Carbon emission controlled investment and warranty policy based production inventory model via meta-heuristic algorithms

Over the last few years, manufacturing firms are required to take more action in order to control the carbon emissions from their activities. Again, the warranty of a product is an important factor for both buyers and manufacturers. However, due to uncertain market situations and fluctuations of customers' demand, to conduct detailed analysis of a manufacturing system is a complicated task. Primarily addressing these concepts together, in this work, an interval valued production inventory model is formulated under carbon taxation regulation which demand is dependent on warranty period of the products. The main objective of this work is to determine the effect of warranty period of the products on the optimal policy of the production firm. Parallelly, this work also navigates the effect of carbon taxation regulation on the revenue of the manufacturer. There arises an in-terval optimization problem that is solved by centre-radius optimization technique. Further, to illustrate the validity of the model, a numerical example is considered and solved by different variants of quantum behaved particle swarm optimization (QPSO) techniques, grey-wolf optimizer algorithm (GWOA), teaching learning based optimizer algorithm (TLBOA), sparrow search algorithm (SSA). From the findings of numerical solution, is observed that all the algorithms are equally efficient. Sensitivity analysis indicates that the centre of the average profit of the system increases most significantly as the initial demand rate of the products increases. Further, the warranty period of the products affects the optimal policy of the system in a suffice way and carbon taxation affects the revenue of the system less significantly. Finally, as a practical illustration of the model, another numerical example is taken into account considering the manufacturing and business strategies of LED monitor in a local manufacturing firm of Kolkata (India).