Enhancing facility location modeling and optimization

工业企业、零售商店或政府机构提高成功机会的最重要方法之一是确定良好的设施位置。一种方法是使用位置分配模型，对服务或制造设施进行最佳定位，并分配对它们的需求或供应：目的是为这些设施找到最佳的位置和服务区域集。位置/物流模型是由它们的目标函数和被认为是现实的约束来定义的。然而，经典模型中的目标函数和约束往往非常简单。例如，考虑一下最流行的位置分配模型，p中值模型，其目标显然是最小化需求点和设施点之间的距离（或旅行成本）。在许多现实世界的情况下，假设没有能力的设施、分配给最近的设施的需求点（传统上是优化模型中的约束）以及点对点服务是不现实的。放松或消除这些假设对于增强设施位置建模和优化至关重要，从而产生更好地结合现实条件的解决方案。

One of the most important ways an industrial firm, retail outlet, or government agency can enhance its chances of success is to identify good facility locations. One approach is to use location-allocation models that optimally locate service or manufacturing facilities and allocate demand or supply to them: the intent is to find the best set of locations and service areas for these facilities. Location/logistics models are defined by their objective functions and what are thought to be realistic constraints. However, the objective functions and the constraints in the classic models tend to be quite simple. Consider, for instance, the most popular location-allocation model, the p-median, where the objective is plainly to minimize the distance (or travel cost) between demand and facility points. In many real-world situations, assumptions as uncapacitated facilities, demand points that are allocated to the closest facility (traditionally a constraint in the optimization models), and point-to-point service are unrealistic. Relaxing or removing some of these assumptions is critical to enhancing facility location modeling and optimization in an attempt to generate solutions that better incorporate real-world conditions.