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所在平台: Udemy |
课程主页: https://www.udemy.com/course/simplex-method-in-linear-programming/
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课程名称：线性规划中的单纯形法 课程概述：线性规划是指用于在可用资源的情况下确定相互依赖活动的最佳调度的编程模型。这是一种科学或数学技术，用于在竞争活动中分配有限资源，以优化给定目标。线性规划（LP）是一种数学方法，用于确定在给定的数学模型中实现最佳结果（例如，最大利润或最低成本）的方法，要求条件以线性方程的形式表示。更正式地说，线性规划是一种线性目标函数优化的技术，受限于线性等式和线性不等式约束。线性规划可以应用于多个研究领域，主要广泛应用于商业和经济学，同时也可用于某些工程问题。使用线性规划模型的行业包括运输、能源、电信和制造业。它在规划、调度、分配和设计等多种问题的建模中证明了其有效性。 本课程将帮助学员理解： - 使用单纯形法解决线性规划问题 - 深入研究单纯形法及其有效解题机制 - 最大化和最小化案例的解决方案 - 使用大M法和两阶段法解决线性规划问题 - 人工变量在单纯形法中的应用 - 不可行解和无界解的概念 本课程适合希望深入了解线性规划及其应用的学员。
Linear Programming refers to those programming models which are used in determining an optimum schedule of interdependent activities in view of the available resources'. They are scientific or mathematical techniques which are used to allocate the limited recourses among the competitive activities so as to optimize the given objective. Linear programming (LP) is a mathematical method for determining a way to achieve the best outcome (such as maximum profit or lowest cost) in a given mathematical model for some list of requirements represented as linear equations. More formally, linear programming is a technique for the optimization of a linear objective function, subject to linear equality and linear inequality constraints. Linear programming can be applied to various fields of study. It is used most extensively in business and economics, but can also be utilized for some engineering problems. Industries that use linear programming models include transportation, energy, telecommunications, and manufacturing. It has proved useful in modeling diverse types of problems in planning, routing, scheduling, assignment, and design.This course will help in understanding:Solution of Linear Programming Problem using Simplex MethodDepth Study of Simplex Method and mechanism of solving it effectivelySolution of Maximization case and Minimization caseSolving Linear Programming Problems using Big-M Method and Two Phase MethodUsage of Artificial Variables in Simplex MethodConcept of Non Feasible Solution and Unbounded Solution
