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

A. F. Momena, M. M. Rahaman, Rakibul Haque, S. Alam, S. Mondal
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引用次数: 2

Abstract

This paper aims to design an inventory model for a retail enterprise with a profit maximization objective using the opportunity for a price discount facility given by a supplier. In the profit maximization objective, the demand should be increased. The demand can be boosted by lowering the selling price. However, lowering the selling price may not always give the best profit. Impreciseness plays a vital role during such decision-making. The decision-making and managerial activities may be imprecise due to some decision variables. For instance, the selling price may not be deterministic. A vague selling price will make the retail decision imprecise. To achieve this goal, the retailer must minimize impreciseness as much as possible. Learning through repetition may be a practical approach in this regard. This paper investigates the impact of fuzzy impreciseness and triangular dense fuzzy setting, which dilutes the impreciseness involved with managerial decisions. Based on the mentioned objectives, this article considers an inventory model with price-dependent demand and time and a purchasing cost-dependent holding cost in an uncertain phenomenon. This paper incorporates the all-units discount policy into the unit purchase cost according to the order quantity. In this paper, the sense of learning is accounted for using a dense fuzzy set by considering the unit selling price as a triangular dense fuzzy number to lessen the impreciseness in the model. Four fuzzy optimization methods are used to obtain the usual extreme profit when searching for the optimal purchasing cost and sale price. It is perceived from the numerical outcomes that a dense fuzzy environment contributes the best results compared to a crisp and general fuzzy environment. Managerial insights from this paper are that learning from repeated dealing activities contributes to enhancing profitability by diluting impreciseness about the selling price and demand rate and taking the best opportunity from the discount facility while purchasing.
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价格折扣政策下库存问题的基于学习的最优决策方案
本文旨在利用供应商提供的价格折扣机会,设计一个以利润最大化为目标的零售企业库存模型。在利润最大化目标下,需求应该增加。降低售价可以刺激需求。然而,降低销售价格不一定会带来最好的利润。在这种决策过程中,不精确性起着至关重要的作用。由于一些决策变量的存在,决策和管理活动可能不精确。例如,销售价格可能不确定。模糊的销售价格会使零售决策不准确。为了实现这一目标,零售商必须尽可能地减少不准确性。在这方面,通过重复学习可能是一种实用的方法。本文研究了模糊不精确性和三角密集模糊设置对管理决策不精确性的影响。基于上述目标,本文考虑了不确定情况下需求与时间依赖于价格、持有成本依赖于采购成本的库存模型。本文根据订单数量,将全单位折扣策略引入到单位采购成本中。本文将单位销售价格作为一个三角形的密集模糊数,利用密集模糊集来考虑学习的意义,以减少模型的不精确性。在寻找最优采购成本和最优销售价格时,采用四种模糊优化方法获得通常的极值利润。从数值结果中可以看出,与清晰和一般模糊环境相比,密集模糊环境贡献了最好的结果。本文的管理见解是,从重复的交易活动中学习有助于提高盈利能力,通过稀释关于销售价格和需求率的不准确性,并在购买时从折扣设施中获得最佳机会。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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