The newsvendor problem is known as one the classical problems in the inventory management context which has received a great deal of attention during the past few decades. In this paper, a two-level simulation-based framework is proposed, where in the first level, agent-based simulation is developed to model the effect of advertising intensity and word-of-mouth on the demand in order to estimate the demand distribution under various levels of advertising intensity. The results from the agent-based model are then plugged into a Monte Carlo simulation model in order to make the final decision on the optimal advertising intensity and economical order quantity with the objective to maximize the expected profit. The proposed approach is then applied to a hypothetical newsvendor problem to illustrate its applicability as a decision support tool for solving real-world newsvendor problems.
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