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所在平台: Udemy |
课程主页: https://www.udemy.com/course/operations-research-assignment-and-transportation-problem/
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## 交通运输问题算法大师课程总结 本课程由拥有丰富运筹学教学经验（自1986年起）的教授倾力打造，旨在以浅显易懂的方式，帮助学生掌握交通运输问题算法。课程摒弃了复杂的数学推导，让没有数学背景的学习者也能轻松理解。 **课程亮点：** * **全面覆盖：** 深入解析交通运输问题的求解，涵盖了**最小化**和**最大化**两种情况，并重点讲解了**退化问题**的处理。 * **多方法教学：** 学习三种获得**初始可行解**的方法：**西北角法则**、**最小成本法**和**伏格尔近似法（VAM）**。 * **进阶优化：** 学习**调整优化**阶段的模型，即**模态分配法（MoDi）**。 * **应对不平衡问题：** 讲解如何解决**不平衡的交通运输问题**。 * **实际应用：** 通过一个具体的**调度问题**示例，展示算法的实际应用。 * **循序渐进：** 采用**分步讲解**的方式，从“预检查”开始，帮助学生逐步掌握问题的建模与求解。 * **转化技巧：** 学习如何将**最大化问题**转化为**最小化问题**进行求解。 本课程的目标是让学生能够独立地将实际情境建模为交通运输问题，并运用所学算法独立求解。
I am a professor of Operations Research, teaching this subject from 1986 onwards. Linear Programming, Assignment Problem, Transportation Problem, Networking Techniques, Game Theory are some of the major algorithms in Operations Research. My attempt, over the years has been to teach without involving great deal of mathematics, so that the student without any math background can also understand the algorithm easily. In the initial phase I launched the courses on Linear Programming, Assignment Problem, Transportation Problem, Sequencing at one go. This 10 hour video course will give you complete insight into solving a Transportation Problem. Both minimization and maximization cases will be covered, including the more complex degenerate problems. The initial Feasible solution to a transportation problem will be obtained by one of the three methods namely North West Cornet Rule, Least Cost Method or Vogel's Approximation Method. In the second phase, we will cover the MoDi or Modified Distribution Method. The solution to the unbalanced Transportation Problem has been covered. A representative problem of applying the algorithm to a scheduling problem has been covered in the course. In the case of the this algorithm, step by step approach to solving the problems has been introduced, starting with what I call as ‘prechecks'. The idea is that the student should be able to formulate and solve the problem on her own. While both these algorithms are for minimization, the problems for maximization can also be solved by converting them into minimization first. Examples of this effect are covered. I hope that the student will be able to formulate a given situation as one of these algorithm problems and solve it on his own.
