随机编程在农业和新闻供应商问题中的应用及其在现实生活中的应用

Afsana Akter Mitu, M. B. Hasan
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摘要

在我们生活的各个领域中,研究如何在风险管理的情况下做出最佳决策,这就是所谓的随机程序设计(Stochastic Programming)。我们将通过两阶段随机线性规划方法来解决现实世界中的各种选择问题,以及如何解决这些问题。为此,我们将根据现实世界中的各种情况,如众所周知的农民问题和新闻供应商问题,构建随机线性规划模型。定价的影响、随机整数线性规划问题、第二阶段随机整数线性规划问题、第一阶段随机二元线性规划问题、风险规避问题以及基于两阶段随机整数线性规划并借助农民问题的随机变量连续函数都将得到研究。我们将再次利用两阶段随机线性规划方法,用确定性等效随机线性规划来解决新闻供应商问题,这是确定性随机线性规划的扩展,用于解决具有大量决策变量和限制条件的风险规避问题。手算是获取问题解决方案的一种具有挑战性的方法。因此,我们将使用编程语言 AMPL 设计计算机解决方案,以解决农民和新闻供应商的难题。我们还将利用 MATLAB 为农民问题的连续函数创建图形:30-45, 2024 (January)
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Application of Stochastic Programming in Agricultural and Newsvendor Problems and It's Application in Real Life
The study of making the best decision under risk management in a variety of areas of our lives is known as Stochastic Programming. We will go through two-stage Stochastic Linear Programming approaches for a variety of real-world choice issues, as well as how to solve them. We will achieve this by constructing stochastic linear programming models based on real-world situations like the well-known Farmer's situation and News Vendors problems. The influence of pricing, Stochastic Integer Linear Programming problem, second stage Stochastic Integer Linear Programming problem, first stage Stochastic Binary Linear Programming problem, risk aversion problem, and continuous function for random variables based on two-stage SLP with the aid of Farmer's problem will all be examined. We will address the Newsvendor’s problem with Deterministic Equivalent Stochastic Linear Programming, an extension of Deterministic Stochastic Linear Programming for risk aversion with a high number of decision variables and restrictions, utilizing the two-stage Stochastic Linear Programming approach once more. Hand calculation is a challenging way to acquire the solution to the problems. As a result, we will use the programming language AMPL to design computer solutions for tackling both farmer and newsvendor difficulties. We will also utilize MATLAB to create graphs for the farmer's problem's continuous function. Dhaka Univ. J. Sci. 72(1): 30-45, 2024 (January)
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