Nowadays, use of various types of hybrid metaheuristic algorithms attracts the researchers to optimize the average profit or cost of an inventory system to avoid the local optimality due to high nonlinearity of the corresponding optimization problem. This paper deals with an application of binary tournament-based quantum-behaved particle swarm optimization algorithms on an imperfect production inventory problem with shortages. In order to reduce the production of defective items, modern/improvement technology has been incorporated in the production system. Also, the demand of the product is assumed to be dependent on its warranty period and selling price. The main objective of this study is to optimize the production rate, production period, selling price of the product, manufacturer’s improvement technology level and maximum shortage level as well as maximize the average profit of the production system. For this purpose, three hybrid metaheuristic algorithms based on binary tournamenting and different variants of quantum-behaved PSO techniques have been developed. Then to examine the validity of the proposed model, three numerical examples have been solved. Considering each example, nonparametric statistical tests have been performed by using four different methods to analyze the performance of the used algorithms. Finally, sensitivity analyses have been performed to investigate the effects of different parameters on optimal policy.

如今，各种类型的混合元启发式算法的使用吸引了研究人员优化库存系统的平均利润或成本，以避免由于相应优化问题的高度非线性而导致的局部最优。本文讨论了基于二进制锦标赛的量子行为粒子群优化算法在具有短缺的不完美生产库存问题上的应用。为了减少次品的生产，现代/改进技术已被纳入生产系统。此外，假设产品的需求取决于其保修期和售价。本研究的主要目标是优化产品的生产率、生产周期、销售价格，制造商的改进技术水平和最大短缺水平以及最大化生产系统的平均利润。为此，开发了三种基于二进制锦标赛和不同变体的量子行为 PSO 技术的混合元启发式算法。然后为了检验所提出模型的有效性，已经解决了三个数值例子。考虑到每个示例，通过使用四种不同的方法来分析所用算法的性能，已经执行了非参数统计测试。最后，进行了敏感性分析以研究不同参数对最优策略的影响。已经开发了三种基于二进制锦标赛和不同变体的量子行为 PSO 技术的混合元启发式算法。然后为了检验所提出模型的有效性，已经解决了三个数值例子。考虑到每个示例，通过使用四种不同的方法来分析所用算法的性能，已经执行了非参数统计测试。最后，进行了敏感性分析以研究不同参数对最优策略的影响。已经开发了三种基于二进制锦标赛和不同变体的量子行为 PSO 技术的混合元启发式算法。然后为了检验所提出模型的有效性，已经解决了三个数值例子。考虑到每个示例，通过使用四种不同的方法来分析所用算法的性能，已经执行了非参数统计测试。最后，进行了敏感性分析以研究不同参数对最优策略的影响。