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首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >A Nonlinear Programming Approach to Solve the Stochastic Multi-objective Inventory Model Using the Uncertain Information
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A Nonlinear Programming Approach to Solve the Stochastic Multi-objective Inventory Model Using the Uncertain Information

机译：一种使用不确定信息解决随机多目标库存模型的非线性规划方法

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摘要

A multi-objective, multi-item fuzzy stochastic inventory model is constructed for deteriorating items under limited storage space as well as capital investment. Demand is considered as a function of price and frequency of advertisements. In this model, some parameters are considered to be vague and some are random. The vagueness of parameters is represented by membership function, and randomness of parameters is represented by a probability distribution. In the inventory model, if some parameters are vague and some are probabilistic, then the model is called a fuzzy stochastic model. Here, parameters such as purchasing cost, shortage costs as well as a capital investment are considered to be random in nature and storage space is considered as imprecise. The randomness of a parameter is represented by a normal distribution, and the impreciseness of parameters is expressed using linear membership function. By using fuzzy nonlinear programming (FNLP) and intuitionistic fuzzy optimization (IFO) techniques, a solution for the multi-objective fuzzy stochastic inventory model is obtained. The major goal of the paper is to find an optimal quantity to be replenished. The objective of this work is to study the effect of capital investment and warehouse space on profit as well as shortage cost through sensitivity analysis. The other objective is to compare the efficiency of FNLP and IFO techniques for obtaining solutions through numerical results. This paper shows that FNLP works better than IFO in case of minimizing shortage cost.
机译：多目标多项模糊随机库存模型用于降低有限的存储空间下的项目以及资本投资。需求被认为是广告价格和频率的函数。在该模型中，一些参数被认为是模糊的，有些参数是随机的。参数的模糊性由隶属函数表示，参数的随机性由概率分布表示。在库存模型中，如果某些参数模糊而且有些参数是概率，那么该模型称为模糊随机模型。这里，诸如购买成本，短缺成本以及资本投资的参数被认为是随机的自然，存储空间被认为是不精确的。参数的随机性由正常分布表示，使用线性成员函数表示参数的不精确性。通过使用模糊非线性编程（FNLP）和直觉模糊优化（IFO）技术，获得了多目标模糊随机库存模型的解决方案。本文的主要目标是找到要补充的最佳数量。这项工作的目标是通过敏感性分析研究资本投资和仓库空间的效果，以及缺乏缺乏成本。另一个目的是通过数值结果比较FNLP和IFO技术的效率来获得解决方案。本文表明，在最小化短缺成本的情况下，FNLP优于IFO。

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