As environmental pollution has become a serious problem in recent years, production firms have now shifted their attention to lower the carbon emission level along with their financial goal. Now, one major drawback of the available models is that they consider the amount of carbon emission associated with various inventory processes like setup process, production process, inventory holding process, etc. as fixed numbers. However, these quantities may not be fixed in reality due to several factors, like the condition of the carbon filtrating apparatus in the production firm, machine breakdown, quality of the raw material, nature of the fuel or energy used, etc. The parameters may vary in between some certain ranges. This paper aims at developing an imperfect production inventory model under the various carbon emission regulatory policies where the various carbon emission parameters are interval numbers. The various inventory cost parameters are also represented as interval numbers to reflect the practical situation. The demand of the product is assumed to be a linearly decreasing function of the selling price of the product. Also, the idea of a new inventory cost component, referred to as the ‘development cost’ is introduced. Four different policies- simple tax policy, cap and purchase policy, cap and reward policy and strictly under permitted cap policy are discussed, and based on those policies four different inventory models are developed. The Quantum behaved Particle Swarm Optimization(QPSO) algorithm has been applied to find the optimal profit of the manufacturer. The optimal profit obtained here is also an interval number which is quite realistic. The traditional fuzzy or probabilistic approach to represent impreciseness fails to find such kind of solution. A numerical example for every case has been given to illustrate the applicability of the models. Finally, sensitivity analysis of the optimal feasible solution due to variation in some key parameters has been provided with the key managerial insights.

由于近年来环境污染已成为一个严重的问题，生产公司现在已经将注意力转移到降低碳排放水平以及其财务目标上。现在，可用模型的一个主要缺点是它们将与各种库存过程（如设置过程，生产过程，库存保持过程等）相关的碳排放量视为固定数字。但是，由于若干因素，例如生产公司中碳过滤装置的状况，机器故障，原材料的质量，所用燃料或能源的性质等，这些数量实际上可能无法固定。在某些特定范围内变化。本文旨在在各种碳排放监管政策（其中各种碳排放参数为区间数）下建立不完善的生产库存模型。各种库存成本参数也以间隔号表示，以反映实际情况。产品需求被假定为产品售价的线性下降函数。另外，引入了新的库存成本组成部分的想法，称为“开发成本”。讨论了四种不同的政策-简单的税收政策，限额和购买政策，限额和奖励政策以及严格在允许的限额政策下，并基于这些政策开发了四种不同的库存模型。量子行为粒子群优化算法已被用于寻找制造商的最优利润。这里获得的最佳利润也是一个很现实的区间数。代表不精确性的传统模糊或概率方法无法找到这种解决方案。给出了每种情况的数值示例，以说明模型的适用性。最后，通过关键管理见解提供了由于某些关键参数的变化而导致的最佳可行解的敏感性分析。给出了每种情况的数值示例，以说明模型的适用性。最后，通过关键管理见解提供了由于某些关键参数的变化而导致的最佳可行解的敏感性分析。给出了每种情况的数值示例，以说明模型的适用性。最后，通过关键管理见解提供了由于某些关键参数的变化而导致的最佳可行解的敏感性分析。