decision variable

简明释义

决策变量,决策变数

英英释义

例句

1.In the supply chain model, the inventory levels are considered decision variables 决策变量 that affect overall efficiency.

在供应链模型中，库存水平被视为影响整体效率的决策变量 决策变量。

2.The marketing budget allocated to each campaign is a decision variable 决策变量 in our optimization model.

分配给每个营销活动的预算是我们优化模型中的一个决策变量 决策变量。

3.In a linear programming model, the amount of product to produce is a crucial decision variable 决策变量 that influences profit.

在线性规划模型中，生产的产品数量是一个关键的决策变量 决策变量，它会影响利润。

4.The price set for each product is a decision variable 决策变量 that can maximize revenue.

每个产品设定的价格是一个可以最大化收入的决策变量 决策变量。

5.Choosing the number of delivery trucks to operate is a significant decision variable 决策变量 for logistics companies.

选择运营的送货卡车数量是物流公司一个重要的决策变量 决策变量。

作文

In the realm of operations research and optimization, the term decision variable refers to the variables that decision-makers will decide the values of in order to achieve the best outcome in a mathematical model. These variables are crucial because they directly influence the objective function and constraints of the model, which are designed to represent the real-world scenario being analyzed. For instance, in a manufacturing context, the decision variable could represent the number of units to produce for each product line. By adjusting these variables, a company can optimize its production schedule to maximize profit or minimize costs.The importance of identifying the right decision variable cannot be overstated. It is the foundation upon which the entire model is built. If the wrong variables are chosen, the results of the optimization process may lead to suboptimal decisions that could adversely affect the organization. Therefore, careful consideration must be given to what the decision variable should be. In many cases, this involves understanding the underlying problem deeply and determining which factors are most critical to the success of the operation.Once the decision variable has been established, it can be incorporated into various optimization techniques, such as linear programming, integer programming, or nonlinear programming. Each of these methods utilizes the decision variable to explore different scenarios and determine the best course of action. For example, in a linear programming problem, the objective function might be to maximize profit, while the decision variable would represent the quantities of different products to manufacture. The solution to the problem provides the optimal levels of these decision variables, which can then be implemented in practice.Moreover, the decision variable is not always a straightforward quantity. In some cases, it may involve binary decisions, such as whether to include a project in a portfolio or not. In these instances, the decision variable can take on values of either 0 or 1, representing the absence or presence of a particular choice. This flexibility allows for a wide range of applications across various fields, including finance, logistics, and resource allocation.Another aspect to consider is the sensitivity analysis of the decision variable. After determining the optimal values, it is essential to understand how changes in the parameters of the model affect the decision variable. This analysis helps decision-makers gauge the robustness of their solution and prepares them for potential fluctuations in the market or operational constraints.In conclusion, the concept of the decision variable is fundamental to the process of optimization in operations research. It serves as the key element that decision-makers manipulate to achieve desired outcomes. A thorough understanding of what constitutes a decision variable, how to select them wisely, and how they interact with other components of the model is essential for effective decision-making in any organization. As businesses continue to face complex challenges in an increasingly competitive landscape, mastering the use of decision variables will undoubtedly enhance their ability to make informed and strategic choices, ultimately leading to better performance and success in their respective fields.

在运筹学和优化领域，术语决策变量指的是决策者将决定其值的变量，以便在数学模型中实现最佳结果。这些变量至关重要，因为它们直接影响模型的目标函数和约束条件，这些都是为了表示正在分析的现实场景。例如，在制造业背景下，决策变量可以代表每条产品线要生产的单位数量。通过调整这些变量，公司可以优化其生产计划，以最大化利润或最小化成本。确定正确的决策变量的重要性不容小觑。它是整个模型构建的基础。如果选择了错误的变量，优化过程的结果可能导致次优决策，从而对组织产生不利影响。因此，必须仔细考虑决策变量应该是什么。在许多情况下，这涉及深入理解基础问题并确定哪些因素对操作的成功最为关键。一旦确定了决策变量，就可以将其纳入各种优化技术中，例如线性规划、整数规划或非线性规划。这些方法利用决策变量来探索不同的场景并确定最佳行动方案。例如，在一个线性规划问题中，目标函数可能是最大化利润，而决策变量将代表不同产品的生产数量。问题的解决方案提供了这些决策变量的最优水平，这些水平可以在实践中实施。此外，决策变量并不总是一个简单的数量。在某些情况下，它可能涉及二元决策，例如是否将某个项目纳入投资组合。在这些情况下，决策变量可以取值0或1，分别表示某个选择的缺失或存在。这种灵活性允许在各个领域广泛应用，包括金融、物流和资源分配。另一个需要考虑的方面是对决策变量的敏感性分析。在确定最佳值之后，了解模型参数的变化如何影响决策变量至关重要。这种分析帮助决策者评估其解决方案的稳健性，并为市场或操作约束的潜在波动做好准备。总之，决策变量的概念是运筹学优化过程中的基础。它作为决策者操控的关键要素，以实现期望的结果。全面理解什么构成决策变量、如何明智地选择它们以及它们如何与模型的其他组成部分相互作用，对于任何组织的有效决策至关重要。随着企业在日益竞争激烈的环境中面临复杂挑战，掌握决策变量的使用无疑将增强它们做出明智和战略选择的能力，最终促进其在各自领域的更好表现和成功。