A Learning-Based Optimal Decision Scenario for an Inventory Problem under a Price Discount Policy

This paper aims to design an inventory model for a retail enterprise with profit maximization objective using the opportunity price discount facility given by supplier. In objective, demand should be increased. The can boosted lowering selling price. However, may not always give best profit. Impreciseness plays vital role during such decision-making. decision-making and managerial activities imprecise due some decision variables. For instance, deterministic. A vague will make imprecise. To achieve this goal, retailer must minimize impreciseness as much possible. Learning through repetition practical approach in regard. investigates impact of fuzzy triangular dense setting, which dilutes involved decisions. Based on mentioned objectives, article considers price-dependent time purchasing cost-dependent holding cost uncertain phenomenon. incorporates all-units policy into unit purchase according order quantity. paper, sense learning is accounted set considering number lessen model. Four optimization methods are used obtain usual extreme when searching optimal sale It perceived from numerical outcomes that environment contributes results compared crisp general environment. Managerial insights repeated dealing enhancing profitability diluting about rate taking while purchasing.