Impact of carbon emission and preservation investment on a manufacturing system for deteriorating item with price and greenness dependent demand via equilibrium optimizer algorithm

The environmental sustainability and eco-friendliness of a product, sometimes known as its “greenness,” can have a wide range of effects on various levels, including the human, social, economic, and environmental. In terms of a company’s income, profitability, market positioning, and client relationships, the price of a product is crucial. Choosing the ideal price/value ratio is a crucial choice that necessitates careful evaluation of numerous internal and external considerations. Combining this impact, in this study, a production model is developed for deteriorating goods, considering preservation technology investment and product’s green level and selling price dependent demand. The manufacturing firm's rate of carbon emissions increases corresponding to production’s rate and time. Also, the cost per unit of produced items increases as the green level rises. The primary goal of this study is to find the optimal policy of the manufacturer to maximize the average profit. Due to the highly nonlinearity of the objective function, the associated optimization problem of our suggested model cannot be solved analytically. Five well-known metaheuristics [Equilibrium optimizer algorithm (EOA), Artificial Electric Field Algorithm (AEFA), Grey Wolf Optimizer Algorithm (GWOA), Whale Optimizer Algorithm (WOA) and Sparrow Search Algorithm (SSA)] are used to find the best-found solutions. From the results of different statistical test (ANOVA, Wilcoxon rank sum and Friedman tests) and convergence rate of different algorithms, it is evident that EOA perform better than other metaheuristics. A numerical example is taken into consideration in order to assess the validity of the suggested model. Finally, the effects of changes in different inventory parameters are studied on the best-found solutions by performing sensitivity analyses and draw a conclusion.