Abstract
In contemporary times, the environment is being progressively polluted by non-eco-friendly products from manufacturing sectors. Therefore, it is vital for individuals to be aware of the necessity of employing environmentally friendly items as a means to mitigate pollution. This consciousness, in return, drives an instant increase in the desire for environmentally friendly products, greatly improving their ecological sustainability. In this context, this study proposes a novel perishable inventory model that incorporates environmental attributes into demand and cost functions, which contributes to sustainable inventory management research. The maximum potential lifespan of a product is a crucial aspect of inventory management, especially when considering its suitability for reuse. One notable challenge in the connection between suppliers/manufacturers and merchants for products accessible during seasonal periods with high demand pertains to the issue of payment in advance. Integrating these multifaceted elements results in a perishable commodity inventory model characterized by a customer demand rate depending on the product's green level and price, an interval-valued holding cost, and a linearly time-dependent holding cost. A partial backlog of shortages with interval values is incorporated in this model. The associated optimization problem is characterized as a maximization problem, wherein the objective function exhibits values throughout an interval. To assess the accuracy and reliability of the proposed model, the Arctic Puffin Optimization (APO) algorithm is employed to analyze and solve a specific numerical illustration. Furthermore, seven other algorithms (Dandelion Optimizer (DO), Grey wolf optimizer (GWO), The whale optimization algorithm (WOA), Artificial electric field algorithm (AEFA), Harris hawks optimization (HHO), Multi-verse optimizer (MVO) and Slime mould algorithm (SMA)) are used to compare the obtained solution from APO. Quantitatively, the APO and DO algorithms provid the same solution for the given example. However, during the statistical test for review the performance of the algorithms, it is observed that APO is outperformed among all other algorithms. Subsequently, a post-optimality analysis examines the quantitative effects of changes made to different inventory parameters, which results in an insightful conclusion. This study not only contributes to the theoretical framework of perishable commodity inventory modeling but also provides practical implications for sustainable inventory management in response to environmental concerns.