A case study on the impact of green investment with a pentagonal fuzzy storage capacity of two green-warehouse inventory systems under two dispatching policies

This paper addresses a two-warehouse inventory model that considers reliability-dependent interval-valued demand and deteriorating items while incorporating green investments. In inventory management, warehouses primarily serve the purpose of storing and protecting inventory. In this study, two warehouses are employed to store deteriorating items which require additional preservation facilities, leading to an increase in the holding cost of these commodities. Investing in green technologies can help the industry reduce warehouse holding expenses. Green technology investments aimed at reducing elevated holding costs within a two-green-warehouse inventory framework, employing both First-in-First-Out and Last-in-First-Out dispatching methods, have not been integrated into existing research. This study mitigates warehouse holding costs by implementing green technology investments. Green investment in a warehouse is a sustainable idea. This study considers both warehouses as eco-friendly with the aid of green and preservation technologies. The owned warehouse storage capacity is considered as a pentagonal fuzzy number, while the rented warehouse capacity is unlimited. The inventory system is designed to manage inventory in the warehouse using Last-in-First-Out and First-in-First-Out strategies. After comparing the policies regarding deterioration rates and holding costs, it recommends a suitable dispatch policy. Preservation technology is applied to reduce the deterioration rate of items in this warehouse inventory system. This system is designed to introduce environmentally friendly green technology, which impacts holding costs and incorporates a real case study to illustrate the current model. This study uses fuzzy hybridization to find the optimal solution for the two-warehouse inventory model. Sensitivity analysis of the different parameters has been done to study the effect of various parameters on the optimal solution.