恶化的项目的 Crisp 和间隔库存模型

摘要 本文提出了两种库存模型，用于在清脆和间隔环境下改善物品。在这些模型中，三参数威布尔分布被认为代表了改善率和恶化率。简而言之，为改进具有不同库存参数的固定值的项目，制定了库存模型。由于不确定性，这些参数可能不是固定的。在这种情况下，开发了另一个具有区间值参数的库存模型。此外，需求取决于产品的售价和广告频率。相应的利润最大化问题已经得到发展。为了解决这个问题，应用了量子行为粒子群优化技术（QPSO）的不同变体。为了验证所提出的模型，考虑并解决了两个数值例子。比较了 QPSO 技术的不同变体的结果。最后，通过图形敏感性分析来研究几个系统参数对周期长度、初始库存水平以及两种模型的平均利润的影响。